WO2023047342A1

WO2023047342A1 - System and method for monitoring subsurface reservoir changes using satellite data

Info

Publication number: WO2023047342A1
Application number: PCT/IB2022/059003
Authority: WO
Inventors: Andrey H. Shabelansky; Kurt T. Nihei; Zhishuai ZHANG; Dimitri Bevc; William J. MILLIKEN
Original assignee: Chevron USA Inc
Current assignee: Chevron USA Inc
Priority date: 2021-09-24
Filing date: 2022-09-23
Publication date: 2023-03-30
Anticipated expiration: 2024-03-24
Also published as: CA3230673A1; AU2022351700A1; EP4405714A1

Abstract

A method is described for reservoir monitoring including receiving multiple temporal InSAR datasets recorded at different times over the reservoir region, setting a location of a virtual source x _A, setting a location x _B, cross-correlating a temporal InSAR record from the virtual source x _A with a temporal InSAR record from the location x _B, summing the cross-correlation results over a temporal index to produce a processed InSAR record, storing the processed InSAR record to a processed InSAR dataset at x _B; and setting another location x _B and repeating the cross-correlating, summing, and storing steps. The method may be repeated for additional virtual source locations. The method is executed by a computer system.

Description

SYSTEM AND METHOD FOR MONITORING SUBSURFACE

RESERVOIR CHANGES USING SATELLITE DATA

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of US provisional patent application 63/248131 filed Sept. 24, 2021.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] Not applicable.

TECHNICAL FIELD

[0003] The disclosed embodiments relate generally to techniques for monitoring changes in subsurface reservoirs using satellite data and, in particular, to a method of improving the resolution of the satellite data in order to localize near-surface overburden effects and identify effects of changes in the subsurface reservoir.

BACKGROUND

[0004] Interferometric Synthetic Aperture Radar (InSAR) data is obtained from two or more remote sensing satellites that transmit pulses of microwave energy towards the Earth’s surface and record the amount of backscattered energy. InSAR data is typically used to identify surface deformation, which may be caused by near-surface overburden effects and/or changes deeper in the subsurface.

[0005] The current InSAR data measurements may suffer from poor spatial resolution that hinders the wide-spread use of the full potential of the InSAR data measurements for surface deformation and subsurface monitoring. One of the reasons for this poor resolution stems from the fact that InSAR signal measured at the surface comes from changes in a reservoir as well as in the overburden. This is illustrated in Figure 1 panel A.

[0006] To our knowledge there is no existing method that attempts to address this phenomenon and fill this gap. There exists a need for improved resolution of InSAR data in order to allow monitoring of subsurface reservoirs. SUMMARY

[0007] In accordance with some embodiments, a method of reservoir monitoring including receiving multiple temporal InSAR datasets recorded at different times over the reservoir region, setting a location of a virtual source x_A, setting a location x_B, crosscorrelating a temporal InSAR record from the virtual source x_A with a temporal InSAR record from the location x_B, summing the cross-correlation results over a temporal index to produce a processed InSAR record, storing the processed InSAR record to a processed InSAR dataset at x_fi; and setting another location x_B and repeating the cross-correlating, summing, and storing steps is disclosed.

[0008] In another aspect of the present invention, to address the aforementioned problems, some embodiments provide a non-transitory computer readable storage medium storing one or more programs. The one or more programs comprise instructions, which when executed by a computer system with one or more processors and memory, cause the computer system to perform any of the methods provided herein.

[0009] In yet another aspect of the present invention, to address the aforementioned problems, some embodiments provide a computer system. The computer system includes one or more processors, memory, and one or more programs. The one or more programs are stored in memory and configured to be executed by the one or more processors. The one or more programs include an operating system and instructions that when executed by the one or more processors cause the computer system to perform any of the methods provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 illustrates simplified examples of changes in the earth’s subsurface and how they may be recorded;

[0011] Figure 2 illustrates a flowchart of a method of reservoir monitoring, in accordance with some embodiments;

[0012] Figure 3 illustrates a synthetic experiment;

[0013] Figure 4 shows the temporal snapshots of the spatial Z-component displacement recorded at the surface for the synthetic in Figure 3; [0014] Figure 5 shows the recorded displacements at nine locations for Figure 3;

[0015] Figure 6 shows results of the present invention with virtual sources marked by the white asterisks;

[0016] Figure 7 shows field data spatial snapshots of the InSAR data taken at nine different time-lapse observations;

[0017] Figure 8 shows the field data InSAR time-series at nine different locations;

[0018] Figure 9 shows results of the present invention with virtual sources marked by the black asterisks; and

[0019] Figure 10 is a block diagram illustrating a reservoir monitoring system, in accordance with some embodiments.

[0020] Like reference numerals refer to corresponding parts throughout the drawings.

DETAILED DESCRIPTION OF EMBODIMENTS

[0021] Described below are methods, systems, and computer readable storage media that provide a manner of reservoir monitoring. These embodiments are designed to be of particular use for reservoir monitoring of hydrocarbon reservoirs and reservoir formations used for carbon sequestration. These embodiments apply principles of Green's function interferometry (GFI) to InSAR data measurements in order to improve the resolution. The application of this methodology produces localization of the near-surface overburden effects and enhancement of InSAR signal from the reservoir changes.

[0022] Reference will now be made in detail to various embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure and the embodiments described herein. However, embodiments described herein may be practiced without these specific details. In other instances, well-known methods, procedures, components, and mechanical apparatus have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0023] The present invention includes embodiments of a method and system for reservoir monitoring using InSAR data. InSAR data provides a measurement of the Earth’s surface displacements that can be used for monitoring reservoir stresses, fluid pressure and volume changes. However, the InSAR measurements may suffer from poor resolution. To improve the resolution of the InSAR data and localize the effects of the near-surface overburden, the present invention employs a Green’s function interferometry (GFI) approach that uses time-lapse InSAR data. The present invention derives the equations and computes the sensitivity between InSAR displacements caused by the reservoir changes with an observation point (i.e., virtual source) at the surface, as illustrated in Figure 1 panel B. The present invention improves resolution of the GFI-InSAR measurements that further can be used for subsurface imaging and continuous reservoir monitoring with applications to development of subsurface reservoirs, production from subsurface reservoirs, and subsurface integrity.

[0024] Figure 2 illustrates a flowchart of a method 100 for reservoir monitoring. At operation 10, InSAR datasets recorded at different times at least one day apart (time-lapse datasets) are received. At operation 11, a virtual source location is set at one of the locations of data observations. At operation 12, a spatial location is set at the same or another location of data observations. At operation 13, each temporal InSAR record from the virtual source location is zero-lag cross-correlated with the contemporaneous InSAR record for the spatial location. At operation 14, the cross-correlation results are summed for across the temporal datasets and normalized by the number of time-lapse datasets. This creates a Green’s function response (GFR) for the spatial location, which is stored at operation 15. The next spatial location for which the method will generate the GFR is set at operation 16 and operations 13, 14, and 15 are repeated until all spatial locations that need GFRs. At operation 17, a decision is made about whether another virtual source location is needed. For sensitivity analysis, meaning this is an investigation to see if time-lapse changes are visible in the time-lapse datasets, one virtual source is enough so method 100 would end. However, if the goal is to localize the changes in the subsurface, particularly at different depths in the subsurface, more virtual sources would be needed. If the answer to operation 17 is yes, then operation 18 sets the next virtual source location and method 100 repeats from operation 12. These results may then be displayed to allow identification of near-surface and reservoir-depth changes.

[0025] As explained for method 100, GFI-InSAR is based on cross-correlating each time-lapse InSAR series from each location with its neighboring locations and then summing over the observation points. The mathematical description is:

Wherein u^obs are the observed displacements at the surface locations x_A and x_B, G is the Green’s function, and S is the source signature.

[0026] For the purposes of reservoir monitoring with data collected at discrete times, we can assume that the data represents static displacement. For static displacement, the time index t does not exist and the temporal cross-correlation reduces to zero-lag cross-correlation (i.e., multiplication). Thus, the equation above becomes the following:

[0027] The input to spatial average ensemble (■) is from subsurface locations at different monitoring times. In the absence of knowledge of the subsurface locations, we use time-lapse information as input for the spatial average ensemble (i.e., number of the timelapse events correspond to the subsurface spatial locations).

[0028] To test this, we designed a 3D synthetic test with (nx, ny, nz) = (200, 200, 200) and 1 m increment (see Figure 3). We simulate 200 time-lapses with two timedependent point force sources in Z-direction located at two clusters (see blue arrows and dashed circles in Fig. 3). For the sake of simplicity, each cluster is a circular vertical plane with 10 m radius and zero azimuth. The clusters are centered at (%i,yi, z_x) = (70, 70, 20) m and (x₂,y₂, z₂)= (130, 130, 130) m and correspond to the near-surface overburden and the deep reservoir, respectively. The locations of the subsurface sources within each cluster are drawn stochastically from a uniform distribution. The maximum source magnitudes of the near-surface overburden and the deep reservoir are 10⁵ and 10⁶, respectively, with the same time-dependent diffusivity of 10^-5m²/s. To account for large source uncertainty, both source magnitudes were scaled with noise generated from uniform distribution between zero and unity. The two sources are mutually uncorrelated in time and space.

[0029] Fig. 4 shows the temporal snapshots of the spatial Z-component displacement recorded at the surface. In Fig. 5, we show the displacements at nine locations that will be used for GFR. In this figure, the early times correspond to the signal from the near-surface overburden and that at the latter times corresponds to the deep part. In Fig. 6, we present results of the GFR with virtual sources marked by the white asterisks. In these results, we observe the ability of GFR to localize the signals coming from the near-surface overburden and the deep reservoir by placing the virtual source at different locations. The virtual sources located at the vicinity of (x₁, y_t) localize the near-surface overburden signal with the largest magnitude. By distancing away from that vicinity, the magnitude of the near-surface overburden signal decreases (e.g., see the GFRs from the first column in Fig. 6) until the signal from the deep reservoir is localized (e.g., the GFRs from the third row in Fig. 6), with its largest magnitude at (x₂,y₂). The magnitudes of each GFR in Fig. 6 are auto-scaled to present large-scale magnitude variability with respect to the virtual source locations. This localization property of the GFR allows us to spatially separate the near-surface overburden and the deep reservoir signals.

[0030] To demonstrate the GFR methodology with the field data, we use the temporal InSAR data monitoring production-related activities in an oil field. The InSAR data are collected over 20 years with 471 time-lapse measurements and a temporal increment of 23 days. It is a single component displacement along the line of sight. In Fig. 7, we show representative spatial snapshots of the InSAR data taken at nine different time-lapse observations. Fig. 8 shows the InSAR time-series at nine different locations. By applying the GFR, we obtain virtual sources of the InSAR data that are shown in Fig. 9. The locations of the virtual sources are marked with a red asterisk, and they are taken at the same locations as the InSAR time-series in Fig. 8. As shown with the synthetic example, the GFR data have higher spatial variability and sensitivity than the original data in Fig. 7. This is because of the localization of the signal around the location of the virtual source. We also observe how the displacement changes spatially with respect to the position of the virtual source. The amplitudes of each GFR refer to the displacement sensitivity to the virtual source location. It is worth noting that when the virtual sources are located inside the area with high variability (for x between 1 and 3 km and for y between 0 and 2.5 km), the GFR-InSAR exhibits a similar spatial response as the original input time-lapse InSAR data than when the virtual sources are outside of this area. Based on our learnings from the synthetic example, we attribute the virtual sources inside the high variability area to the responses from the near- surface overburden, whereas those outside of this area are attributed to the responses from the deeper reservoir depths.

[0031] Figure 10 is a block diagram illustrating a reservoir monitoring system 200, in accordance with some embodiments. The system 200 may include one or more of a processor 21, an interface 22 (e.g., bus, wireless interface), an electronic storage 23, a graphical display 24, and/or other components.

[0032] The electronic storage 23 may be configured to include electronic storage medium that electronically stores information. The electronic storage 23 may store software algorithms, information determined by the processor 21, information received remotely, and/or other information that enables the system 200 to function properly. For example, the electronic storage 23 may store information relating to InSAR data, and/or other information. The electronic storage media of the electronic storage 23 may be provided integrally (i.e., substantially non-removable) with one or more components of the system 200 and/or as removable storage that is connectable to one or more components of the system 200 via, for example, a port (e.g., a USB port, a Firewire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storage 23 may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EPROM, EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. The electronic storage 23 may be a separate component within the system 200, or the electronic storage 23 may be provided integrally with one or more other components of the system 200 (e.g., the processor 21). Although the electronic storage 23 is shown in FIG. 2 as a single entity, this is for illustrative purposes only. In some implementations, the electronic storage 23 may comprise a plurality of storage units. These storage units may be physically located within the same device, or the electronic storage 23 may represent storage functionality of a plurality of devices operating in coordination.

[0033] The graphical display 24 may refer to an electronic device that provides visual presentation of information. The graphical display 24 may include a color display and/or a non-color display. The graphical display 24 may be configured to visually present information. The graphical display 24 may present information using/within one or more graphical user interfaces. For example, the graphical display 24 may present information relating to the InSAR data, the processed InSAR data, and/or other information.

[0034] The processor 21 may be configured to provide information processing capabilities in the system 200. As such, the processor 21 may comprise one or more of a digital processor, an analog processor, a digital circuit designed to process information, a central processing unit, a graphics processing unit, a microcontroller, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. The processor 21 may be configured to execute one or more machine-readable instructions 210 to facilitate reservoir monitoring. The machine-readable instructions 210 may include one or more computer program components. The machine- readable instructions 210 may include a cross-correlation component 212 and a summation component 214, and/or other computer program components.

[0035] It should be appreciated that although computer program components are illustrated in Figure 2 as being co-located within a single processing unit, one or more of computer program components may be located remotely from the other computer program components. While computer program components are described as performing or being configured to perform operations, computer program components may comprise instructions which may program processor 21 and/or system 200 to perform the operation.

[0036] While computer program components are described herein as being implemented via processor 21 through machine-readable instructions 210, this is merely for ease of reference and is not meant to be limiting. In some implementations, one or more functions of computer program components described herein may be implemented via hardware (e.g., dedicated chip, field-programmable gate array) rather than software. One or more functions of computer program components described herein may be software- implemented, hardware-implemented, or software and hardware-implemented.

[0037] Referring again to machine-readable instructions 210, the cross-correlation component 212 may be configured to cross correlate each temporal InSAR record from each location with its neighboring locations. The summation component 214 may be configured to sum the cross-correlations over the observation points (i.e., each location).

[0038] By applying the GFI approach, the present invention obtains the GFI-InSAR data with higher resolution than the original InSAR data, which can be further used for low- frequency imaging such as reverse time migration.

[0039] The description of the functionality provided by the different computer program components described herein is for illustrative purposes, and is not intended to be limiting, as any of computer program components may provide more or less functionality than is described. For example, one or more of computer program components may be eliminated, and some or all of its functionality may be provided by other computer program components. As another example, processor 21 may be configured to execute one or more additional computer program components that may perform some or all of the functionality attributed to one or more of computer program components described herein.

[0040] While particular embodiments are described above, it will be understood it is not intended to limit the invention to these particular embodiments. On the contrary, the invention includes alternatives, modifications and equivalents that are within the spirit and scope of the appended claims. Numerous specific details are set forth in order to provide a thorough understanding of the subject matter presented herein. But it will be apparent to one of ordinary skill in the art that the subject matter may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0041] The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms "includes," "including," "comprises," and/or "comprising," when used in this specification, specify the presence of stated features, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, operations, elements, components, and/or groups thereof.

[0042] As used herein, the term "if may be construed to mean "when" or "upon" or "in response to determining" or "in accordance with a determination" or "in response to detecting," that a stated condition precedent is true, depending on the context. Similarly, the phrase "if it is determined [that a stated condition precedent is true]" or "if [a stated condition precedent is true]" or "when [a stated condition precedent is true]" may be construed to mean "upon determining" or "in response to determining" or "in accordance with a determination" or "upon detecting" or "in response to detecting" that the stated condition precedent is true, depending on the context.

[0043] Although some of the various drawings illustrate a number of logical stages in a particular order, stages that are not order dependent may be reordered and other stages may be combined or broken out. While some reordering or other groupings are specifically mentioned, others will be obvious to those of ordinary skill in the art and so do not present an exhaustive list of alternatives. Moreover, it should be recognized that the stages could be implemented in hardware, firmware, software or any combination thereof.

[0044] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

What is claimed is:

1. A computer-implemented method of reservoir monitoring, comprising: a. receiving, at a computer processor, multiple temporal InSAR datasets recorded at n different times over the reservoir region at m locations at Earth’s surface; b. setting, via the computer processor, a location of a virtual source x_A within the m locations; c. setting, via the computer processor, a location x_B within the m locations; d. cross-correlating, via the computer processor, a temporal InSAR record from the virtual source x_A with a temporal InSAR record from the location x_B e. summing, via the computer processor, the cross-correlation results over a temporal index to produce a processed InSAR record; f. storing the processed InSAR record to a processed InSAR dataset at x_B and g. setting another location x_B within the m locations and repeating the crosscorrelating, summing, and storing steps.

2. The method of claim 1 further comprising repeating steps b - g for a second virtual source x_A.

3. The method of claim 1 wherein the processed InSAR dataset is normalized by the n.

4. The method of claim 1 further comprising displaying the processed InSAR dataset on a graphical display.

5. A computer system, comprising: one or more processors; memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions that when executed by the one or more processors cause the system to: a. receive, at the one or more processors, multiple temporal InSAR datasets recorded at n different times over the reservoir region at m locations at Earth’s surface; b. set, via the one or more processors, a location of a virtual source x_A within the m locations; c. set , via the one or more processors, a location x_B within the m locations; d. cross-correlate, via the one or more processors, a temporal InSAR record from the virtual source x_A with a temporal InSAR record from the location x_B e. sum, via the one or more processors, the cross-correlation results over a temporal index to produce a processed InSAR record; f. store the processed InSAR record to a processed InSAR dataset at x_B and g. set another location x_B within the m locations and repeating the crosscorrelating, summing, and storing steps.

6. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device with one or more processors and memory, cause the device to: a. receive, at the one or more processors, multiple temporal InSAR datasets recorded at n different times over the reservoir region at m locations at Earth’s surface; b. set, via the one or more processors, a location of a virtual source x_A within the m locations; c. set , via the one or more processors, a location x_B within the m locations; d. cross-correlate, via the one or more processors, a temporal InSAR record from the virtual source x_A with a temporal InSAR record from the location x_B e. sum, via the one or more processors, the cross-correlation results over a temporal index to produce a processed InSAR record; f. store the processed InSAR record to a processed InSAR dataset at x_B; and g. set another location x_B within the m locations and repeating the crosscorrelating, summing, and storing steps.