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WO2017035104A1 - Correction statique sismique de modèle de vitesse - Google Patents

Correction statique sismique de modèle de vitesse Download PDF

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Publication number
WO2017035104A1
WO2017035104A1 PCT/US2016/048100 US2016048100W WO2017035104A1 WO 2017035104 A1 WO2017035104 A1 WO 2017035104A1 US 2016048100 W US2016048100 W US 2016048100W WO 2017035104 A1 WO2017035104 A1 WO 2017035104A1
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WO
WIPO (PCT)
Prior art keywords
seismic
data
velocity model
statics
seismic event
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2016/048100
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English (en)
Inventor
Mattia SPEZIALI
Massimo Clementi
Marco Mantovani
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Schlumberger Canada Ltd
Services Petroliers Schlumberger SA
Geoquest Systems BV
Schlumberger Technology Corp
Original Assignee
Schlumberger Canada Ltd
Services Petroliers Schlumberger SA
Geoquest Systems BV
Schlumberger Technology Corp
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Publication date
Application filed by Schlumberger Canada Ltd, Services Petroliers Schlumberger SA, Geoquest Systems BV, Schlumberger Technology Corp filed Critical Schlumberger Canada Ltd
Publication of WO2017035104A1 publication Critical patent/WO2017035104A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/303—Analysis for determining velocity profiles or travel times
    • G01V1/305—Travel times
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/282—Application of seismic models, synthetic seismograms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/303—Analysis for determining velocity profiles or travel times
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/10—Aspects of acoustic signal generation or detection
    • G01V2210/12—Signal generation
    • G01V2210/121—Active source
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/40—Transforming data representation
    • G01V2210/41—Arrival times, e.g. of P or S wave or first break
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/50—Corrections or adjustments related to wave propagation
    • G01V2210/53—Statics correction, e.g. weathering layer or transformation to a datum
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/61—Analysis by combining or comparing a seismic data set with other data
    • G01V2210/614—Synthetically generated data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/62—Physical property of subsurface
    • G01V2210/622—Velocity, density or impedance
    • G01V2210/6222—Velocity; travel time

Definitions

  • Seismic interpretation is a process that may examine seismic data (e.g., location and time or depth) in an effort to identify subsurface structures such as horizons and faults. Structures may be, for example, faulted stratigraphic formations indicative of hydrocarbon traps or flow channels.
  • enhancements to seismic interpretation can allow for construction of a more accurate model, which, in turn, may improve seismic volume analysis for purposes of resource extraction.
  • Various techniques described herein pertain to processing of seismic data, for example, for analysis of such data to characterize one or more regions in a geologic environment and, for example, to perform one or more operations (e.g., field operations, etc.).
  • a method can include receiving seismic data of a survey of a geologic environment; extracting a seismic event of the geologic environment based at least in part on the seismic data; estimating a velocity model of the geologic environment based at least in part on the extracted seismic event; outputting predicted seismic event data based at least in part on the velocity model; determining velocity model- based statics using the velocity model; determining data-based statics based at least in part on the predicted seismic event data; and determining at least one static value based at least in part on at least one of the velocity model-based statics and at least one of the data-based statics.
  • a system can include a processor; memory operatively coupled to the processor; and processor-executable instructions stored in the memory to instruct the system where the instructions include instructions to receive seismic data of a survey of a geologic environment; extract a seismic event of the geologic environment based at least in part on the seismic data; estimate a velocity model of the geologic environment based at least in part on the extracted seismic event; output predicted seismic event data based at least in part on the velocity model; determine velocity model-based statics using the velocity model; determine data-based statics based at least in part on the predicted seismic event data; and determine at least one static value based at least in part on at least one of the velocity model-based statics and at least one of the data-based statics.
  • One or more computer-readable storage media can include computer-executable instructions to instruct a computer where the instructions include instructions to receive seismic data of a survey of a geologic environment; extract a seismic event of the geologic environment based at least in part on the seismic data; estimate a velocity model of the geologic environment based at least in part on the extracted seismic event; output predicted seismic event data based at least in part on the velocity model; determine velocity model-based statics using the velocity model; determine data-based statics based at least in part on the predicted seismic event data; and determine at least one static value based at least in part on at least one of the velocity model-based statics and at least one of the data-based statics.
  • the instructions can include instructions to adjust at least a portion of the seismic data based at least in part on the at least one static value to output adjusted seismic data.
  • FIG. 1 illustrates an example system that includes various components for modeling a geologic environment and various equipment associated with the geologic environment
  • Fig. 2 illustrates an example of a sedimentary basin, an example of a method, an example of a formation, an example of a borehole, an example of a borehole tool, an example of a convention and an example of a system
  • FIG. 3 illustrates an example of a technique that may acquire data
  • Fig. 4 illustrates examples of signals, an example of a technique, examples of data, etc.
  • Fig. 5 illustrates examples of survey angles
  • Fig. 6 illustrates examples of trends with respect to survey angles
  • Fig. 7 illustrates an example of a survey and an example of a moveout technique
  • FIG. 8 illustrates an example of a survey and associated processing
  • Fig. 9 illustrates an example of a common azimuth survey and an example of a variable azimuth survey
  • Fig. 10 illustrates an example of forward modeling and an example of inversion
  • Fig. 1 1 illustrates examples of surveys with respect to a datum
  • Fig. 12 illustrates an example of a survey with respect to travel time
  • FIG. 13 illustrates examples of processing techniques
  • FIG. 14 illustrates an example of a processing technique
  • FIG. 15 illustrates an example of a processing technique
  • FIG. 16 illustrates an example of a method
  • Fig. 17 illustrates an example of a method that can include modeling and inversion
  • Fig. 18 illustrates an example of a geologic environment with respect to a datum
  • Fig. 19 illustrates examples of first break picks
  • Fig. 20 illustrates examples of stacks
  • Fig. 21 illustrates example components of a system and a networked system.
  • Fig. 1 shows an example of a system 100 that includes various management components 1 10 to manage various aspects of a geologic environment 150 (e.g., an environment that includes a sedimentary basin, a reservoir 151 , one or more faults 153-1 , one or more geobodies 153-2, etc.).
  • a geologic environment 150 e.g., an environment that includes a sedimentary basin, a reservoir 151 , one or more faults 153-1 , one or more geobodies 153-2, etc.
  • the geologic environment 150 e.g., an environment that includes a sedimentary basin, a reservoir 151 , one or more faults 153-1 , one or more geobodies 153-2, etc.
  • management components 1 10 may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment 150. In turn, further information about the geologic environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the
  • the management components 1 10 include a seismic data component 1 12, an additional information component 1 14 (e.g. , well/logging data), a processing component 1 16, a simulation component 120, an attribute component 130, an analysis/visualization component 142 and a workflow component 144.
  • seismic data and other information provided per the components 1 12 and 1 14 may be input to the simulation component 120.
  • the simulation component 120 may rely on entities 122.
  • Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc.
  • the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation.
  • the entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 1 12 and other information 1 14).
  • An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g. , acquired data), calculations, etc.
  • the simulation component 120 may operate in conjunction with a software framework such as an object-based framework.
  • entities may include entities based on pre-defined classes to facilitate modeling and simulation.
  • a software framework such as an object-based framework.
  • objects may include entities based on pre-defined classes to facilitate modeling and simulation.
  • An object-based framework is the MICROSOFT® . NETTM framework (Redmond, Washington), which provides a set of extensible object classes.
  • .NETTM framework an object class encapsulates a module of reusable code and associated data structures.
  • Object classes can be used to instantiate object instances for use in by a program, script, etc.
  • borehole classes may define objects for representing boreholes based on well data.
  • the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. Such processing may occur prior to input to the simulation component 120 (e.g. , consider the processing component 1 16). As an example, the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130. In an example embodiment, the simulation component 120 may construct one or more models of the geologic environment 150, which may be relied on to simulate behavior of the geologic environment 150 (e.g. , responsive to one or more acts, whether natural or artificial). In the example of Fig. 1 , the
  • analysis/visualization component 142 may allow for interaction with a model or model-based results (e.g. , simulation results, etc.).
  • output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.
  • the simulation component 120 may include one or more features of a simulator such as the ECLIPSETM reservoir simulator
  • a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc.).
  • a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as SAGD, etc.).
  • the management components 1 10 may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (Schlumberger Limited, Houston, Texas).
  • the PETREL® framework provides components that allow for optimization of exploration and development operations.
  • the PETREL® framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity.
  • various professionals e.g., geophysicists, geologists, and reservoir engineers
  • Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).
  • various aspects of the management components 1 10 may include add-ons or plug-ins that operate according to specifications of a framework environment.
  • a framework environment For example, a commercially available framework environment marketed as the OCEAN® framework environment
  • various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g. , according to application programming interface (API) specifications, etc.).
  • a framework environment e.g. , according to application programming interface (API) specifications, etc.
  • Fig. 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and a modules layer 175.
  • the framework 170 may include the commercially available OCEAN® framework where the model simulation layer 180 is the commercially available PETREL® model-centric software package that hosts OCEAN® framework applications.
  • the PETREL® software may be considered a data-driven application.
  • the PETREL® software can include a framework for model building and visualization.
  • a framework may include features for implementing one or more mesh generation techniques.
  • a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc.
  • Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.
  • the model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188.
  • Rendering 186 may provide a graphical environment in which applications can display their data while the user interfaces 188 may provide a common look and feel for application user interface components.
  • the domain objects 182 can include entity objects, property objects and optionally other objects.
  • Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc.
  • property objects may be used to provide property values as well as data versions and display parameters.
  • an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).
  • data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks.
  • the model simulation layer 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project can be accessed and restored using the model simulation layer 180, which can recreate instances of the relevant domain objects.
  • the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and one or more other features such as the fault 153-1 , the geobody 153-2, etc.
  • the geologic environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc.
  • equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155.
  • Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc.
  • Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc.
  • one or more satellites may be provided for purposes of communications, data acquisition, etc.
  • Fig. 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).
  • Fig. 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159.
  • equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159.
  • a well in a shale formation may include natural fractures, artificial fractures (e.g. , hydraulic fractures) or a combination of natural and artificial fractures.
  • a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an
  • the equipment 157 and/or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.
  • a workflow may be a process that includes a number of worksteps.
  • a workstep may operate on data, for example, to create new data, to update existing data, etc.
  • a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms.
  • a system may include a workflow editor for creation, editing, executing, etc. of a workflow.
  • the workflow editor may provide for selection of one or more pre-defined worksteps, one or more customized worksteps, etc.
  • a workflow may be a workflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc.
  • a workflow may be a process implementable in the OCEAN® framework.
  • a workflow may include one or more worksteps that access a module such as a plug-in (e.g. , external executable code, etc.).
  • a plug-in e.g. , external executable code, etc.
  • Fig. 2 shows an example of a sedimentary basin 210 (e.g. , a geologic environment), an example of a method 220 for model building (e.g., for a simulator, etc.), an example of a formation 230, an example of a borehole 235 in a formation, an example of a convention 240 and an example of a system 250.
  • a sedimentary basin 210 e.g. , a geologic environment
  • a method 220 for model building e.g., for a simulator, etc.
  • a formation 230 e.g., for a simulator, etc.
  • the sedimentary basin 210 which is a geologic environment, includes horizons, faults, one or more geobodies and fades formed over some period of geologic time. These features are distributed in two or three dimensions in space, for example, with respect to a Cartesian coordinate system (e.g. , x, y and z) or other coordinate system (e.g. , cylindrical, spherical, etc.).
  • the model building method 220 includes a data acquisition block 224 and a model geometry block 228. Some data may be involved in building an initial model and, thereafter, the model may optionally be updated in response to model output, changes in time, physical phenomena, additional data, etc.
  • data for modeling may include one or more of the following: depth or thickness maps and fault geometries and timing from seismic, remote-sensing, electromagnetic, gravity, outcrop and well log data.
  • data may include depth and thickness maps stemming from fades variations (e.g., due to seismic unconformities) assumed to following geological events ("iso" times) and data may include lateral fades variations (e.g., due to lateral variation in sedimentation characteristics).
  • data may be provided, for example, data such as geochemical data (e.g. , temperature, kerogen type, organic richness, etc.), timing data (e.g. , from paleontology, radiometric dating, magnetic reversals, rock and fluid properties, etc.) and boundary condition data (e.g., heat-flow history, surface temperature, paleowater depth, etc.).
  • geochemical data e.g. , temperature, kerogen type, organic richness, etc.
  • timing data e.g. , from paleontology, radiometric dating, magnetic reversals, rock and fluid properties, etc.
  • boundary condition data e.g., heat-flow history, surface temperature, paleowater depth, etc.
  • PETROMOD® framework (Schlumberger Limited, Houston, Texas) includes features for input of various types of information (e.g. , seismic, well, geological, etc.) to model evolution of a sedimentary basin.
  • the PETROMOD® framework provides for petroleum systems modeling via input of various data such as seismic data, well data and other geological data, for example, to model evolution of a sedimentary basin.
  • the PETROMOD® framework may predict if, and how, a reservoir has been charged with hydrocarbons, including, for example, the source and timing of hydrocarbon generation, migration routes, quantities, pore pressure and
  • workflows may be constructed to provide basin-to-prospect scale exploration solutions.
  • Data exchange between frameworks can facilitate construction of models, analysis of data (e.g.,
  • PETROMOD® framework data analyzed using PETREL® framework capabilities
  • the formation 230 includes a horizontal surface and various subsurface layers.
  • a borehole may be vertical.
  • a borehole may be deviated.
  • the borehole 235 may be considered a vertical borehole, for example, where the z-axis extends downwardly normal to the horizontal surface of the formation 230.
  • a tool 237 may be positioned in a borehole, for example, to acquire information.
  • a borehole tool may be configured to acquire electrical borehole images.
  • the fullbore Formation Microlmager (FMI) tool (Schlumberger Limited, Houston, Texas) can acquire borehole image data.
  • a data acquisition sequence for such a tool can include running the tool into a borehole with acquisition pads closed, opening and pressing the pads against a wall of the borehole, delivering electrical current into the material defining the borehole while translating the tool in the borehole, and sensing current remotely, which is altered by interactions with the material.
  • a borehole may be vertical, deviate and/or horizontal.
  • a tool may be positioned to acquire information in a horizontal portion of a borehole. Analysis of such information may reveal vugs, dissolution planes (e.g., dissolution along bedding planes), stress-related features, dip events, etc.
  • a tool may acquire information that may help to characterize a fractured reservoir, optionally where fractures may be natural and/or artificial (e.g. , hydraulic fractures). Such information may assist with completions, stimulation treatment, etc.
  • information acquired by a tool may be analyzed using a framework such as the TECHLOG® framework (Schlumberger Limited, Houston, Texas).
  • the three dimensional orientation of a plane can be defined by its dip and strike.
  • Dip is the angle of slope of a plane from a horizontal plane (e.g., an imaginary plane) measured in a vertical plane in a specific direction. Dip may be defined by magnitude (e.g. , also known as angle or amount) and azimuth (e.g. , also known as direction).
  • various angles ⁇ indicate angle of slope downwards, for example, from an imaginary horizontal plane (e.g., flat upper surface); whereas, dip refers to the direction towards which a dipping plane slopes (e.g. , which may be given with respect to degrees, compass directions, etc.).
  • strike is the orientation of the line created by the intersection of a dipping plane and a horizontal plane (e.g. , consider the flat upper surface as being an imaginary horizontal plane).
  • Some additional terms related to dip and strike may apply to an analysis, for example, depending on circumstances, orientation of collected data, etc.
  • One term is “true dip” (see, e.g. , ⁇ in the convention 240 of Fig. 2).
  • True dip is the dip of a plane measured directly perpendicular to strike (see, e.g. , line directed northwardly and labeled “strike” and angle po) and also the maximum possible value of dip magnitude.
  • Appent dip see, e.g. , DipA in the convention 240 of Fig. 2).
  • Apparent dip may be the dip of a plane as measured in any other direction except in the direction of true dip (see, e.g., ⁇ as DipA for angle );
  • apparent dip e.g., in a method, analysis, algorithm, etc.
  • a value for "apparent dip" may be equivalent to the true dip of that particular dipping plane.
  • true dip is observed in wells drilled vertically. In wells drilled in any other orientation (or deviation), the dips observed are apparent dips (e.g., which are referred to by some as relative dips). In order to determine true dip values for planes observed in such boreholes, as an example, a vector computation (e.g. , based on the borehole deviation) may be applied to one or more apparent dip values.
  • relative dip e.g. , DIPR
  • a value of true dip measured from borehole images in rocks deposited in very calm environments may be subtracted (e.g. , using vector-subtraction) from dips in a sand body.
  • the resulting dips are called relative dips and may find use in interpreting sand body orientation.
  • a convention such as the convention 240 may be used with respect to an analysis, an interpretation, an attribute, etc. (see, e.g., various blocks of the system 100 of Fig. 1 ).
  • various types of features may be described, in part, by dip (e.g., sedimentary bedding, faults and fractures, cuestas, igneous dikes and sills, metamorphic foliation, etc.).
  • dip may change spatially as a layer approaches a geobody. For example, consider a salt body that may rise due to various forces (e.g. , buoyancy, etc.). In such an example, dip may trend upward as a salt body moves upward.
  • Seismic interpretation may aim to identify and/or classify one or more subsurface boundaries based at least in part on one or more dip parameters (e.g. , angle or magnitude, azimuth, etc.).
  • dip parameters e.g. , angle or magnitude, azimuth, etc.
  • various types of features e.g., sedimentary bedding, faults and fractures, cuestas, igneous dikes and sills, metamorphic foliation, etc.
  • various types of features e.g., sedimentary bedding, faults and fractures, cuestas, igneous dikes and sills, metamorphic foliation, etc.
  • equations may be provided for petroleum expulsion and migration, which may be modeled and simulated, for example, with respect to a period of time.
  • Petroleum migration from a source material e.g. , primary migration or expulsion
  • Determinations as to secondary migration of petroleum may include using hydrodynamic potential of fluid and accounting for driving forces that promote fluid flow. Such forces can include buoyancy gradient, pore pressure gradient, and capillary pressure gradient.
  • the system 250 includes one or more information storage devices 252, one or more computers 254, one or more networks 260 and instructions 270.
  • each computer may include one or more processors (e.g. , or processing cores) 256 and memory 258 for storing instructions (e.g. , modules), for example, executable by at least one of the one or more processors.
  • a computer may include one or more network interfaces (e.g. , wired or wireless), one or more graphics cards, a display interface (e.g., wired or wireless), etc.
  • imagery such as surface imagery (e.g. , satellite, geological, geophysical, etc.) may be stored, processed, communicated, etc.
  • data may include SAR data, GPS data, etc. and may be stored, for example, in one or more of the storage devices 252.
  • the instructions 270 may include instructions (e.g. , stored in memory) executable by one or more processors to instruct the system 250 to perform various actions.
  • the system 250 may be configured such that the instructions 270 provide for establishing the framework 170 of Fig. 1 or a portion thereof.
  • one or more methods, techniques, etc. may be performed using instructions, for example, the instructions 270.
  • seismic data may be acquired and analyzed to understand better subsurface structure of a geologic environment.
  • Reflection seismology finds use in geophysics, for example, to estimate properties of subsurface formations.
  • reflection seismology may provide seismic data representing waves of elastic energy (e.g. , as transmitted by P-waves and S- waves, in a frequency range of approximately 1 Hz to approximately 100 Hz or optionally less that 1 Hz and/or optionally more than 100 Hz). Seismic data may be processed and interpreted, for example, to understand better composition, fluid content, extent and geometry of subsurface rocks.
  • FIG. 3 shows an example of an acquisition technique 340 to acquire seismic data (see, e.g. , data 360).
  • a system may process data acquired by the technique 340, for example, to allow for direct or indirect
  • an operation may pertain to a reservoir that exists in a geologic
  • a technique may provide information (e.g., as an output) that may specifies one or more location coordinates of a feature in a geologic environment, one or more characteristics of a feature in a geologic environment, etc.
  • the technique 340 may be implemented with respect to a geologic environment 341.
  • an energy source e.g., a transmitter
  • the geologic environment 341 may include a bore 343 where one or more sensors (e.g., receivers) 344 may be positioned in the bore 343.
  • energy emitted by the energy source 342 may interact with a layer (e.g. , a structure, an interface, etc.) 345 in the geologic environment 341 such that a portion of the energy is reflected, which may then be sensed by one or more of the sensors 344.
  • Such energy may be reflected as an upgoing primary wave (e.g., or "primary” or “singly” reflected wave).
  • a portion of emitted energy may be reflected by more than one structure in the geologic environment and referred to as a multiple reflected wave (e.g. , or “multiple").
  • the geologic environment 341 is shown as including a layer 347 that resides below a surface layer 349. Given such an environment and arrangement of the source 342 and the one or more sensors 344, energy may be sensed as being associated with particular types of waves.
  • a "multiple” may refer to multiply reflected seismic energy or, for example, an event in seismic data that has incurred more than one reflection in its travel path.
  • a multiple may be
  • seismic data may include evidence of an interbed multiple from bed interfaces, evidence of a multiple from a water interface (e.g., an interface of a base of water and rock or sediment beneath it) or evidence of a multiple from an air-water interface, etc.
  • the acquired data 360 can include data associated with downgoing direct arrival waves, reflected upgoing primary waves, downgoing multiple reflected waves and reflected upgoing multiple reflected waves.
  • the acquired data 360 is also shown along a time axis and a depth axis.
  • time information may allow for understanding spatial relations of layers, interfaces, structures, etc. in a geologic environment.
  • Fig. 3 also shows a diagram 380 that illustrates various types of waves as including P, SV an SH waves.
  • a P-wave may be an elastic body wave or sound wave in which particles oscillate in the direction the wave propagates.
  • P-waves incident on an interface e.g., at other than normal incidence, etc.
  • S-waves e.g., "converted" waves.
  • an S-wave or shear wave may be an elastic body wave, for example, in which particles oscillate perpendicular to the direction in which the wave propagates.
  • S-waves may be generated by a seismic energy sources (e.g. , other than an air gun).
  • S-waves may be converted to P-waves. S-waves tend to travel more slowly than P-waves and do not travel through fluids that do not support shear.
  • recording of S-waves involves use of one or more receivers operatively coupled to earth (e.g. , capable of receiving shear forces with respect to time).
  • interpretation of S-waves may allow for
  • rock properties such as fracture density and orientation, Poisson's ratio and rock type, for example, by crossplotting P-wave and S-wave velocities, and/or by other techniques.
  • the Thomsen parameter ⁇ describes depth mismatch between logs (e.g., actual depth) and seismic depth.
  • the Thomsen parameter ⁇ it describes a difference between vertical and horizontal compressional waves (e.g., P or P-wave or quasi compressional wave qP or qP-wave).
  • the Thomsen parameter ⁇ it describes a difference between horizontally polarized and vertically polarized shear waves (e.g.
  • the Thomsen parameters ⁇ and ⁇ may be estimated from wave data while estimation of the Thomsen parameter ⁇ may involve access to additional information.
  • an inversion technique may be applied to generate a model that may include one or more parameters such as one or more of the Thomsen parameters.
  • one or more types of data may be received and used in solving an inverse problem that outputs a model (e.g., a reflectivity model, an impedance model, etc.).
  • a diagram 390 shows acquisition equipment 392 emitting energy from a source (e.g., a transmitter) and receiving reflected energy via one or more sensors (e.g. , receivers) strung along an inline direction.
  • a source e.g., a transmitter
  • sensors e.g. , receivers
  • the region includes layers 393 and, for example, the geobody 395
  • energy emitted by a transmitter of the acquisition equipment 392 can reflect off the layers 393 and the geobody 395.
  • Evidence of such reflections may be found in the acquired traces.
  • energy received may be discretized by an analog-to- digital converter that operates at a sampling rate.
  • the acquisition equipment 392 may convert energy signals sensed by sensor Q to digital samples at a rate of one sample per approximately 4 ms.
  • a sample rate may be converted to an approximate distance.
  • the speed of sound in rock may be on the order of around 5 km per second.
  • a sample time spacing of approximately 4 ms would correspond to a sample "depth" spacing of about 10 meters (e.g. , assuming a path length from source to boundary and boundary to sensor).
  • a trace may be about 4 seconds in duration; thus, for a sampling rate of one sample at about 4 ms intervals, such a trace would include about 1000 samples where latter acquired samples correspond to deeper reflection boundaries.
  • the deepest boundary depth may be estimated to be about 10 km (e.g., assuming a speed of sound of about 5 km per second).
  • Fig. 4 shows an example of a technique 440, examples of signals 462 associated with the technique 440, examples of interbed multiple reflections 450 and examples of signals 464 and data 466 associated with the interbed multiple reflections 450.
  • the technique 440 may include emitting energy with respect to time where the energy may be represented in a frequency domain, for example, as a band of frequencies.
  • the emitted energy may be a wavelet and, for example, referred to as a source wavelet which has a
  • corresponding frequency spectrum e.g. , per a Fourier transform of the wavelet.
  • a geologic environment may include layers 441 -1 , 441- 2 and 441 -3 where an interface 445-1 exists between the layers 441 -1 and 441 -2 and where an interface 445-2 exists between the layers 441 -2 and 441 -3.
  • an interface 445-1 exists between the layers 441 -1 and 441 -2 and where an interface 445-2 exists between the layers 441 -2 and 441 -3.
  • a wavelet may be first transmitted downward in the layer 441 -1 ; be, in part, reflected upward by the interface 445-1 and transmitted upward in the layer 441 -1 ; be, in part, transmitted through the interface 445-1 and transmitted downward in the layer 441 -2; be, in part, reflected upward by the interface 445-2 (see, e.g., "i") and transmitted upward in the layer 441 -2; and be, in part, transmitted through the interface 445-1 (see, e.g., "ii") and again transmitted in the layer 441 -1 .
  • signals see, e.g.
  • the signals 462) may be received as a result of wavelet reflection from the interface 445-1 and as a result of wavelet reflection from the interface 445-2. These signals may be shifted in time and in polarity such that addition of these signals results in a waveform that may be analyzed to derive some information as to one or more characteristics of the layer 441 -2 (e.g. , and/or one or more of the interfaces 445-1 and 445-2). For example, a Fourier transform of signals may provide information in a frequency domain that can be used to estimate a temporal thickness (e.g., Azt) of the layer 441 -2 (e.g., as related to acoustic impedance, reflectivity, etc.).
  • a temporal thickness e.g., Azt
  • the data 466 illustrate further transmissions of emitted energy, including transmissions associated with the interbed multiple reflections 450.
  • the data 466 further account for additional interface related events, denoted iii, that stem from the event ii.
  • iii additional interface related events
  • energy is reflected downward by the interface 445-1 where a portion of that energy is transmitted through the interface 445-2 as an interbed downgoing multiple and where another portion of that energy is reflected upward by the interface 445-2 as an interbed upgoing multiple.
  • receivers 444 e.g., disposed in a well 443 as signals.
  • interbed multiple signals may be received by one or more receivers over a period of time in a manner that acts to "sum" their amplitudes with amplitudes of other signals (see, e.g. , illustration of signals 462 where interbed multiple signals are represented by a question mark "?").
  • the additional interbed signals may interfere with an analysis that aims to determine one or more characteristics of the layer 441-2 (e.g., and/or one or more of the interfaces 445-1 and 445-2).
  • interbed multiple signals may interfere with identification of a layer, an interface, interfaces, etc. (e.g. , consider an analysis that determines temporal thickness of a layer, etc.).
  • FIG. 5 shows examples 502 and 504 of survey angles ⁇ and ⁇ 2 in a geologic environment that includes layers 541 -1 , 541 -2 and 541 -3 where an interface 545-1 exists between the layers 541 -1 and 541 -2, where an interface 545-2 exists between the layers 541 -2 and 541 -3 and where a relatively vertical feature 547 extends through the layers 541 -1 , 541 -2 and 541 -3.
  • the angle ⁇ 1 is less than the angle ⁇ 2.
  • path length of a wave traveling in a subsurface region from an emitter to a detector increases, which can lead to attenuation of higher frequencies and increased interactions with features such as the feature 547.
  • arrangements of emitters and detectors can, for a particular subsurface region, have an effect on acquired seismic survey data that covers that subsurface region.
  • Fig. 6 shows examples of trends 610 that may exist as angle increases.
  • the trends 610 include a path length trend where path length increases with respect to angle, a frequency trend where higher frequencies are attenuated with respect to angle and where "resolution" with respect to layer thickness decreases with respect to angle (e.g. , smaller angles may provide high resolution that can distinguish thinner layers).
  • Fig. 7 shows an example of a survey technique 710 and an example of processing seismic data 730, which may be referred to as normal moveout (NMO).
  • NMO aims to account for the effect of the separation between receiver and source on the arrival time of a reflection that does not dip.
  • a reflection may arrive first at the receiver nearest the source.
  • the offset between the source and other receivers induces a delay in the arrival time of a reflection from a horizontal surface at depth.
  • a plot of arrival times versus offset has a hyperbolic shape.
  • traces from different source- receiver pairs that share a common midpoint can be adjusted during seismic processing to remove effects of different source-receiver offsets, or NMO. After NMO adjustments, the traces can be stacked to improve the signal-to-noise ratio.
  • CMP common midpoint
  • R6 receiver 6
  • Fig. 8 shows an example of various AVO processes 800 where angles exist between a common midpoint (CMP) and sources and receivers.
  • CMP common midpoint
  • amplitude can increase with offset.
  • averaging the four traces with Offsets 1 , 2, 3 and 4 would produce a trace that does not resemble a zero-offset trace; in other words, stacking would not preserve amplitudes.
  • the offset versus angle relationship may be determined by, for example, ray tracing.
  • Fig. 9 shows an example of a survey with common azimuth 910 and an example of a survey with variable azimuth 930.
  • variations for the common azimuth with respect to near and far source-receiver distance correspond to the trend illustrated in Fig. 7 (e.g. , where NMO may be applied) while variations for the variable azimuth correspond to a different type of trend.
  • a technique may be referred to as an AVOz technique, which is an abbreviation for amplitude variation with offset and azimuth (e.g. , the azimuthal variation of the AVO response).
  • Fig. 10 shows an example of forward modeling 1010 and an example of inversion 1030 (e.g., an inversion or inverting).
  • forward modeling progresses from an earth model of acoustic impedance and an input wavelet to a synthetic seismic trace while inversion progresses from a recorded seismic trace to an estimated wavelet and an Earth model of acoustic impedance.
  • forward modeling can take a model of formation properties (e.g. , acoustic impedance as may be available from well logs) and combine such information with a seismic wavelength (e.g.
  • a pulse) to output one or more synthetic seismic traces while inversion can commence with a recorded seismic trace, account for effect(s) of an estimated wavelet (e.g., a pulse) to generate values of acoustic impedance for a series of points in time (e.g. , depth).
  • an estimated wavelet e.g., a pulse
  • a method may employ amplitude inversion.
  • an amplitude inversion method may receive arrival times and amplitude of reflected seismic waves at a plurality of reflection points to solve for relative impedances of a formation bounded by the imaged reflectors.
  • Such an approach may be a form of seismic inversion for reservoir characterization, which may assist in generation of models of rock properties.
  • an inversion process can commence with forward modeling, for example, to provide a model of layers with estimated formation depths, thicknesses, densities and velocities, which may, for example, be based at least in part on information such as well log information.
  • a model may account for compressional wave velocities and density, which may be used to invert for P-wave, or acoustic, impedance.
  • a model can account for shear velocities and, for example, solve for S-wave, or elastic, impedance.
  • a model may be combined with a seismic wavelet (e.g., a pulse) to generate a synthetic seismic trace.
  • Inversion can aim to generate a "best-fit" model by, for example, iterating between forward modeling and inversion while seeking to minimize differences between a synthetic trace or traces and actual seismic data.
  • a method can include seismic amplitude variation with offset and azimuth (AVOAz) or amplitude variation with azimuth (AVAz) inversion.
  • AVOAz seismic amplitude variation with offset and azimuth
  • AVAz amplitude variation with azimuth
  • a framework such as the ISIS inversion framework (Schlumberger Limited, Houston Texas) may be implemented to perform an inversion.
  • a framework such as the Linerarized Orthotropic Inversion framework (Schlumberger Limited, Houston, Texas) may be implemented to perform an inversion.
  • a method can include determining a static or statics and, for example, applying a static or statics (e.g. , as a bulk shift of a seismic trace in time during seismic processing, etc.).
  • a static or statics e.g. , as a bulk shift of a seismic trace in time during seismic processing, etc.
  • an adjustment may be made to account for one or more subterranean features composed of material that may be of a relatively low seismic velocity.
  • an adjustment or adjustments may be made for one or more instances of weathered material (e.g. , a weathered layer, etc.).
  • Weathering of material may involve one or more physical, chemical and biological processes that act to decompose rock at and below the surface of earth (e.g., through low pressures and temperatures and the presence of air and water).
  • weathering can include processes such as dissolution, chemical weathering, disintegration and hydration.
  • a weather correction can be a type of static correction that aims to compensate for delays in seismic reflection or refraction times from one point to another, such as among geophone groups in a survey. Such delays may be induced by relatively low-velocity layers such as a weathered layer near a top surface of earth.
  • an adjustment or adjustments may be made for one or more elevations.
  • seismic data may be subjected to static correction to reduce one or more effects of topography with respect to locations of one or more sources and/or one or more receivers.
  • a static or statics may aim to compensate for one or more factors, which may include, for example, factors such as material factors and elevation factors.
  • a static or statics may aim to compensate for one or more "near-surface” factors, optionally including surface factors (e.g. , surface locations of equipment).
  • a near-surface factor may be a lower boundary of a near-surface region that includes a low-velocity zone in which rocks are physically, chemically or biologically broken down, in some cases coincident with a water table.
  • static corrections to seismic data may aim to compensate for the relatively low velocity of a weathered layer in comparison with relatively higher-velocity strata that reside below such a layer.
  • a method may include common mid-point (CMP) stacking as a process that uses the fact that a particular location in the subsurface will have been sampled numerous times and at different offsets.
  • CMP common mid-point
  • a group of traces may be constructed with a range of offsets that sample a particular subsurface location (e.g., a common mid-point gather (CMP gather)).
  • CMP gather common mid-point gather
  • an average amplitude may be calculated along a time sample, resulting in lower random noise; however, with a loss as to information about relationship between seismic amplitude and offset.
  • static adjustment or statics adjustment may be applied prior to CMP stacking.
  • static or statics correction or adjustment may be referred to as static correction or static adjustment.
  • static adjustment e.g., static correction
  • marine seismic data are acquired using surface vessels that may tow streamers, phenomena such as ghosts may appear, which may be processed via deghosting.
  • elevation adjustment as with land seismic data may be forgone; noting that deghosting may account for variations in, for example, streamer depth below an air-water interface.
  • static correction may be made as a form of a vertical time shift to a flat datum. As an example, further adjustment or adjustments may be made where velocity of a near-surface region is not known with sufficient certainty. As an example, static correction can include residual static correction.
  • a method can include determining source-receiver statics for seismic traces. For example, such a method may be applied to determine source-receiver statics for common depth point (CDP) seismic traces that may be gathered and stacked (e.g. , summed, to improve the signal-to-noise ratio of reflection traces).
  • CDP common depth point
  • primary reflection energy components in various traces of the gather may be subject to some amount of alignment in time before stacking.
  • seismic traces can be time shifted, for example, prior to stacking to account for offsets in the source-receiver pair spacing used to generate the traces.
  • NMO normal moveout
  • a method can include determining statics that can include elevation statics, which may be determined and applied to traces, for example, to simulate sources and receivers used to generate the traces being on a flat surface.
  • source-receiver statics may be estimated and used to further time shift traces in an effort to achieve alignment in time for a CDP stacking operation.
  • Fig. 1 1 shows an example graphic of a survey 1 1 10 and a graphic of a survey 1 130 that illustrate differences in source-to-datum travel times S1 . . . S4 and receiver-to-datum travel time R1 . . . R4 for a common depth point gather of traces, which may be adjusted (e.g., "corrected"), for example, for one or more of elevation statics, normal moveout (NMO), and source-receiver statics prior to stacking.
  • corrected e.g., "corrected”
  • a geologic environment includes a near- surface region that may be defined at least in part by a datum, which may be a depth or layer or surface at which data above are handled differently than data below.
  • a method can include processing seismic data that aims to "place" the source and the receiver on a datum plane by adjusting (e.g. , "correcting") travel times for propagation through the near-surface region (e.g., a shallower subsurface region).
  • a method can include first adjusting for elevation statics and normal moveout and then estimating and adjusting for source-receiver statics.
  • the survey 1 1 10 illustrates a scenario where elevation may be approximately the same for source-receiver pairs; whereas, for the survey 1 130, as illustrated, it corresponds to a scenario where elevations may be taken into account for source-receiver pairs.
  • a method may include determining source-receiver statics Si +Rj associated with a particular source and receiver pair used to generate a trace and then include applying the "statics" as a time shift to the trace.
  • Such an approach can provide some amount of measure of a time offset introduced into a seismic trace by source and receiver statics.
  • t 11/v.
  • Such an approach can provide some amount of measure of a time offset introduced into a seismic trace by source and receiver statics.
  • such an approach involves determination of a velocity value for a particular source or receiver location.
  • different subsurface formation characteristics can have different velocity characteristics and the datum may be located in a region where several formation layers are present between it and the surface, each may having its own velocity characteristic.
  • this can introduce some amount of uncertainty in source-receiver statics estimations (e.g.,
  • Fig. 12 shows an example of a reflection signal path model 1200 between a source and receiver.
  • a total reflection signal travel time Tij exists between a particular source and a particular receiver pair.
  • Si is the source static (acoustic wave travel time from a source Si to a datum)
  • Rj is the receiver static (acoustic wave travel time from a receiver R.j to a datum)
  • Gi+j is the two-way travel time from the datum to the reflector
  • Vi+j may be a residual moveout component.
  • Vi+j can represents a residual moveout component that remains after normal moveout correction (NMO) has been applied to the Tij reflection trace.
  • NMO normal moveout correction
  • Fig. 13 shows example plots 1310 and 1330 as to various traces in a geologic environment as acquired by sources and receivers. Referring to the plots 730 of Fig. 7, in comparison, the plots 1310 and 1330 illustrate residuals that may be due to one or more factors (e.g. , elevations, weathered layer, near-surface layers, etc.).
  • factors e.g. , elevations, weathered layer, near-surface layers, etc.
  • the plot 1310 illustrates a gather of traces before normal moveout correction (NMO) and the plot 1330 illustrates the traces after applying NMO.
  • the plot 1330 also illustrates residual time differences between the NMO corrected trace signals which may be estimated and applied (e.g. , prior to stacking, etc.).
  • Si, Rj may represent a source and receiver static to the datum or may represent residual source and receiver statics after a known source- receiver statics estimating and correcting technique has been applied.
  • total travel times Tij and Ti+1 , j+1 taken in propagation of a reflection component of an acoustic wave from a source to a receiver may be measured values (e.g., obtained from seismic traces).
  • Fig. 14 shows an example of a refraction signal path model 1400.
  • a travel time for the refraction signal may be calculated per an equation as shown in Fig. 14 where Tij is the travel time of the refraction wave arrival from the ith source to the jth receiver, Si and Rj are the respective statics
  • the source-to-receiver time of the refraction wave component Tij and Ti+1 , j+1 of the gather traces can be measured values.
  • Fig. 15 shows an example of a plot 1500 that includes a parameter Li+j as a scaler factor which may be implemented to normalize a refraction signal path to the reflection signal path, for example, to correct for slant paths (angular
  • coupled non-linear equations may be formulated and solved via one or more iteration techniques, such as a modified Gauss Seidel method for solving simultaneous equations to yield statics estimates for ASi, ARj, which may then be integrated and applied as time shifts to individual traces of a gather, e.g., a common depth point gather, for time alignment and subsequent stacking (see, e.g., US Patent No. 4,577,298 as to employing both reflection and refraction measurements for statics estimates, which is incorporated by reference herein; see also US Patent No. 4,613,960 as to constructing an optimal pilot trace from a gather of seismic traces to obtain statics estimates for time correction of the gathered traces prior to common depth point stacking, which is incorporated by reference herein).
  • a modified Gauss Seidel method for solving simultaneous equations to yield statics estimates for ASi, ARj, which may then be integrated and applied as time shifts to individual traces of a gather, e.g., a common depth point gather,
  • a method can include use of an objective function.
  • an objective function may be subject to minimization while generating values that may represent variations in a geologic environment that may be associated with sources, receivers, a datum, lithologies, velocities, etc.
  • i i which is written in a frequency domain where S(w) and S,(w) are Fourier transforms of S(t) and Si(t), respectively, and where integration is over a frequency band of interest in a positive half of a frequency axis.
  • the foregoing example objective function is quadratic and may be minimized to output the optimal pilot trace S(w) as approximately scaled by a, and time shifted relative to a gathered trace by t where a scale factor a, and a time shift t, exist for individual traces of the gather.
  • Such an objective function may inherently generate statics estimates, for example, as the pilot trace S(w) is being optimized (e.g., as time shifts t, between an estimated pilot trace S(w) and individual gather traces Si(w) are determined).
  • a method can include an iterative process where information generated may be utilized to further enhance calculation of one or more statics.
  • a method can include a model-based branch of calculations and a data- based branch of calculations. In such an example, feedback may occur in an iterative manner to update values of a model-based branch and/or to update values in a data-based branch.
  • Fig. 16 shows an example of a method 1600 that can include one or more information blocks 1601 , 1602 and 1603 where one or more of the blocks 1601 , 1602 and 1603 may form loops such that various types of information may be utilized, for example, in an iterative manner.
  • the block 1602 may be part of an inner loop while the block 1603 may be part of an outer loop, which may, for example, provide information for the block 1602.
  • a method that can determine surface consistent static(s) and/or other aspects of a seismic data interpretation workflow may include one or more loops, which can include one or more inner loops and/or one or more outer loops.
  • a method may be iterative where a loop or loops iterate based on an iteration limit, a convergence criterion, etc.
  • the method 1600 includes a reception block 1610 for receiving seismic data (e.g., seismic traces, etc.), an extraction block 1620 for extracting one or more seismic events based at least in part on at least a portion of the seismic data, an output block 1630 for outputting one or more observed seismic events, an estimation block 1640 for estimating at least one velocity model, an output block 1650 for outputting at least one velocity model, an output block 1660 for outputting at least one predicted seismic event as predicted seismic event data, a determination block 1670 for determining velocity model-based statics, a determination block 1680 for determining data-based statics, and a determination block 1690 for determining at least one surface consistent static, for example, based at least in part on at least one velocity model-based static and at least one data-based static.
  • seismic data e.g., seismic traces, etc.
  • an extraction block 1620 for extracting one or more seismic events based at least in part on at least a portion of the seismic data
  • an output block 1630 for outputting one or
  • At least a portion of the seismic data received per the reception block 1610 may be, per the block 1601 , used for determining at least a portion of the data-based statics via the determination block 1680.
  • at least one predicted seismic event per the output block 1660 may be, per the block 1602, used for extracting one or more seismic events per the extraction block 1620.
  • one or more determined surface consistent static of the determination block 1690 may be, per the block 1603, utilized to process seismic data of the reception block 1610, which may, for example, then be available for the determination block 1680 per the block 1601 .
  • a method can include feedback, which may be in the form of one or more loops.
  • a method can be iterative such as an iterative feedback method.
  • a method can include iterative feedback of predicted seismic events and/or computed statics, applied within a workflow that estimates a velocity model and then computes surface consistent statics, for example, as a sum of model-based statics and data driven statics.
  • the method 1600 of Fig. 16 includes the determination block 1670 for model-based statics and the determination block 1680 for data-based statics where output from the blocks 1670 and 1680 can be utilized in the determination block 1690 for determining surface consistent statics.
  • output from the output block 1660 can be fed back to the extraction block 1620, for example, to improve extraction of seismic events from the seismic data.
  • Such feedback can improve determinations of the blocks 1670 and 1680, which can improve determinations of the block 1690.
  • a method can include back-propagating output of velocity model estimation (e.g. refraction tomography predicted first arrivals) for uses as a guide function for the extraction of seismic events (e.g., first break (FB) picking), which can improve the velocity model estimation and therefore produce more accurate predicted picks.
  • velocity model estimation e.g. refraction tomography predicted first arrivals
  • FB first break
  • a final velocity model and predicted seismic arrivals may be used to compute respectively model-based and data driven statics.
  • computed statics can be back-propagated per the block 1603 and, for example, applied to input data (e.g., received seismic data of the reception block 1610), for example, to ease the extraction of seismic events and iterate again.
  • input data e.g., received seismic data of the reception block 1610
  • the method 1600 can provide for robust and accurate computation of statics. Such an approach can be implemented when starting from low-quality and/or sparsely picked first breaks or equivalent seismic events.
  • a guide function approach may be utilized to facilitate extraction of seismic events (e.g. , consider a time window approach for a trace, etc.).
  • a method can use a guide function to back-propagate and perform one or more stages of seismic event extraction.
  • the block 1602 of the method 1600 can be part of a loop that aims to utilize predicted seismic events to extract seismic events where such a loop may be iterated multiple times, for example, according to one or more criteria (e.g. , a number of loops or iterations, an error criterion, a convergence criterion, etc.).
  • the method 1600 can continue to the determination blocks 1670 and 1680 and then the determination block 1690, which may optionally loop back to the reception block 1610 per the block 1603.
  • a method can include one or more inner loops and one or more outer loops.
  • a method can include applying a static as a time shift to seismic data (e.g. , optionally on a trace by trace basis for a plurality of statics).
  • the method 1600 may be applied to land-based seismic survey data and/or to marine seismic survey data.
  • the method 1600 may be applied to a land-based seismic survey of a geologic environment that includes sand fields (e.g. , sand dunes, etc.).
  • a method can include utilizing a technique to generate a first set of extracted seismic events from traces and then utilizing a feedback technique for generating an enhanced set of seismic events, for example, extracted by exploiting feedback via the first set of predicted seismic events at the extraction of the seismic event phase.
  • the events can be seismic first arrivals (e.g., first breaks), which can be representative of refracted waves.
  • such a method can include determining statics and, for example, generating stacks, which may be rendered to a display, compared, etc.
  • a method can include an outer loop that aims to converge as to statics.
  • the method 1600 may be associated with various computer-readable media (CRM) blocks or modules 161 1 , 1621 , 1631 , 1641 , 1651 , 1661 , 1671 , 1681 and 1691 .
  • Such blocks or modules may include instructions suitable for execution by one or more processors (or processor cores) to instruct a computing device or system to perform one or more actions.
  • a single medium may be configured with instructions to allow for, at least in part, performance of various actions of the method 1600.
  • a computer-readable medium may be a computer-readable storage medium (e.g., a non-transitory medium) that is not a carrier wave and that is not a signal.
  • the method 1600 may be computer-implemented for use in geophysical exploration.
  • the method 1600 may be computer-implemented for use in geophysical exploration.
  • the method 1600 may be computer-implemented for use in geophysical exploration.
  • the method 1600 may be computer-implemented for use in geophysical exploration.
  • the method 1600 may be computer-implemented for use in geophysical exploration.
  • the method 1600 may be computer-implemented for use in geophysical exploration.
  • the method 1600 may be computer-implemented for use in geophysical exploration.
  • the method 1600 may be computer-implemented for use in geophysical exploration.
  • the method 1600 may be computer-implemented for use in geophysical exploration.
  • a static correction e.g., at least a portion of a static correction workflow, etc.
  • a method can include, based in part on seismic data, estimating a velocity model that may be implemented to make predictions germane to such data (e.g., to predict at least a portion of the seismic data).
  • the velocity model and seismic response predicted by it may be used to compute a time shift (a static) that can be associated with one or more seismic sources and/or receivers (e.g., detectors).
  • a method can include determining a static and applying the determined static to one or more seismic traces.
  • the seismic trace or traces may be output as a processed seismic trace or traces.
  • the application of the static may aim to enhance quality, for example, quality of a seismic image.
  • a method can include processing seismic traces using a determined static (e.g., adjusted or corrected using a determined static) and outputting at least one image to a display.
  • a computing system may render a seismic image or seismic images to a display based at least in part on processing of seismic data using at least one determined static.
  • a method can include estimating a velocity model iteratively.
  • the method 1600 of Fig. 16 can include a loop where iterations may occur to refine an estimated velocity model or velocity models.
  • an estimation of a velocity model itself may be accomplished via an iterative technique.
  • a method may include iterations within iterations (e.g. , an inner loop and an outer loop, etc.).
  • a method can include from received seismic traces, extracting one or more events of interest (e.g. reflection or refraction or reflection and refraction). Such an extraction process may occur via picking, which may be automated, manual or semi-automated. In such an example, the one or more picked events may be deemed to be observed data.
  • events of interest e.g. reflection or refraction or reflection and refraction.
  • picking may be automated, manual or semi-automated.
  • the one or more picked events may be deemed to be observed data.
  • At least a portion of observed data may be used for estimating a velocity model that is able to produce a seismic response close to the observed data given as input.
  • an inverse problem may be formulated and solved. For example, consider a method that includes solving an inverse problem associated with seismic wave propagation (see, e.g. , Fig. 10, which illustrates forward modeling and inversion; see also Fig. 17, which illustrates inversion).
  • one or more types of a priori information e.g. well logs, etc.
  • a method can include outputting an estimated velocity model (or models) and one or more predicted seismic events generated by such a model or models.
  • a predicted seismic event or events may be used as feedback in a method.
  • one or more predicted seismic events may be fed back to the extraction block 1620, which aims to extracts one or more events from at least a portion of the received seismic data.
  • the predicted seismic event or events may be utilized as a priori information (e.g., used in part as a guide function to restrict a search interval for a desired event). In turn, such an approach can generate another set of observed data.
  • a method may include iterations. For example, iterations may aim to iteratively generate observed data while increasing quality of one or more velocity models. For example, a method can include iterations that proceed until a quality of an estimated velocity model and its associated predicted data are considered to be satisfactory (e.g. , according to one or more quality metrics). As an example, a method may proceed based on an output estimated velocity model as in the output block 1650 from a first pass (e.g. , without iterating through the blocks 1640 and 1650 as to a second pass). For example, where a first pass velocity model is deemed acceptable, a method may proceed to static determination and, for example, application thereof.
  • output of a velocity model may be used for estimating a static.
  • a static correction associated with seismic propagation in the velocity model is computed.
  • a complementary (e.g. , residual) static correction is extracted.
  • one or more of the seismic traces or one or more of the observed seismic events picked on traces may be used for estimating residual error with respect to predicted data.
  • a method can include decomposing one or more static terms in surface-consistent way, for example, to obtain a single value for an individual shot-point and a single value for an individual receiver-point.
  • two components may be merged producing a static value.
  • a static value may be applied as a time shift to one or more seismic traces.
  • a method can include processing information via a framework such as, for example, the OMEGATM framework (Schlumberger Limited, Houston, Texas).
  • a framework may provide for processing of geophysics data, for example, via one or more workflows, algorithms, etc.
  • a framework may provide for accessing data from one or more sources and/or formats.
  • the aforementioned OMEGATM framework may operatively couple with the PETRELTM framework and/or the OCEANTM framework.
  • Earth Model Building (EMB) tools may enable a variety of depth imaging workflows, including model building, editing and updating, depth-tomography QC, residual moveout analysis, and volumetric common-image-point (CIP) pick QC.
  • EMB Earth Model Building
  • Such functionalities, in conjunction with the OMEGATM framework algorithm e.g. , depth tomography, migration, etc.
  • a method such as the method 1600 of Fig. 16 or a portion thereof may be implemented via a framework, for example, as a plug-in, a module, etc.
  • a framework may include one or more modules for processing data (e.g. , imaging, prestack seismic interpretation, quantitative interpretation, exploration to development analysis, etc.).
  • modules for processing data e.g. , imaging, prestack seismic interpretation, quantitative interpretation, exploration to development analysis, etc.
  • a method may include seismic velocity modeling (SVM), which can include velocity model building.
  • SVM seismic velocity modeling
  • a workflow may include one or more of isotropic and anisotropic depth imaging, migration velocity model building and updating, 4D imaging and anisotropic illumination studies, and borehole to surface seismic calibration.
  • a method may include geostatistical analysis.
  • a framework may be implemented to analyze near-surface velocity variations.
  • a method may include common image point tomography implemented in conjunction with a velocity model where such a method includes resolving residual velocity errors.
  • a selected geometry of sources and receivers in a seismic survey can yields a number of seismic traces with common midpoints or central bins suitable for stacking. As such traces may be recorded at different offset distances, travel times for seismic waves traveling to and from a given reflecting horizon can vary with that distance. As an example, if overburden through which seismic waves pass is of substantially constant velocity, then the time-variation with distance may be accomplished via application of Pythagorean geometry where the shape of a reflector on a seismic "gather" of traces tends to be hyperbolic.
  • a method can include selecting a set of velocities to use at a variety of reflectors and constructing a velocity function of two-way travel time. Such velocity functions may be interpolated, spatially and in two-way travel time. Seismic gathers may then be "corrected for normal moveout". As an example, a set of moveout-corrected gathers may be summed or "stacked", for example, after eliminating ("muting") one or more portions of traces that may be distorted by moveout.
  • locations of reflected events may be somewhat uncertain because of lateral variations in velocity and/or dipping interfaces.
  • a velocity function may be derived through a trial-and- error approach guided by one or more statistical techniques, for example, of lateral coherence, knowledge of expected geologic structure, other constraints such as interval velocities and well log data, etc.
  • a workflow may include imaging through modeling velocity structure, for example, as a form of inversion of seismic data.
  • Inversion can include building a velocity model which is iteratively refined until it and corresponding seismic data reach some level of agreement (e.g., within one or more limits, etc.).
  • seismic data, vertical seismic profiles and/or well log data may be used to perform inversion that can generate a model as a result where the model can be model of layers, for example, including their thickness, density and P- and S-wave velocities.
  • a method can include surface wave analysis (SWA).
  • a method may include SWA modeling and inversion (SWAMI).
  • SWAMI SWA modeling and inversion
  • a framework may be provided that can perform SWA associated calculations (e.g., SWAMI calculations, etc.).
  • SWAMI velocity modeling framework marketed by Schlumberger Limited (Houston, Texas), which may optionally be utilized at least in part with one or more other frameworks (e.g. , PETRELTM, OCEANTM, OMEGATM, etc.).
  • the SWAMI framework includes an inversion module that allows measurements from analysis of surface waves to be converted into a near-surface velocity model. Such a velocity model may be added to geological information and geophysical measurements to provide a more accurate
  • a framework may include a near-surface modeling toolkit (NSM), for example, as a set of modules, workflow components, etc., that may provide for construction of velocity models, for example, optionally in conjunction with one or more seismic data processing frameworks (e.g. , OMEGATM framework, etc.).
  • NSM near-surface modeling toolkit
  • a framework may allow for one or more of import and export of geometry databases (e.g. , as in the OMEGATM framework), population of layered velocity models to gridded Volcan models (e.g.
  • a smooth datum close to a recording surface e.g. , suitable for depth migration, etc.
  • surface-consistent static corrections to an above datum region e.g. , a near surface region as may be appropriate for a migration model
  • visualization of source and detector attributes at source and detector positions, source and detector data as "point sets" e.g., as in the PETRELTM framework object tree.
  • performing prestack depth migration on data may account for velocity variations in the near surface.
  • a framework such as the SWAMI framework may be utilized to generate a relatively high-resolution velocity model.
  • a high resolution, near- surface model may be utilized as part of a workflow (e.g., one or more method, etc.) to calculate surface consistent statics.
  • a framework can include receiving data that can include phase velocity information, for example, information picked from high- resolution spectra and inverted to a shear velocity section for a near surface region (e.g., for individual receiver lines). In such an example, by tessellating several receiver lines, 3D coverage may be achieved.
  • a framework such as, for example, the SWAMI framework, may yield a shear velocity model for a near surface region, which may be in a range, for example, of about a first 100 m to about 150 m below the surface.
  • aspects of a model and depth of a near surface region may depend in part on low frequency content and/or near-surface characteristics.
  • a surface-wave inversion may be implemented to model near surface shear wave velocity.
  • a Rayleigh wave inversion problem may be formulated where a SWAMI framework may use a model-based approach.
  • local surface wave modal dispersion curves may be extracted using, for example, an adaptive high-resolution wavefield transform (e.g., for each local super-gather).
  • a super-gather may be generated with multiple shots and receivers, for example, within a defined aperture.
  • one or more processing options may be selected.
  • a method may include using a fundamental mode of a Rayleigh wave.
  • a method can include picking wavenumber and frequency values, for example, automatically, semi-automatically or manually.
  • one or more quality control checks may be implemented, optionally with editing (e.g., automatic, semi-automatic, user-implemented, etc.).
  • Fig. 17 shows an example of a method 1700 that can include modeling and inversion.
  • information may be acquired in a geologic environment 1710 and analyzed to characterize at least a portion of the geologic environment as including various types of material 1712.
  • the method 1700 can include acquiring data (e.g. , or receiving data) 1722, which may be rendered as time with respect to offset (e.g., distance).
  • the method 1700 can include data processing 1726, which may include generating phase velocity versus wavelength data (e.g., a relationship between phase velocity and wavelength).
  • the method 1700 can include performing an inversion 1726 (e.g. , inverting), which may include generating a relationship between velocity and depth.
  • inversion 1726 e.g. , inverting
  • velocity may vary with respect to depth, for example, where velocity may generally increase with respect to depth and where in a near surface region a relationship or relationships between velocity and depth may differ from those at greater depths.
  • velocity in a near surface region of about 150 m, velocity can vary in a non-linear manner, a segmented manner, a reverse manner, etc. with respect to depth (e.g. , depending on composition of and arrangement of materials in the near surface region).
  • a method can include receiving seismic data, estimating a velocity model able to predict at least a portion of the received seismic data and using the estimated model and the seismic response predicted by it to compute a time shift or time shifts (e.g., a static or statics) that can be associated with one or more seismic sources and/or seismic receivers.
  • a static or statics may be applied to seismic traces, for example, to enhance quality of a seismic image.
  • a method that includes estimating a velocity model can include estimating the velocity model iteratively.
  • predicted seismic events e.g., of an estimated velocity model
  • a loop may be formed whereby seismic data is received, events are picked, a velocity model is estimated and predicted events of the estimated velocity model are used for extraction of events from the seismic data, however, now in a manner that may be enhanced by the predicted events.
  • the predicted events may be exploited as a priori information (e.g., consider use as a guide function to restrict the search interval for a desired event).
  • a revised or "new" set of extracted information may be obtained (e.g. , "new" observed data as to one or more events).
  • a loop may be repeated a number of times to achieve a desired level of quality, for example, according to one or more quality metrics (e.g., error measures, convergence measures, a number of loops, etc.).
  • output of an estimated velocity model may be used for estimating a static.
  • a method can include estimating a static for a first estimated velocity model, for a second estimated velocity model, etc.
  • a static may be estimated after a number of iterations, N, where an estimated velocity model is the Nth estimated velocity model.
  • a static correction associated with the seismic propagation in the model may be computed.
  • a complementary (residual) static correction may be extracted.
  • seismic traces and/or observed seismic events picked on the seismic traces can be used for estimating the residual error with respect to the predicted data.
  • a method can include decomposing static terms in surface-consistent way, for example, to obtain a single value for each shot-point and a single value for each receiver-point.
  • the two components may be merged for producing a final static value.
  • a velocity model may be represented by an equation with respect to depth, such as, for example:
  • V Vo + kz
  • V the seismic velocity at depth z
  • Vo the seismic velocity at a location
  • k the rate of increase of velocity.
  • the parameters Vo and k can be set to satisfy a condition to be a minimum, such as, for example:
  • the derivative of ⁇ with respect to the parameters is to approximate zero.
  • the parameters as a term Vo/k may be taken to be a single parameter; however, as isolation of k is not possible, an iterative approach may be taken (e.g. , optionally graphically via plotting).
  • a measure of quality of fit may be via a standard deviation or other metric.
  • a single k value or parameter is mentioned.
  • a plurality of k values may exist.
  • the foregoing example is provided to illustrate how velocity may vary with respect to depth and to illustrate why an iterative approach as to parameter estimation for a velocity model may be implemented.
  • iterations may be "inner loop” iterations that provide an estimated velocity model that can be utilized in an "outer loop” that may include its own iterations.
  • Fig. 18 shows an example of a system 1800 in a geologic environment 1801 that includes a surface 1802, a source 1805 at the surface 1802, a near- surface zone 1806, a receiver 1807, a bedrock zone 1808 and a datum 1810 where the near-surface zone 1806 (e.g. , near-surface region) may be defined at least in part by the datum 1810, which may be a depth or layer or surface at which data above are handled differently than data below.
  • a method can include processing seismic data that aims to "place" the source 1805 and the receiver 1807 on a datum plane defined by the datum 1810 by adjusting (e.g. , "correcting") travel times for propagation through the near-surface region (e.g., a shallower subsurface region).
  • the geologic environment 1801 can include various features such as, for example, a layer 1820 that defines an interface 1822 that can be a reflector, a water table 1830, a leached zone 1832, a glacial scour 1834, a buried river channel 1836, a region of material 1838 (e.g., ice, evaporates, volcanics, etc.), a high velocity zone 1840, and a region of material 1842 (e.g., Eolian or peat deposits, etc.).
  • a layer 1820 that defines an interface 1822 that can be a reflector
  • a water table 1830 e.g., a leached zone 1832, a glacial scour 1834, a buried river channel 1836, a region of material 1838 (e.g., ice, evaporates, volcanics, etc.), a high velocity zone 1840, and a region of material 1842 (e.g., Eolian or peat deposits, etc.).
  • a method may include adjusting seismic data in a source by source manner, a receiver by receiver manner, a source-receiver manner or other manner.
  • adjustments may be made based in part on one or more of thickness and velocity of a near-surface region.
  • a shallow subsurface can include large and abrupt vertical and horizontal variations that may be, for example, caused by differences in lithology, compaction cementation, weather, etc. Such variations can generate delays or advances in arrival times of seismic waves passing through them relative to waves that do not.
  • a seismic image may be of enhanced resolution with a reduction in false structural anomalies at depth, a reduction in mis-ties between intersecting lines, a reduction in artificial events created from noise, etc.
  • a method can include adjusting for such time
  • an amount by which a trace is adjusted can depend on one or more factors (e.g. , thickness, velocity of near-surface anomalies, etc.).
  • the datum 1810 is shown, for example, as a plane, below which strata may be of particular interest in a seismic imaging workflow.
  • a near surface region may be defined, for example, at least in part with respect to a datum.
  • a velocity model may be a multidimensional model that models at least a portion of a geologic environment.
  • the source 1805 can be a seismic energy source such as a vibrator.
  • a vibrator may be a mechanical source that delivers vibratory seismic energy to the Earth for acquisition of seismic data.
  • a vibrator may be mounted on a vehicle (e.g., a truck, etc.).
  • a seismic source or seismic energy source may be one or more types of devices that can generate seismic energy (e.g. , an air gun, an explosive charge, a vibrator, etc.).
  • Vibratory seismic data can be seismic data whose energy source is a vibrator that may use a vibrating plate to generate waves of seismic energy.
  • the frequency and the duration of emitted energy can be controllable, for example, frequency and/or duration may be varied according to one or more factors (e.g., terrain, type of seismic data desired, etc.).
  • a vibrator may emit a linear sweep of a duration that is of the order of seconds (e.g., at least seven seconds, etc.), for example, beginning with high frequencies and decreasing with time (downsweeping) or going from low to high frequency (upsweeping).
  • frequency may be changed (e.g. , varied) in a nonlinear manner (e.g., certain frequencies are emitted longer than others, etc.).
  • resulting source wavelet can be one that is not impulsive.
  • parameters of a vibrator sweep can include start frequency, stop frequency, sweep rate and sweep length.
  • each reflection event can span about 12 seconds in the raw, uncorrelated data (e.g., raw measurement data or RMD) where various reflection events can overlay each other.
  • data may be reduced to an interpretable form by a cross-correlation of the known input sweep with the raw data recorded at the receiver stations (e.g., receivers).
  • a correlation process finds a replication of the input sweep, it can produce a compact symmetrical correlation wavelet centered on the long reflection event.
  • data tend to exhibit a relatively high signal-to-noise ratio, and reflection events tend to be robust wavelets spanning, for example, a few tens of milliseconds.
  • a vibrator may be employed in land acquisition surveys for areas where explosive sources may be contraindicated (e.g., via regulations, etc.).
  • more than one vibrator can be used simultaneously (e.g. , in an effort to improve data quality, etc.).
  • a receiver may be a may be a UniQTM sensor unit (Schlumberger Limited, Houston, Texas).
  • a sensor unit can include a geophone, which may be configured to detect motion in a single direction.
  • a geophone may be configured to detect motion in a vertical direction.
  • three mutually orthogonal geophones may be used in combination to collect so-called 3C seismic data.
  • a sensor unit that can acquire 3C seismic data may allow for determination of type of wave and its direction of propagation.
  • a sensor assembly or sensor unit may include circuitry that can output samples at intervals of 1 ms, 2 ms, 4 ms, etc.
  • an assembly or sensor unit can include an analog to digital converter (ADC) such as, for example, a 24-bit sigma-delta ADC (e.g. , as part of a geophone or operatively coupled to one or more geophones).
  • ADC analog to digital converter
  • a sensor assembly or sensor unit can include synchronization circuitry such as, for example, GPS synchronization circuitry with an accuracy of about plus or minus 12.5 microseconds.
  • an assembly or sensor unit can include circuitry for sensing of real-time and optionally continuous tilt, temperature, humidity, leakage, etc.
  • an assembly or sensor unit can include calibration circuitry, which may be self- calibration circuitry.
  • near-surface characterization and effective static adjustment can pose challenges in land seismic data processing. For example, if near-surface distortions are not adequately addressed in early stages, they may propagate through a processing workflow and impact imaging of deep structures (e.g., impact image quality, etc.).
  • a method can include exploiting redundancy and angular range of refracted waves such that a compressional velocity model of a weathering layer can be estimated and perturbations introduced by such shallow geologic heterogeneities in the seismic data be accounted for via
  • a method may employ first break (FB) picking.
  • Quality of FB picking can impact estimation of thickness and velocity of the first refractor in a geologic environment being surveyed. Quality of such estimation can affect starting model quality in an inversion approach.
  • a method can include integrated near-surface characterization through Simultaneous Joint Inversion (SJI) of refracted and surface waves.
  • SJI Simultaneous Joint Inversion
  • a workflow can provide modeling results and enhance a static solution.
  • FB picking may be automated or semi-automated (e.g., optionally with sparse guidance provided by a user). Where a survey region is heterogeneous, generation of FB picks that are representative of the "true" first arrival can be problematic using an automated approach. In addition to uncorrelated noise, picking errors may include cycle skips, biased picking at short offsets, and incapability to follow the topographic variations.
  • FB picks are used for computing statics adjustments
  • a higher level of accuracy can be desirable.
  • a residual refraction static workflow can decompose the residual error between observed and predicted FB picks in a surface-consistent fashion, producing a short-wavelength correction for improving the stack continuity and coherency.
  • low-quality observed picks can bias a static result.
  • SJI near-surface modeling can be utilized for enhancing FB picks before statics computation. For example, after a preliminary SJI pass, predicted FB picks on the SJI P-velocity models can be produced. These arrival times can be a guide function for repicking the first arrivals, producing a new set of observed data.
  • a FB picks enhancing workflow can include utilizing the output of a first SJI stage as guide function for improving reliability of a FB dataset where new FB picks can be used as input for a subsequent or final SJI modeling and statics.
  • Fig. 19 shows an example of a receiver gather 1900 from a dune portion of a survey. An original set of picks is illustrated via broken lines (e.g., dashed lines) and an enhanced set of picks is illustrated via a solid line. In the example of Fig. 19, the seismic data are contaminated by random noise and fail to follow a clear elevation trend.
  • the enhanced picks utilize the SJI predicted picks as a guide function produces (see solid line representing new set of pick times). The new dataset tends to be unbiased and the amount of noise is reduced, showing overlay on the traces.
  • the data are from a single-sensor high- density 3D land dataset from the Sahara desert.
  • the survey region is characterized by severe dunes in a southern portion and by noisy recordings. These two factors reduce effectiveness of an automated picker in the region with dunes.
  • a first pass of FB picking struggled to honor the topography effect introduced by the sand dunes.
  • high- frequency incoherent noise leaked in the picks.
  • enhancement is demonstrated via generation of a set of unbiased predicted picks that tend to be consistent with topography trends.
  • a synthetic FB dataset allowed for the enhancement of the observed data that were repicked on the seismic traces using the predicted picks as guide function.
  • Such an enhanced set of refraction data may be used, for example, as input for inversion modeling and for subsequent statics computation.
  • Fig. 20 shows two crossline stacks 2010 and 2030 passing through sand dunes on the southern side of a region of the Sahara desert.
  • the stack 2010 has statics derived from tau-p refraction tomography while the stack 2030 has simultaneous joint inversion (SJI) assisted statics.
  • SJI simultaneous joint inversion
  • model-based SJI statics can improve flatness of structures, where residual refraction SJI statics enhance the continuity of the events and increase the signal-to-noise ratio both in the shallow and in the deep sections.
  • Figs. 19 and 20 show comparisons where Fig. 19 shows a comparison between a first set of extracted seismic events from traces (dashed lines) and an enhanced set of seismic events (solid line) where the enhanced set is extracted in part by exploiting feedback of the first set of predicted seismic events (dashed lines) at the extraction of the seismic event phase.
  • the events are the seismic first arrivals (first breaks or FBs), which are representative of refracted waves.
  • a comparison is shown on a stack section after having applied the aforementioned tau-p algorithm for statics (stack 2010) and a feedback approach (stack 2030).
  • a method may include determining one or more statics and applying at least one of the one or more statics to seismic data and/or data derived therefrom. Such a method may be referred to as a feedback approach (e.g. , a method that includes at least one loop).
  • a method can include rendering information to a display where the information includes information associated with a geologic environment that is at least in part below a defined datum, which may correspond to a physical depth, layer, surface, etc. in the geologic environment.
  • a method such as the method 1600 of Fig. 16 may be implemented for a geologic environment that may include sand features (e.g. , sand dunes, etc.).
  • a method such as the method 1600 of Fig. 16 may be implemented where seismic data quality may be less than a desired level.
  • a method can include estimating a static correction to be applied to seismic traces to enhance a stack image.
  • a predicted seismic response may be exploited to estimate a value of a static correction.
  • Such an approach may include iterations where predicted responses are utilized successively to enhance extraction of information from seismic data, which, in turn, can be utilized to enhance velocity model estimation.
  • a method can include receiving seismic data of a survey of a geologic environment; extracting a seismic event of the geologic environment based at least in part on the seismic data; estimating a velocity model of the geologic environment based at least in part on the extracted seismic event; outputting predicted seismic event data based at least in part on the velocity model; determining velocity model-based statics using the velocity model; determining data- based statics based at least in part on the predicted seismic event data; and determining at least one static value based at least in part on at least one of the velocity model-based statics and at least one of the data-based statics.
  • Such a method may include adjusting at least a portion of the seismic data based at least in part on the at least one static value.
  • a survey may be a common midpoint survey.
  • a survey can be a land-based survey such as, for example, a land-based survey in a region that includes sands (e.g. , sand dunes, etc.).
  • adjusting can include adjusting a seismic trace.
  • adjusting can include time-shifting a seismic trace.
  • velocity model-based statics can include statics associated with seismic propagation.
  • residual error between a predicted seismic event data and received seismic data may be used to determine one or more complementary, residual statics.
  • a method can include estimating residual error with respect to predicted seismic event data, for example, using at least one seismic trace of the seismic data.
  • estimating can include using at least one extracted seismic event picked on at least one seismic trace of the seismic data.
  • a method can include repeating extracting of a seismic event of a geologic environment based at least in part on received seismic data and based at least in part on at least a portion of predicted seismic event data to provide an updated extracted seismic event.
  • the method may include estimating an updated velocity model of the geologic environment based at least in part on the updated extracted seismic event.
  • such a method may include outputting updated predicted seismic event data based at least in part on the updated velocity model.
  • a method may include repeating extracting, estimating and outputting to update an extracted seismic event, to update a velocity model and to update predicted seismic event data.
  • a system can include a processor; memory operatively coupled to the processor; and processor-executable instructions stored in the memory to instruct the system where the instructions include instructions to receive seismic data of a survey of a geologic environment; extract a seismic event of the geologic environment based at least in part on the seismic data; estimate a velocity model of the geologic environment based at least in part on the extracted seismic event; output predicted seismic event data based at least in part on the velocity model; determine velocity model-based statics using the velocity model; determine data-based statics based at least in part on the predicted seismic event data; and determine at least one static value based at least in part on at least one of the velocity model-based statics and at least one of the data-based statics.
  • the system can include instructions to adjust at least a portion of the seismic data based at least in part on the at least one static value to output adjusted seismic data.
  • such a system can include instructions to extract one or more seismic events from the adjusted seismic data (e.g., as in an iteration to determine one or more surface consistent statics based at least in part on the adjusted seismic data).
  • a system can include instructions to extract a seismic event of a geologic environment based at least in part on seismic data and based at least in part on predicted seismic event data as based at least in part on a velocity model.
  • such an approach may be a feedback approach where predicted seismic events can be utilized to extract seismic events from seismic data and/or adjusted seismic data.
  • one or more computer-readable storage media can include computer-executable instructions to instruct a computer where the
  • instructions include instructions to receive seismic data of a survey of a geologic environment; extract a seismic event of the geologic environment based at least in part on the seismic data; estimate a velocity model of the geologic environment based at least in part on the extracted seismic event; output predicted seismic event data based at least in part on the velocity model; determine velocity model-based statics using the velocity model; determine data-based statics based at least in part on the predicted seismic event data; and determine at least one static value based at least in part on at least one of the velocity model-based statics and at least one of the data-based statics.
  • the instructions can include instructions to adjust at least a portion of the seismic data based at least in part on the at least one static value to output adjusted seismic data.
  • one or more computer-readable media can include instructions to extract a seismic event of a geologic environment based at least in part on seismic data and based at least in part on predicted seismic event data as based at least in part on velocity model.
  • a workflow may be associated with various computer- readable media (CRM) blocks.
  • Such blocks generally include instructions suitable for execution by one or more processors (or cores) to instruct a computing device or system to perform one or more actions.
  • a single medium may be configured with instructions to allow for, at least in part, performance of various actions of a workflow.
  • a computer-readable medium may be a computer-readable storage medium.
  • blocks may be provided as one or more modules, for example, such as the instructions 270 of the system 250 of Fig. 2.
  • Fig. 21 shows components of an example of a computing system 2100 and an example of a networked system 21 10.
  • the system 2100 includes one or more processors 2102, memory and/or storage components 2104, one or more input and/or output devices 2106 and a bus 2108.
  • instructions may be stored in one or more computer-readable media (e.g. , memory/storage components 2104). Such instructions may be read by one or more processors (e.g. , the processor(s) 2102) via a communication bus (e.g., the bus 2108), which may be wired or wireless.
  • the one or more processors may execute such instructions to implement (wholly or in part) one or more attributes (e.g., as part of a method).
  • a user may view output from and interact with a process via an I/O device (e.g. , the device 2106).
  • a computer-readable medium may be a storage component such as a physical memory storage device, for example, a chip, a chip on a package, a memory card, etc. (e.g., a computer- readable storage medium).
  • components may be distributed, such as in the network system 21 10.
  • the network system 21 10 includes components 2122-1 , 2122-2, 2122-3, . . . 2122-N.
  • the components 2122-1 may include the processor(s) 2102 while the component(s) 2122-3 may include memory accessible by the processor(s) 2102.
  • the component(s) 2122-2 may include an I/O device for display and optionally interaction with a method.
  • the network may be or include the Internet, an intranet, a cellular network, a satellite network, etc.
  • a device may be a mobile device that includes one or more network interfaces for communication of information.
  • a mobile device may include a wireless network interface (e.g. , operable via IEEE 802.1 1 , ETSI GSM, BLUETOOTH®, satellite, etc.).
  • a mobile device may include components such as a main processor, memory, a display, display graphics circuitry (e.g. , optionally including touch and gesture circuitry), a SIM slot, audio/video circuitry, motion processing circuitry (e.g., accelerometer, gyroscope), wireless LAN circuitry, smart card circuitry, transmitter circuitry, GPS circuitry, and a battery.
  • a mobile device may be configured as a cell phone, a tablet, etc.
  • a method may be implemented (e.g. , wholly or in part) using a mobile device.
  • a system may include one or more mobile devices.
  • a system may be a distributed environment, for example, a so-called “cloud" environment where various devices, components, etc. interact for purposes of data storage, communications, computing, etc.
  • a device or a system may include one or more components for
  • a communication occurs via one or more Internet protocols), a cellular network, a satellite network, etc.
  • a method may be implemented in a distributed environment (e.g., wholly or in part as a cloud-based service).
  • information may be input from a display (e.g. , consider a touchscreen), output to a display or both.
  • information may be output to a projector, a laser device, a printer, etc. such that the information may be viewed.
  • information may be output stereographically or
  • a printer may include one or more substances that can be output to construct a 3D object.
  • data may be provided to a 3D printer to construct a 3D representation of a subterranean formation.
  • layers may be constructed in 3D (e.g. , horizons, etc.), geobodies constructed in 3D, etc.
  • holes, fractures, etc. may be constructed in 3D (e.g. , as positive structures, as negative structures, etc.).

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Acoustics & Sound (AREA)
  • Environmental & Geological Engineering (AREA)
  • Geology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Geophysics (AREA)
  • Geophysics And Detection Of Objects (AREA)

Abstract

L'invention concerne un procédé qui consiste à recevoir des données sismiques d'une étude relative à un environnement géologique ; extraire un événement sismique de l'environnement géologique au moins en partie sur la base des données sismiques ; estimer un modèle de vitesse de l'environnement géologique au moins en partie sur la base de l'événement sismique extrait ; produire des données d'événement sismique prédit, au moins en partie sur la base du modèle de vitesse ; déterminer une correction statique basée sur le modèle de vitesse à l'aide du modèle de vitesse ; déterminer une correction statique basée sur des données, au moins en partie sur la base des données d'événement sismique prédit ; et déterminer au moins une valeur statique, au moins en partie sur la base d'au moins une des corrections statiques basées sur un modèle de vitesse et d'au moins une des corrections statiques basées sur des données.
PCT/US2016/048100 2015-08-24 2016-08-23 Correction statique sismique de modèle de vitesse Ceased WO2017035104A1 (fr)

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US62/209,158 2015-08-24

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WO2021016064A1 (fr) * 2019-07-19 2021-01-28 Saudi Arabian Oil Company Génération de modèles de vitesse sismique améliorés à l'aide d'une modélisation géomécanique
CN112327353A (zh) * 2019-07-30 2021-02-05 中国石油天然气集团有限公司 一种横波折射层建模方法及装置
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CN112394412A (zh) * 2020-10-30 2021-02-23 中国石油天然气集团有限公司 一种基于浅层叠加建模的横波静校正方法及装置
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CN113031069A (zh) * 2021-03-02 2021-06-25 吉林大学 一种岩溶地区的多元信息约束智能层析静校正方法
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WO2022114985A1 (fr) * 2020-11-27 2022-06-02 Aramco Overseas Company B.V. Systèmes et procédés de correction de données sismiques pour les effets indésirables causés par les variations de vitesse à proximité de la surface en corrigeant la statique à grande longueur d'onde
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