River channel prediction method and system based on structure tensor attribute and deposition distribution rule constraint

Abstract

The invention discloses a river channel prediction method and system based on structure tensor attributes and deposition distribution rule constraints, and relates to the technical field of oil-gas exploration and development reservoir prediction. Comprising the steps of establishing a three-dimensional earthquake work area; performing quality analysis on the seismic data, and determining a seismic frequency band range; obtaining a logging curve with high sensitivity to sand shale; a well-seismic relation is established through the seismic frequency band range and the high-sensitivity logging curve; performing waveform indication inversion on the well logging curve with high sensitivity to obtain a high-resolution inversion body; structure tensor attributes are extracted based on seismic data, and river channel distribution features are preliminarily obtained; obtaining a main sand body distribution range by utilizing the existing sedimentary facies diagram; carrying out sedimentary distribution rule constraint by integrating the river channel distribution characteristics and the main sand body distribution range so as to establish a sedimentary facies, and carrying out sedimentary distribution rule constraint inversion based on a high-resolution inversion body so as to obtain a high-precision main river channel sand body distribution condition; according to the invention, the precision and accuracy of river channel prediction are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of reservoir prediction technology in oil and gas exploration and development, specifically to a channel prediction method and system based on structural tensor properties and sedimentary distribution constraints. Background Technology

[0002] In the early stages of oilfield development, seismic inversion technology developed rapidly. Impedance inversion, as the most commonly used inversion technique, achieved significant results in reservoir prediction. Conventional impedance inversion methods mainly include sparse pulse inversion and geostatistical inversion. Sparse pulse inversion is limited by the narrow seismic frequency band, has low vertical resolution, and is heavily modeled. Geostatistical inversion combines stochastic simulation theory with seismic inversion methods, and has a good effect on identifying thin layers. However, in the inversion, its high-frequency components are entirely derived from inter-well interpolation, failing to effectively utilize the advantage of seismic lateral resolution, resulting in high randomness. Moreover, its interpolation method is a spatial domain interpolation, which requires high uniformity of well location distribution.

[0003] As oil reservoir development enters the middle and late stages, the focus of shallow and medium-depth research has shifted from structural oil and gas reservoirs to lithologic oil and gas reservoirs. The reservoirs have also changed from thick sandstone to thin interbedded layers. This places higher demands on the accuracy of reservoir prediction. Conventional wave impedance inversion methods can no longer meet the current needs of reservoir prediction. How to accurately predict the distribution of thin layers and main sandstone bodies is a major problem restricting oil and gas development. Summary of the Invention

[0004] The purpose of this invention is to propose a river channel prediction method and system, which uses the comprehensive structural tensor properties to characterize the river channel and the main sand bodies of sedimentary facies to constrain the sedimentary distribution patterns, thereby significantly improving the accuracy and precision of river channel prediction.

[0005] According to a first aspect of the present disclosure, a channel prediction method based on structural tensor properties and sedimentary distribution patterns is provided, comprising the following steps:

[0006] Establish a three-dimensional seismic work area and load seismic data, well logging data, and seismic interpretation results;

[0007] Perform quality analysis on earthquake data to determine the earthquake frequency band range;

[0008] Based on well logging data, obtain well logging curves that are highly sensitive to sandstone and mudstone;

[0009] Establish well-seismic correlation by utilizing seismic frequency bands and highly sensitive logging curves;

[0010] Waveform indication inversion is performed on highly sensitive well logging curves, and a high-resolution inversion body is obtained by combining seismic interpretation results and well-seismic correlation.

[0011] Based on the extraction of structural tensor properties from seismic data, the river channel boundaries are characterized, and the preliminary characteristics of the river channel distribution are obtained.

[0012] In one embodiment, it further includes:

[0013] The distribution range of the main sand bodies was obtained by using existing sedimentary facies diagrams, and the orientation, dip and possible connectivity of the sand bodies were preliminarily determined.

[0014] In one embodiment, it further includes:

[0015] By constraining the sedimentary distribution patterns based on the comprehensive characteristics of the river channel and the distribution range of the main sand bodies, a sedimentary facies is established. Based on the high-resolution inversion body, the sedimentary distribution pattern is constrained and inverted to obtain a high-precision distribution of the main channel sand bodies.

[0016] In one embodiment, the logging data includes wellhead coordinates, core elevation, well trajectory, logging curves, etc., and the seismic interpretation results include target layer position and fault data.

[0017] In one embodiment, each logging curve is standardized and calibrated before obtaining highly sensitive logging curves to eliminate the uncertainties caused by different instruments, abnormal well diameters, and differences in drilling mud properties to the inversion results.

[0018] In one embodiment, the logging curves are subjected to sensitivity analysis using statistical methods, intersection plots, and geological pattern recognition technology. The response characteristics of different logging curves in sandstone and mudstone formations are compared to obtain logging curves that are highly sensitive to sandstone and mudstone.

[0019] In one embodiment, extracting structural tensor properties based on seismic data specifically includes:

[0020] The variance E(x,y,z) of a three-dimensional seismic image is defined as:

[0021] E(x,y,z)=∑w(x,y,z)×[I(x+Δx,y+Δy,z+Δz)-I(x,y,z)] 2 (1)

[0022] In the formula, I(x,y,z) is the three-dimensional seismic amplitude function, x,y,z are the line and trace positions and two-way travel time of the three-dimensional seismic data, respectively; w(x,y,z) is the Gaussian window function;

[0023] Given displacements (Δx, Δy, Δz), expanding the attribute function I according to the Taylor sequence yields its first-order approximation:

[0024] E(x,y,z)=I(x+Δx,y+Δy,z+Δz)-I(x,y,z)≈ΔxI x+ΔyI y +ΔzI z (2)

[0025] In the formula, I x ,I y ,I z These are the first-order partial derivatives of the attribute function I along the x, y, and z directions, respectively;

[0026] Squaring both sides of equation (2), we get:

[0027]

[0028] The second term on the right-hand side of equation (3) is the structure tensor.

[0029] According to a second aspect of the present disclosure, a channel prediction system based on structural tensor properties and sedimentary distribution patterns is provided, comprising:

[0030] The seismic work area construction module establishes a 3D seismic work area and loads seismic data, well logging data, and seismic interpretation results.

[0031] The seismic frequency band determination module performs quality analysis on seismic data to determine the seismic frequency band range;

[0032] The well logging curve acquisition module acquires well logging curves that are highly sensitive to sandstone and mudstone based on well logging data.

[0033] The well-seismic correlation acquisition module establishes well-seismic correlations through seismic frequency band range and highly sensitive logging curves;

[0034] The high-resolution inversion volume acquisition module performs waveform indication inversion on highly sensitive well logging curves and obtains a high-resolution inversion volume by combining seismic interpretation results and well-seismic correlation.

[0035] The preliminary river channel distribution feature acquisition module extracts structural tensor attributes based on seismic data, delineates river channel boundaries, and obtains preliminary river channel distribution features.

[0036] According to a third aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the memory. When the processor executes the program, it implements the aforementioned method for predicting river channels based on structural tensor properties and sediment distribution patterns.

[0037] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the aforementioned method for predicting river channels based on structural tensor properties and sediment distribution patterns.

[0038] Compared with the prior art, the above technical solutions adopted in this invention have the following advantages: The river channel prediction method and system provided by this invention improves the vertical resolution while maintaining consistency with the lateral and seismic waveform change characteristics. By constraining the sedimentary distribution law of the river channel and the main sand bodies of the sedimentary facies characterized by the comprehensive structural tensor attributes, the accuracy and precision of river channel prediction will be greatly improved, providing a basis for zone research and deployment, and effectively supporting the deployment of shallow and medium-depth well locations and exploration and development. Attached Figure Description

[0039] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.

[0040] Figure 1 The flowchart shows a channel prediction method based on structural tensor properties and sediment distribution patterns.

[0041] Figure 2 A schematic diagram illustrating the selection of sample points for waveform indication inversion;

[0042] Figure 3 For structural tensor river channel distribution diagram;

[0043] Figure 4 Schematic diagram of sedimentary facies;

[0044] Figure 5 This is a schematic diagram of the waveform inversion profile;

[0045] Figure 6 For structural tensor river channel distribution prediction map;

[0046] Figure 7 Sedimentary facies diagram of oil-bearing strata;

[0047] Figure 8 This is a schematic diagram of a planar phase constraint section;

[0048] Figure 9 Schematic diagram of inversion profile and layered comparison profile constrained by waveform to indicate sedimentary distribution patterns;

[0049] Figure 10 A schematic diagram of the inversion profile and reservoir correlation profile constrained by the waveform indicating the sedimentary distribution pattern;

[0050] Figure 11 This is an inversion thickness map of the oil-bearing strata. Specific implementation methods

[0051] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0052] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0053] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0054] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of this disclosure. It should be noted that each block in a flowchart or block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0055] Example 1:

[0056] like Figure 1 As shown, this embodiment provides a channel prediction method based on structural tensor properties and sedimentary distribution patterns. Taking a real 3D seismic survey area as an example, it includes the following steps:

[0057] S1. Establish a 3D seismic work area and load seismic data, well logging data, and seismic interpretation results;

[0058] Specifically, well logging data includes wellhead coordinates, core elevation, well trajectory, and logging curves, while seismic interpretation results include target layer position and fault data.

[0059] S2. Perform quality analysis on seismic data to determine the seismic frequency band range;

[0060] Specifically, the seismic frequency band was determined to be 8-100Hz, so as to understand the propagation characteristics and attenuation laws of seismic waves, providing an important basis for subsequent earthquake prediction.

[0061] S3. Obtain logging curves that are highly sensitive to sandstone and mudstone based on logging data;

[0062] Specifically, before obtaining highly sensitive logging curves, each logging curve is standardized and calibrated. After calibration, the wave impedance and GR logging curves can better identify favorable reservoirs in the entire area.

[0063] Sensitivity analysis of well logging curves was conducted using statistical methods, intersection plots, and geological pattern recognition techniques. The response characteristics of different well logging curves in sandstone and mudstone formations were compared to identify well logging curves with high sensitivity to sandstone and mudstone. Generally, well logging curves that can clearly and stably reflect the sandstone-mudstone interface and lithological changes are considered to have high sensitivity, providing favorable support for subsequent detailed description of river channels.

[0064] S4. Establish well-seismic correlation through seismic frequency band range and highly sensitive logging curves;

[0065] S5. Perform waveform indication inversion on highly sensitive well logging curves, and combine the seismic interpretation results and well-seismic correlation to obtain a high-resolution inversion body;

[0066] like Figure 2 The diagram shows the selection of sample points for waveform indication inversion. Based on the waveform similarity and spatial distance of known wells, wells with similar low-frequency structures are selected as spatial estimation samples. The sample well curves are analyzed, that is, well logging curves with similar seismic waveforms are analyzed.

[0067] S6. Based on seismic data, structural tensor properties are extracted to characterize the river channel boundary and obtain preliminary characteristics of the river channel distribution.

[0068] It should be noted that, since the delineation and distribution of river boundaries are controlled by a variety of geological factors, the prediction results need to be verified and corrected by integrating multiple pieces of information.

[0069] like Figure 3 The image shown is a structural tensor channel distribution prediction map. By treating seismic data as an image and identifying different texture units in the seismic image, such as layered textures and chaotic textures, the automatic detection of geological targets can be achieved. Abandoned channels exhibit small bead-like features on seismic profiles. Structural tensor properties can be used as an effective technique for characterizing small bead-like geological anomalies, and can clearly characterize the boundaries of reservoir channels.

[0070] S7. Use existing sedimentary facies diagrams to obtain the distribution range of the main sand bodies and make a preliminary judgment on the strike, dip and possible connectivity of the sand bodies.

[0071] Among them, sedimentary facies diagrams, such as Figure 4 As shown;

[0072] S8. The sedimentary distribution pattern is constrained by the comprehensive characteristics of the river channel and the distribution range of the main sand bodies. Based on this, the sedimentary facies is established. Based on the high-resolution inversion body, the sedimentary distribution pattern is constrained and inverted to obtain the high-precision distribution of the main channel sand bodies.

[0073] Inversion profile as Figure 5 As shown, from the perspective of well-seismic relationship, waveform indication inversion improves vertical resolution while maintaining consistency with the lateral and seismic waveform variation characteristics, and has good applicability in this area;

[0074] like Figure 6 As shown, the channel distribution can be obtained by using the structure tensor, and the distribution of the main channel sand bodies can be obtained by further constraining the inversion impedance volume.

[0075] Depend on Figure 7 Sedimentary facies diagram of oil-bearing strata to Figure 8 Planar facies-constrained profiles are used to further obtain the distribution of abandoned small channels by utilizing structural tensors based on waveform indication inversion, and the distribution of major sand bodies by utilizing existing sedimentary facies maps. The sedimentary distribution patterns of small channels and major channels are combined to establish sedimentary facies and constrain them.

[0076] from Figure 9 It can be seen that the sand body distribution in the constrained inversion profile matches the drilling sand body well. Figure 10 It can be seen that the constrained inversion profile matches the known reservoirs well, which can meet the exploration requirements.

[0077] Figure 11 This is a thickness map of the oil-bearing strata, with sedimentary distribution patterns constraining the inversion results, sand body distribution characteristics, and sedimentary facies diagram. Figure 7 The results are relatively consistent, indicating that the inversion results are reliable. Furthermore, the analysis of the sedimentary distribution patterns of another sandstone group in the oil-bearing strata showed that the consistency rate of the inversion predictions was above 85%, significantly improving the accuracy of channel predictions and providing valuable support for exploration deployment in the study area.

[0078] Example 2:

[0079] This embodiment provides a river channel prediction system based on structural tensor properties and sedimentary distribution patterns, including:

[0080] The seismic work area construction module establishes a 3D seismic work area and loads seismic data, well logging data, and seismic interpretation results.

[0081] The seismic frequency band determination module performs quality analysis on seismic data to determine the seismic frequency band range;

[0082] The well logging curve acquisition module acquires well logging curves that are highly sensitive to sandstone and mudstone based on well logging data.

[0083] The well-seismic correlation acquisition module establishes well-seismic correlations through seismic frequency band range and highly sensitive logging curves;

[0084] The high-resolution inversion volume acquisition module performs waveform indication inversion on highly sensitive well logging curves and obtains a high-resolution inversion volume by combining seismic interpretation results and well-seismic correlation.

[0085] The preliminary river channel distribution feature acquisition module extracts structural tensor attributes based on seismic data, delineates river channel boundaries, and obtains preliminary river channel distribution features.

[0086] Example 3:

[0087] An electronic device includes a memory, a processor, and a computer program stored in the memory and running thereon. When the processor executes the program, it implements the aforementioned channel prediction method based on structural tensor properties and sedimentary distribution constraints, comprising:

[0088] Establish a three-dimensional seismic work area and load seismic data, well logging data, and seismic interpretation results;

[0089] Perform quality analysis on earthquake data to determine the earthquake frequency band range;

[0090] Based on well logging data, obtain well logging curves that are highly sensitive to sandstone and mudstone;

[0091] Establish well-seismic correlation by utilizing seismic frequency bands and highly sensitive logging curves;

[0092] Waveform indication inversion is performed on highly sensitive well logging curves, and a high-resolution inversion body is obtained by combining seismic interpretation results and well-seismic correlation.

[0093] Based on the extraction of structural tensor properties from seismic data, the river channel boundaries are characterized, and the preliminary characteristics of the river channel distribution are obtained.

[0094] Example 4:

[0095] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned channel prediction method based on structural tensor properties and sedimentary distribution constraints, comprising:

[0096] Establish a three-dimensional seismic work area and load seismic data, well logging data, and seismic interpretation results;

[0097] Perform quality analysis on earthquake data to determine the earthquake frequency band range;

[0098] Based on well logging data, obtain well logging curves that are highly sensitive to sandstone and mudstone;

[0099] Establish well-seismic correlation by utilizing seismic frequency bands and highly sensitive logging curves;

[0100] Waveform indication inversion is performed on highly sensitive well logging curves, and a high-resolution inversion body is obtained by combining seismic interpretation results and well-seismic correlation.

[0101] Based on the extraction of structural tensor properties from seismic data, the river channel boundaries are characterized, and the preliminary characteristics of the river channel distribution are obtained.

[0102] This invention plays a crucial role in effectively identifying sand body distribution in the context of river channel prediction for oil and gas resources. Using this invention, the consistency rate between sedimentary distribution patterns constrained inverted sand body distribution and drilled sandstone increased from 73.2% to 88.7%, effectively guiding well location deployment in the region and playing a significant role in the utilization of remaining resources.

[0103] Those skilled in the art will understand that the modules or steps described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, which can then be stored in a storage device for execution by a computer device. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. This disclosure is not limited to any particular combination of hardware and software.

[0104] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0105] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A channel prediction method based on structural tensor properties and sedimentary distribution patterns, characterized in that, Includes the following steps: Establish a three-dimensional seismic work area and load seismic data, well logging data, and seismic interpretation results; Perform quality analysis on earthquake data to determine the earthquake frequency band range; Based on well logging data, obtain well logging curves that are highly sensitive to sandstone and mudstone; Establish well-seismic correlation by utilizing seismic frequency bands and highly sensitive logging curves; Waveform indication inversion is performed on highly sensitive well logging curves, and a high-resolution inversion body is obtained by combining seismic interpretation results and well-seismic correlation. Based on the extraction of structural tensor properties from seismic data, the river channel boundaries are characterized, and the preliminary characteristics of the river channel distribution are obtained.

2. The channel prediction method based on structural tensor properties and sedimentary distribution constraints according to claim 1, characterized in that, Also includes: The distribution range of the main sand bodies was obtained by using existing sedimentary facies diagrams, and the orientation, dip and possible connectivity of the sand bodies were preliminarily determined.

3. The channel prediction method based on structural tensor properties and sedimentary distribution constraints according to claim 2 further includes: By constraining the sedimentary distribution patterns based on the comprehensive characteristics of the river channel and the distribution range of the main sand bodies, a sedimentary facies is established. Based on the high-resolution inversion body, the sedimentary distribution pattern is constrained and inverted to obtain a high-precision distribution of the main channel sand bodies.

4. The channel prediction method based on structural tensor properties and sedimentary distribution constraints as described in claim 1, wherein the well logging data includes wellhead coordinates, core elevation, well trajectory, and well logging curves, and the seismic interpretation results include target layer position and fault data.

5. The channel prediction method based on structural tensor properties and sedimentary distribution law constraints as described in claim 1, wherein each well logging curve is standardized and calibrated before obtaining highly sensitive well logging curves.

6. The channel prediction method based on structural tensor properties and sedimentary distribution patterns as described in claim 1 uses statistical methods, intersection plots, and geological pattern recognition technology to perform sensitivity analysis on well logging curves, compares the response characteristics of different well logging curves in sandstone and mudstone formations, and obtains well logging curves that are highly sensitive to sandstone and mudstone.

7. The channel prediction method based on structural tensor properties and sedimentary distribution patterns as described in claim 1, wherein structural tensor properties are extracted from seismic data, specifically including: The variance E(x,y,z) of a three-dimensional seismic image is defined as: E(x,y,z)=∑w(x,y,z)×[I(x+Δx,y+Δy,z+Δz)-I(x,y,z)] 2 (1) In the formula, I(x,y,z) is the three-dimensional seismic amplitude function, x,y,z are the line and trace positions and two-way travel time of the three-dimensional seismic data, respectively; w(x,y,z) is the Gaussian window function; Given displacements (Δx, Δy, Δz), expanding the attribute function I according to the Taylor sequence yields its first-order approximation: E(x,y,z)=I(x+Δx,y+Δy,z+Δz)-I(x,y,z)≈ΔxI x +ΔyI y +ΔzI z (2) In the formula, I x ,I y ,I z These are the first-order partial derivatives of the attribute function I along the x, y, and z directions, respectively; Squaring both sides of equation (2), we get: The second term on the right-hand side of equation (3) is the structure tensor.

8. A channel prediction system based on structural tensor properties and sedimentary distribution patterns, characterized in that, include: The seismic work area construction module establishes a 3D seismic work area and loads seismic data, well logging data, and seismic interpretation results. The seismic frequency band determination module performs quality analysis on seismic data to determine the seismic frequency band range; The well logging curve acquisition module acquires well logging curves that are highly sensitive to sandstone and mudstone based on well logging data. The well-seismic correlation acquisition module establishes well-seismic correlations through seismic frequency band range and highly sensitive logging curves; The high-resolution inversion volume acquisition module performs waveform indication inversion on highly sensitive well logging curves and obtains a high-resolution inversion volume by combining seismic interpretation results and well-seismic correlation. The module for preliminary acquisition of river channel distribution characteristics extracts structural tensor attributes from seismic data, delineates river channel boundaries, and obtains preliminary river channel distribution characteristics.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running thereon, characterized in that, When the processor executes the program, it implements the channel prediction method based on structural tensor properties and sediment distribution law constraints as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the channel prediction method based on structural tensor properties and sedimentary distribution constraints as described in any one of claims 1-7.

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