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WO2025097017A1 - Stratigraphie de séquence à haute résolution entraînée par apprentissage automatique - Google Patents

Stratigraphie de séquence à haute résolution entraînée par apprentissage automatique Download PDF

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Publication number
WO2025097017A1
WO2025097017A1 PCT/US2024/054210 US2024054210W WO2025097017A1 WO 2025097017 A1 WO2025097017 A1 WO 2025097017A1 US 2024054210 W US2024054210 W US 2024054210W WO 2025097017 A1 WO2025097017 A1 WO 2025097017A1
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WIPO (PCT)
Prior art keywords
data
stratigraphic
geological
resource site
site
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.)
Pending
Application number
PCT/US2024/054210
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English (en)
Inventor
Hussein MUSTAPHA
Manish Kumar SINGH
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 WO2025097017A1 publication Critical patent/WO2025097017A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V20/00—Geomodelling in general
    • 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
    • G01V1/301—Analysis for determining seismic cross-sections or geostructures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/18—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for well-logging
    • G01V3/26—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for well-logging operating with magnetic or electric fields produced or modified either by the surrounding earth formation or by the detecting device
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V5/00—Prospecting or detecting by the use of ionising radiation, e.g. of natural or induced radioactivity
    • G01V5/04—Prospecting or detecting by the use of ionising radiation, e.g. of natural or induced radioactivity specially adapted for well-logging
    • G01V5/08—Prospecting or detecting by the use of ionising radiation, e.g. of natural or induced radioactivity specially adapted for well-logging using primary nuclear radiation sources or X-rays
    • G01V5/12—Prospecting or detecting by the use of ionising radiation, e.g. of natural or induced radioactivity specially adapted for well-logging using primary nuclear radiation sources or X-rays using gamma or X-ray sources
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B2200/00—Special features related to earth drilling for obtaining oil, gas or water
    • E21B2200/22—Fuzzy logic, artificial intelligence, neural networks or the like
    • 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
    • G01V2210/61—Analysis by combining or comparing a seismic data set with other data
    • G01V2210/616—Data from specific type of measurement
    • G01V2210/6169—Data from specific type of measurement using well-logging
    • 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
    • G01V2210/64—Geostructures, e.g. in 3D data cubes
    • G01V2210/643—Horizon tracking

Definitions

  • This disclosure is directed to a machine learning technique for sequence stratigraphic analysis of subsurface structures associated with a resource site.
  • a method for determining stratigraphic marker data for energy development operations at a resource site comprises: generating a stratigraphic model for a resource site, wherein the stratigraphic model includes a machine learning model having one or more parameters; receiving one or more training data associated with the resource site, the training data including one or more of: first sensor data captured by one or more sensors deployed within a wellbore of the resource site, or synthetic data generated from, or associated with a site that is distinct from or similar to the resource site; configuring the one or more parameters of the stratigraphic model to initialize the stratigraphic model, the configuring including: isolating cyclic stratigraphic features associated with one or more high order sequences comprised in the first sensor data or synthetic data, and determining inflection point data indicating geological transitions or sudden geological changes comprised in the isolated cyclic stratigraphic features and thereby generate an initialized strati
  • a system and a computer program can include or execute the method described above. These and other implementations may each optionally include one or more of the following features.
  • the stratigraphic model is a machine learning model that has an intelligent structure including one or more of a neural network structure or a decision tree structure.
  • the first sensor data referenced above comprises one of a gamma ray log captured from the resource site by a gamma ray sensor; and sonic or acoustic log captured from the resource site by a sonic or acoustic sensor.
  • the cyclic stratigraphic features may be isolated for a plurality of timeframes corresponding to geological layering or thickness information for a plurality of geological structures associated with or comprised in a subsurface of the resource site.
  • the classifying operation is based on a thickness of a subsurface geological structure associated with the resource site. Moreover, the classifying operation comprises computationally marking of maximum flooding surface information associated with the second sensor data to characterize the geological sequence boundary data and thereby indicate: a coarsening upward pattern of a geological structure within a subsurface of the resource site, or a fining upward pattern of the geological structure within the subsurface of the resource site.
  • the stratigraphic marker data or geological facies data includes chronostratigraphic marker data that indicates temporal geological data associated with a subterranean structure of the resource site.
  • the well log data can comprise log motif data while the sea level data can comprise eustatic curve data.
  • the stratigraphic marker data or geological facies data comprises sequence event data or one or more characteristics of a first geological structure at the resource site that reflect an origin for the first geological structure and differentiates the first geological structure from other geological structures at the resource site.
  • the first geological structure referenced above is a rock.
  • the stratigraphic marker data or geological facies data comprises depth values defining ordered sequence boundaries associated with subsurface geological structures of one or more sections of the resource site.
  • the energy development operations comprises one or more of: well placement operations; equipment placement operations; and surgically locating a subsurface resource at the resource site.
  • FIG. 1 depicts an exemplary high-level workflow for methods, systems, and computer programs that determine stratigraphic marker data for energy development operations at a resource site.
  • FIG. 2 depicts a cross-sectional view of a resource site for which the process of FIG. 4 may be executed.
  • FIG. 3 depicts a network system illustrating a communicative coupling of devices or systems associated with the resource site of FIG. 2.
  • FIG. 4 depicts an exemplary detailed workflow for methods, systems, and computer programs that determine stratigraphic marker data for energy development operations at a resource site.
  • FIG. 5 which indicates stratigraphic marker data or geological facies data for a second order stratigraphic sequence and a third order stratigraphic sequence.
  • the systems and methods disclosed may be accomplished using interconnected devices and systems that obtain a plurality of data parameters of interest associated with a resource site.
  • the workfl ows/flowcharts described in this disclosure implicate a new processing approach (e.g., hardware, special purpose processors, and specially programmed general-purpose processors) because such analyses are too complex and cannot be mentally performed by a person in the time available or at all.
  • the described systems and methods are directed to tangible computing implementations that solve specific technological problems in developing natural resources such as oil, gas, water well industries, and other mineral exploration operations. More specifically, the systems and methods disclosed may be applicable to operations associated with stratigraphic analysis associated with a resource site.
  • This disclosure provides methods and systems that facilitate identifying high order geological sequence data (e.g., greater than or equal to second order stratigraphic sequence) based on wellbore log data and/or eustatic curve data.
  • the disclosed geological sequence data may facilitate describing, interpreting, classifying, and/or categorizing geological sequences and/or geological layering information for a plurality of time periods.
  • the geological sequence data comprises data indicating a pattern of observable geological phenomena that occur in a predictable or non- predictable order or manner.
  • the geological sequence data or geological layering data may comprise geological sediment depositions due to environmental changes that affect, for example, subsurface tectonics, subsidence, sediment supply, and relative sea level within a given geological area.
  • the geological sequence data comprises geological depositional data for a plurality of timeframes or timescales.
  • a first order geological sequence may be defined by a large geological time scale (e.g., greater than or equal to 50 million years) for one or more cycles of sedimentary deposition in the subsurface.
  • the disclosed methods and systems drive the picking or selection of chronostratigraphic markers associated with a well within a given resource site.
  • the chronostratigraphic markers can indicate temporal data (e.g., geological timeframes) associated with subterranean or geological structures based on data measurements captured in a well.
  • the disclosed methods and systems enable the use of log data (e.g., log motif data) and/or eustatic data (e.g, eustatic curve data) to detect and/or categorize, and/or select sequence event data based on flooding and non-flooding geological sites (e.g., a resource site such as those discussed below). Furthermore, the disclosed methods and systems can be seamlessly used to detect, categorize, and/or select sequence event data for high order sequences (e.g., 3rd order sequences, 4th order sequences, 5th order sequences) such that the high order sequences comprise geological timeframes that have ranges of thousands of years to millions of years.
  • high order sequences e.g., 3rd order sequences, 4th order sequences, 5th order sequences
  • a fifth order sequence (e.g., a geological sequence in stratigraphy) may have a timeframe that spans between 0.01 million years to 0.1 million years; a fourth order sequence may have a geological timeframe between 0.05 million years to 0.5 million years while a third order sequence may have a geological timeframe between 0.5 million years to 3 million years, e.g.
  • the embodiments described herein allow for intelligent and automated identification of high order sequences (e.g., geological sequences or geological sequence data) which are subsequently used to guide or otherwise drive chronostratigraphic correlation (e.g., geological time correlation) operations (e.g., computing operations or otherwise) and which can further serve as inputs to facies modeling tools.
  • the disclosed workflow includes mechanisms for selecting and/or picking and/or categorizing seismic event data or stratigraphic event data comprised in geological sequence data using a supervised and/or unsupervised machine learning (ML) model.
  • ML machine learning
  • the supervised and/or unsupervised machine learning (ML) model may have attendant supervised learning computing operations that include parameterizing, guiding, and/or controlling the ML model to optimize its selecting, categorizing, or picking computing operations based on data captured from one or more representative wells at a resource site.
  • ML machine learning
  • the ML model is comprised in a signal processing engine (e.g., a plug-in engine) the is electronically integrated or otherwise electronically coupled to a geological analysis tool (e.g., Petrel) which: provides identification of 3 rd order sequences, 4 th order sequences, and 5 th order sequences; and selects or picks chronostratigraphic markers (e.g., chronostratigraphic marker data) using, for example, subsurface data (e.g., well log data) from a resource site.
  • a geological analysis tool e.g., Petrel
  • the disclosed methods and systems can employ: gamma ray (GR) log data and sonic log signature data obtained from a resource site using one or more logging tools (e.g., sensors) together with sea level curve data to determine progradation information, aggradation gradation information, and retrogradation information, all of which indicate geological patterns for different time scales or timeframes.
  • progradation occurs when sea level rises, but sediment influx is higher, with the rate of sediment deposition being more or higher than the rate of sea level rise. This can represent a coarsening upward sequence of subsurface structures, or a funnel shape log signature of captured gamma ray data associated with the subsurface structures.
  • Aggradation on the other hand can occur when the rate of sediment deposition is similar to the rate of seal level rise. This can represent a blocky log signature of captured gamma ray data associated with subsurface structures. Furthermore, retrogradation can occur when the rate of sea level rise is more than the rate of sediment deposition. This can indicate a finning upwards sequence of subsurface structures, or a bell-shaped log signature of captured gamma ray data associated with the subsurface structures. It is appreciated that progradation information, aggradation information, and retrogradation information may be comprised in generated stratigraphic marker data or geological facies data for a resource site under consideration using the disclosed methods and systems.
  • a machine learning model is disclosed that is used to identify or otherwise determine geological data indicating a coarsening upward pattern and/or a fining upward pattern comprised in captured geological data (e.g., well log data,).
  • captured geological data e.g., well log data
  • the disclosed methods and systems enables generating robust structural models (e.g., structural computing models) and/or other resource models (e.g., resource computing models) for resource site exploration operations, field development planning and execution operations.
  • FIG. 1 provides an exemplary high-level workflow 100 for methods, systems, and computer programs that determine stratigraphic marker data for energy development operations at a resource site.
  • a data managing module may generate a stratigraphic model for a resource site under consideration.
  • the stratigraphic model comprises a machine learning model that has one or more parameters.
  • the one or more parameters comprise: a lithology or rock type parameter; a sea level parameter, geological age parameter, a gamma ray log parameter, a sonic log parameter and a stratigraphic sequence parameter.
  • the one or more parameters of the stratigraphic model comprise at least one rock property that is used to determine, correlate, and/or interpret depositional environments in the subsurface and/or the age of a stratigraphic sequence.
  • the data managing module may configure the stratigraphic model using training data.
  • the training data may be used to configure or otherwise update or quantitatively or qualitatively characterize the one or more parameters of the stratigraphic model.
  • stratigraphic marker data or geological facies data may be generated for the resource site using the trained stratigraphic model. This, according to one embodiment, is based on configuring the stratigraphic model.
  • the stratigraphic marker data or geological facies data comprises geological sequence boundary data indicating uniform or non- uniform sediment deposition information or geological layering information associated with the resource site.
  • FIG. 2 shows a cross-sectional view of a resource site 200 for which the process of FIG. 1 may be executed. While the illustrated resource site 200 represents a subterranean formation, the resource site, according to some embodiments, may be below water bodies such as oceans, seas, lakes, ponds, wetlands, rivers, etc.
  • various measurement tools capable of sensing one or more parameters such as seismic two-way travel time, density, resistivity, production rate, etc., of a subterranean formation and/or geological formations may be provided at the resource site.
  • wireline tools may be used to obtain measurement information related to geological attributes (e.g, geological attributes of a wellbore and/or reservoir) including geophysical and/or chemical information.
  • the chemical information may include chemical information associated with the subsurface and/or chemical information associated with the surface/above ground areas of the resource site 200.
  • various sensors may be located at various locations around the resource site 200 to monitor and collect data for executing the process outlined in FIG. 4.
  • the techniques disclosed herein may be applied to surface seismic monitoring applications, surface gravity applications, surface electromagnetic applications, surface ground heave applications, and surface measurement of induced seismicity applications.
  • the disclosed methods and systems may be applied to remote sensing applications (e.g., satellite-based measurements), subsea applications associated with permanent sensors, temporary sensor applications, remotely operated vehicles applications, and aerial -based measurement (e.g, performed from planes, helicopters, and/or drones) applications.
  • the aerial-based measurements may include Synthetic Aperture Radar data measurements, atmospheric concentration data measurements associated with molecules such as CCh, CP , and/or gas concentration data measurements associated with gases within the seabed.
  • Part, or all, of the resource site 200 may be on land, on water, or below water.
  • the disclosed methods and systems may be used with any combination of one or more resource sites (e.g., multiple oil fields or multiple wellsites, one or more saline aquifers, one or more depleted oil/gas fields, etc.), one or more processing facilities, etc.
  • the resource site 200 of FIG. 2 may have data acquisition tools 202a, 202b, 202c, and 202d positioned at various locations within the resource site 200.
  • the subterranean structure 204 may have a plurality of geological formations 206a-206d.
  • this structure may have several formations or layers, including a shale layer 206a, a carbonate layer 206b, a shale layer 206c, and a sand layer 206d.
  • a fault 207 within the subterranean formation of the resource site 200 may extend through the shale layer 206a and the carbonate layer 206b.
  • the data acquisition tools for example, may be adapted to take measurements and detect geophysical and/or chemical characteristics of the various formations shown.
  • the oil field 200 may contain a variety of geological structures and/or formations, sometimes having extreme complexity.
  • a given geological structure for example below a water line (e.g., aquifer) relative to the given geological structure, fluid may occupy pore spaces of the formations.
  • Each of the measurement devices may be used to measure properties of the formations and/or other geological features. While each data acquisition tool is shown as being in specific locations in FIG. 2, it is appreciated that one or more types of measurement may be taken at one or more locations across one or more sources of the resource site 200 or other locations for comparison and/or analysis.
  • the data collected from various sources at the resource site 200 may be processed and/or evaluated and/or used as training data, and or used to generate high resolution result sets for characterizing a resource at the resource site, and/or used for generating resource models, etc.
  • the data collected by a set of sensors at the resource site may include data associated with the number of wells of a first reservoir or second reservoir at the resource site, data associated with the number of grid cells of the first or second reservoir, data associated with the average permeability of the first or second reservoir, data associated with the production duration history (e.g, number of years of production) of the first reservoir or second, etc.
  • data acquisition tool 202a is illustrated as a measurement truck, which may comprise devices or sensors that take measurements of the subsurface through sound vibrations such as, but not limited to, seismic measurements.
  • Drilling tool 202b may include a downhole sensor adapted to perform logging while drilling (LWD) data collection.
  • the wireline tool 202c may include a downhole sensor deployed in a wellbore or borehole.
  • Production tool 202d may be deployed from a production unit or Christmas tree into a completed wellbore.
  • parameters that may be measured include weight on bit, torque on bit, subterranean pressures (e.g., underground fluid pressure), temperatures, flow rates, compositions, rotary speed, particle count, voltages, currents, and/or other parameters of operations as further discussed below.
  • subterranean pressures e.g., underground fluid pressure
  • temperatures e.g., temperature, flow rates, compositions, rotary speed, particle count, voltages, currents, and/or other parameters of operations as further discussed below.
  • sensors may be positioned about the resource site to collect data relating to various resource site operations, such as sensors deployed by the data acquisition tools 202.
  • the sensors may include any type of sensor such as a metrology sensor (e.g., temperature, humidity), an automation enabling sensor, an operational sensor (e.g., pressure sensor, H2S sensor, thermometer, depth, tension), evaluation sensors that can be used for acquiring data regarding a subsurface formation, wellbore, formation fluid/gas, wellbore fluid, gas/oil/water comprised in the formation/wellbore fluid, or any other suitable sensor.
  • the sensors may include accelerometers, flow rate sensors, pressure transducers, electromagnetic sensors, acoustic sensors, temperature sensors, chemical agent detection sensors, nuclear sensors, and/or any additional suitable sensors.
  • the data captured by the one or sensors may be used to characterize, or otherwise generate one or more parameter values for a high resolution result set used to, for example, label or configure a machine learning (ML) engine or a resource model as the case may require.
  • ML machine learning
  • test data or synthetic data may also be used in developing the ML engine or the resource model via one or more parameterization/labeling operations such as those discussed in association with the workflows presented herein.
  • Evaluation sensors may be featured in downhole tools such as tools 202b-202d and may include for instance electromagnetic sensors, acoustic sensors, nuclear sensors, and optic sensors.
  • tools including evaluation sensors that can be used in the framework of the current method include electromagnetic tools such as imaging sensors.
  • the imaging sensors comprise one or more of: FMITM or QuantaGeoTM (mark of SLB, Houston, TX) sensors; induction sensors including Rt ScannerTM (mark of SLB, Houston, TX) sensors; multifrequency dielectric dispersion sensors including Dielectric ScannerTM (mark of SLB, Houston, TX) sensors; acoustic tools including sonic sensors such as Sonic ScannerTM (mark of SLB, Houston, TX) sensors; ultrasonic sensors including pulse-echo sensors orUBITM sensors or PowerEchoTM (marks of SLB, Houston, TX) sensors or flexural sensors or PowerFlexTM (mark of SLB, Houston, TX) sensors; nuclear sensors such as Litho ScannerTM (mark of SLB, Houston, TX) sensors or nuclear magnetic resonance sensors; fluid sampling tools including fluid analysis sensors such as InSitu Fluid AnalyzerTM (mark of SLB, Houston, TX) sensors; and distributed sensors including fiber optic sensors.
  • FMITM or QuantaGeoTM mark of S
  • the disclosed evaluation sensors are used for: evaluating or determining formation data associated with a well at the resource site (z.e., determining petrophysical or geological properties of the formation); verifying or determining integrity data for the well (e.g. , integrity data such as casing data or cement properties data); and/or analyzing determining fluid data associated with produced fluid (e.g., hydrocarbons) at the resource site.
  • the fluid data can comprise flowrate data, fluid type data (e.g., whether the fluid is liquid or gaseous or a combination thereof).
  • data acquisition tools 202a-202d may generate data plots or measurements 208a-208d, respectively. These data plots are depicted within the resource site 200 to demonstrate or indicate data generated by some of the operations executed at the resource site 200.
  • Data plots 208a-208c are examples of static data plots that may be generated by data acquisition tools 202a-202c, respectively. However, it is herein contemplated that data plots 208a-208c may also be data plots generated and updated in real time.
  • These measurements may be analyzed to better define properties of the formation(s) and/or determine the accuracy of the measurements and/or check for and compensate for measurement errors.
  • the plots of each of the respective measurements may be aligned and/or scaled for comparison and verification purposes.
  • base data associated with the plots may be incorporated into site planning, modeling a test at the resource site 200.
  • the respective measurements that can be taken may be any of the above.
  • Other data may also be collected, such as historical data of the resource site 200 and/or sites similar to the resource site 200, user inputs, information (e.g. , economic information) associated with the resource site 200 and/or sites similar to the resource site 200, and/or other measurement data and other parameters of interest. Similar measurements may also be used to measure changes in formation aspects over time.
  • computer facilities such as those discussed in association with FIG. 3 may be positioned at various locations about the resource site 200 (e.g., a surface unit) and/or at remote locations.
  • a surface unit e.g., one or more terminals 320
  • the surface unit may be capable of sending commands to the resource site (e.g., oil/gas field) equipment/sy stems, and/or receiving data therefrom.
  • the surface unit may also collect data generated during production operations and can produce output data, which may be stored or transmitted for further processing at the resource site or to an offsite location relative to the resource site.
  • the data collected by sensors associated with the resource site may be used alone or in combination with other data for energy development operations (e.g., computing operations or otherwise). It is further appreciated that the data aggregated using the sensors may be collected in one or more databases and/or transmitted to one or more computing systems at the resource site or to the offsite location.
  • the data associated with the sensors may be historical data, real time data, or combinations thereof. The real time data may be used in real time or stored for later use. The data may also be combined with historical data or other inputs for further analysis or for modeling purposes to optimize production processes at the resource site 200. In one embodiment, the data is stored in separate databases, or combined into a single database.
  • FIG. 3 shows a high-level network system 300 illustrating a communicative coupling of devices or systems associated with the resource site 200 of FIG. 2.
  • the system 300 shown in the figure may include a set of processors 302a, 302b, and 302c for executing one or more processes discussed herein.
  • the set of processors 302 may be electrically coupled to one or more servers (e.g., computing systems) including memory 306a, 306b, and 306c that may store for example, program data, databases, and other forms of data.
  • Each server of the one or more servers may also include one or more communication devices 308a, 308b, and 308c.
  • the set of servers may provide a cloud-computing platform 310.
  • the set of servers includes different computing devices that are situated in different locations and may be scalable based on the needs and workflows associated with the oil field 200.
  • the communication devices of each server may enable the servers to communicate with each other through a local or global network such as an Internet network.
  • the servers may be arranged as a town 312, which may provide a private or local cloud service for users.
  • a town may be advantageous in remote locations with poor connectivity.
  • a town may be beneficial in scenarios with large networks where security may be of concern.
  • a town in such large network embodiments can facilitate implementation of a private network within such large networks.
  • the town may interface with other towns or a larger cloud network, which may also communicate over public communication links.
  • cloud-computing platform 310 may include a private network and/or portions of public networks.
  • a cloud-computing platform 310 may include remote storage and/or other application processing capabilities.
  • the system 300 of FIG. 3 may also include one or more user terminals 314a and 314b each including at least a processor to execute programs, a memory (e.g, 316a and 316b) for storing data, a communication device and one or more user interfaces and devices that enable a user to receive, view, and transmit information.
  • the user terminals 314a and 314b is a computing system having interfaces and devices including keyboards, touchscreens, display screens, speakers, microphones, a mouse, styluses, etc.
  • the user terminals 314 may be communicatively coupled to the one or more servers of the cloud-computing platform 310.
  • the user terminals 314 may be client terminals or expert terminals, enabling collaboration between clients and experts through the system 300 of FIG. 3.
  • the system 300 of FIG. 3 may also include computing systems associated with at least one or more resource sites 200, such that the computing systems may have, for example, a set of terminals 320, each including at least a processor, a memory, and a communication device for communicating with other devices communicatively coupled to the cloud-computing platform 310.
  • the at least one or more resource sites 200 may have associated sets of sensors (e.g., one or more sensors described in association with FIG. 2) or sensor interfaces 322a and 322b communicatively coupled to the set of terminals 320 and/or directly coupled to the cloud-computing platform 310.
  • data collected by the sets of sensors/sensor interfaces 322a and 322b may be processed to generate a one or more resource models (e.g., reservoir models) or one or more resolved data sets used to generate the resource models which may be displayed on a user interface associated with the set of terminals 320, and/or displayed on user interfaces associated with the set of servers of the cloud computing platform 310, and/or displayed on user interfaces of the user terminals 314.
  • resource models e.g., reservoir models
  • resolved data sets used to generate the resource models which may be displayed on a user interface associated with the set of terminals 320, and/or displayed on user interfaces associated with the set of servers of the cloud computing platform 310, and/or displayed on user interfaces of the user terminals 314.
  • various equipment/devices discussed in association with the resource site 200 may also be communicatively coupled to the set of terminals 320 and or communicatively coupled directly to the cloud-computing platform 310.
  • the equipment and sensors may also include one or more communication device(s) that may communicate with the set of terminals 320 to receive orders/instructions locally and/or remotely from the resource site 200 and also send statuses/updates to other terminals such as the user terminals 314.
  • one or more communication device(s) may communicate with the set of terminals 320 to receive orders/instructions locally and/or remotely from the resource site 200 and also send statuses/updates to other terminals such as the user terminals 314.
  • the system 300 of FIG. 3 may also include one or more client servers 324 including a processor, memory and communication device.
  • the client servers 324 may be communicatively coupled to the cloud-computing platform 310, and/or to the user terminals 314a and 314b, and/or to the set of terminals 320 at the resource site 200 and/or to sensors at the oil field, and/or to other equipment at the resource site 200.
  • a processor may include a microprocessor, a graphical processing unit (GPU), a microcontroller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, or another control or computing device.
  • a microprocessor a graphical processing unit (GPU)
  • GPU graphical processing unit
  • microcontroller a processor module or subsystem
  • programmable integrated circuit a programmable gate array
  • another control or computing device may include a microprocessor, a graphical processing unit (GPU), a microcontroller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, or another control or computing device.
  • the memory/storage media discussed above in association with FIG. 3 can be implemented as one or more computer-readable or machine-readable storage media that are non-transitoiy.
  • storage media may be distributed within and/or across multiple internal and/or external enclosures of a computing system and/or additional computing systems.
  • Storage media may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories; magnetic disks such as fixed, floppy and removable disks; other magnetic media including tape; optical media such as compact disks (CDs) or digital video disks (DVDs), BluRays or any other type of optical media; or other types of storage devices.
  • semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories
  • magnetic disks such as fixed, floppy and removable disks; other magnetic media including tape
  • optical media such as compact disks (CDs) or digital video disks (DVDs), BluRays or any other type of optical media; or other types of storage
  • instructions can be provided on one or more computer-readable or machine-readable storage media, or alternatively, can be provided on multiple computer- readable or machine-readable storage media distributed in a large system having possibly plural nodes and/or non-transitory storage means.
  • Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture).
  • the storage medium or media can be located either in a computer system running the machine- readable instructions, or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.
  • the described system 300 of FIG. 3 is an example that: may have more or fewer components than shown; may combine additional components; and/or may have a different configuration or arrangement of the components than those shown in FIG. 3.
  • the various components shown may be implemented in hardware, software, or a combination of both, hardware and software, including one or more signal processing engines and/or application specific integrated circuits.
  • the steps in the flowcharts described below may be implemented by running one or more functional modules in an information processing apparatus such as general-purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, GPUs or other appropriate devices associated with the system 300 of FIG. 3.
  • the flowchart of FIG. 1 as well as the flowcharts below may be executed using a signal processing engine or a data processing module (e.g., computing module) stored in memory 306a, 306b, or 306c such that the signal processing engine or the data processing module includes instructions that are executed by the one or more processors such as processors 302a, 302b, or 302c as the case may require.
  • a signal processing engine or a data processing module e.g., computing module
  • one or more computing processors may be described as executing steps associated with one or more of the flowcharts described in this disclosure
  • the one or more computing device processors may be associated with the cloud-based computing platform 310 and may be located at one location or distributed across multiple locations.
  • the one or more computing device processors may also be associated with other systems of FIG. 3 other than the cloud-computing platform 310.
  • a computing system associated with the system 300 includes at least one processor, at least one memory, and one or more programs stored in the at least one memory, such that the programs comprise instructions, which when executed by the at least one processor, are configured to perform any method disclosed herein.
  • a computer readable storage medium associated with the system 300 is provided, which has stored therein one or more programs, the one or more programs including instructions, which when executed by a processor, cause the processor to perform any method disclosed herein.
  • a computing system is provided that includes at least one processor, at least one memory, and one or more programs stored in the at least one memory for performing any method disclosed herein.
  • an information processing apparatus for use in a computing system is provided for performing any method disclosed herein.
  • FIG. 4 provides an exemplary detailed workflow 400 for methods, systems, and computer programs that determine stratigraphic marker data for energy development operations at a resource site.
  • a data managing module stored in a memory device may cause a computer processor to execute the various processing stages of the workflow 400.
  • the disclosed techniques may be implemented as a data manager or signal processing engine within a geological software tool such that the data manager or signal processing engine enables the modeling of geological structures in the subsurface of a resource site based on the processes outlined herein.
  • the data managing module referenced above generates a stratigraphic model for the resource site.
  • the stratigraphic model comprises a machine learning model having one or more parameters including gamma ray parameter, sonic log parameter, and an eustatic curve parameter. It is appreciated that gamma ray parameter, the sonic log parameter, and the eustatic parameter are configured to receive and/or analyze and/or process gamma ray data, sonic log data, and eustatic curve data that are received or associated with one or more sensors.
  • the data managing module may receive one or more training data associated with the resource site.
  • the training data may be used to parameterize or otherwise configure the one or more parameters of the machine learning model.
  • the training data includes one or more of: first sensor data captured by one or more sensors deployed within a wellbore of the resource site; or synthetic data generated from, or associated with a site that is distinct from or similar to the resource site.
  • the data managing module may configure, at block 406, the one or more parameters of the stratigraphic model to initialize the stratigraphic model.
  • the data manager may train the stratigraphic model based on the one or more configured parameters.
  • the training comprises: isolating cyclic stratigraphic features associated with one or more high order sequences comprised in the first sensor data or synthetic data; and determining inflection point data indicating geological transitions or sudden geological changes comprised in the isolated cyclic stratigraphic features and thereby generate an initialized stratigraphic model.
  • the data managing module may designate or apply boundary condition data for the configured stratigraphic model based on one or more low order stratigraphic sequence data associated with the resource site to generate a trained stratigraphic model.
  • the data managing module may receive second sensor data associated with the resource site.
  • the second sensor data comprises one or more of well log data or sea level data associated with the resource site.
  • the well log data may include gamma ray data or sonic log data while the sea level data may comprise eustatic curve data.
  • the gamma ray parameter, sonic log parameter, and an eustatic curve parameter may each include logic and/or control or operator routines that act or otherwise operate or analyze received corresponding gamma ray data, sonic log data, and sea level data, as the case may be, and thereby cause the machine learning model to generate stratigraphic marker data or geological facies data based on same as further discussed below.
  • the data managing module may determine, using the trained stratigraphic model, a stratigraphic log pattern associated with the second sensor data and then classify, based on the trained stratigraphic model and on the stratigraphic log pattern, geological sequence data comprised in the second sensor data at block 414.
  • the data managing module may generate, based on the classifying and/or stratigraphic marker data or geological facies data for the resource site, the stratigraphic marker data or geological facies data comprising geological sequence boundary data indicating uniform or non-uniform sediment deposition information or geological layering information associated with the resource site.
  • the data managing module may be used to initiate executing, based on the stratigraphic marker data or geological facies data for the resource site, one or more energy development operations.
  • the stratigraphic model is a machine learning model that has an intelligent structure including one or more of a neural network structure or a decision tree structure.
  • the first sensor data referenced above comprises one of: a gamma ray log captured from the resource site by a gamma ray sensor; and sonic or acoustic log captured from the resource site by a sonic or acoustic sensor.
  • cyclic stratigraphic features discussed in association with flowchart 400 may be isolated for a plurality of timeframes corresponding to geological layering or thickness information for a plurality of geological structures associated with or comprised in a subsurface of the resource site.
  • the classifying operation is based on a thickness of a subsurface geological structure associated with the resource site. Moreover, the classifying operation comprises computationally marking of maximum flooding surface information associated with the second sensor data to characterize the geological sequence boundary data and thereby indicate: a coarsening upward pattern of a geological structure within a subsurface of the resource site, or a fining upward pattern of the geological structure within the subsurface of the resource site.
  • the stratigraphic marker data or geological facies data includes chronostratigraphic marker data that indicates temporal geological data associated with a subterranean structure of the resource site.
  • the well log data can comprise log motif data while the sea level data can comprise eustatic curve data.
  • the stratigraphic marker data or geological facies data comprises sequence event data or one or more characteristics of a first geological structure at the resource site that reflect an origin for the first geological structure and differentiates the first geological structure from other geological structures at the resource site.
  • the first geological structure referenced above is a rock.
  • the stratigraphic marker data or geological facies data comprises depth values defining ordered sequence boundaries associated with subsurface geological structures of one or more sections of the resource site.
  • the energy development operations comprises one or more of: well placement operations; equipment placement operations; and surgically locating a subsurface resource at the resource site.
  • the data engine may be used to generate a file, a report, or some other digital document that includes stratigraphic marker data or geological facies data, and which drive the energy development operations.
  • the stratigraphic marker data or the geological facies data may be comprised in multi-dimensional image in one or more of the file, the report, or the digital document.
  • the report may comprise the multi-dimensional image of in FIG.
  • the report, file, or digital document may be automatically transmitted to one or more stake holders (e.g, energy experts) and autonomous energy development systems (e.g., drones, robotic drilling equipment, robotic imaging equipment, robotic resource extraction equipment) to guide or otherwise drive or configure controls equipment controls such as resolution controls of sensor equipment; pump rate controls for resource extraction equipment, drill rate controls of drilling equipment, etc.
  • stake holders e.g, energy experts
  • autonomous energy development systems e.g., drones, robotic drilling equipment, robotic imaging equipment, robotic resource extraction equipment
  • controls equipment controls such as resolution controls of sensor equipment; pump rate controls for resource extraction equipment, drill rate controls of drilling equipment, etc.
  • first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used to distinguish one element from another.
  • a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of this disclosure.
  • the first object or step, and the second object or step are both objects or steps, respectively, but they are not to be considered the same object or step.
  • the term “if’ may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

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  • Geophysics (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
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Abstract

Sont divulgués des procédés, des systèmes et des programmes informatiques qui déterminent des données de marqueurs stratigraphiques pour des opérations de développement énergétique au niveau d'un site de ressource. Dans un mode de réalisation, les procédés consistent à générer un modèle stratigraphique pour le site de ressource. Le modèle stratigraphique, par exemple, comprend un modèle d'apprentissage automatique ayant au moins un paramètre. Les procédés consistent également à configurer le modèle stratigraphique au moyen de données d'entraînement, le modèle stratigraphique entraîné servant ensuite à générer, en fonction de données capturées au niveau du site de ressource, des données de marqueurs stratigraphiques ou des données de faciès géologiques pour le site de ressource. Les données de marqueurs stratigraphiques ou les données de faciès géologiques comprennent des données de limite de séquence géologique indiquant des informations de dépôts de sédiments uniformes ou non uniformes ou des informations de stratification géologique associées au site de ressource.
PCT/US2024/054210 2023-11-01 2024-11-01 Stratigraphie de séquence à haute résolution entraînée par apprentissage automatique Pending WO2025097017A1 (fr)

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Citations (3)

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Publication number Priority date Publication date Assignee Title
US20200183047A1 (en) * 2018-12-11 2020-06-11 Exxonmobil Upstream Research Company Automated Reservoir Modeling Using Deep Generative Networks
US20200301036A1 (en) * 2017-09-12 2020-09-24 Schlumberger Technology Corporation Seismic image data interpretation system
US10859725B2 (en) * 2017-05-22 2020-12-08 Sensia Llc Resource production forecasting

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Publication number Priority date Publication date Assignee Title
US10859725B2 (en) * 2017-05-22 2020-12-08 Sensia Llc Resource production forecasting
US20200301036A1 (en) * 2017-09-12 2020-09-24 Schlumberger Technology Corporation Seismic image data interpretation system
US20200183047A1 (en) * 2018-12-11 2020-06-11 Exxonmobil Upstream Research Company Automated Reservoir Modeling Using Deep Generative Networks

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