Reservoir heterogeneity zone identification method and apparatus, storage medium, and processor

Abstract

Embodiments of the present invention relate to the field of data processing, and provide a reservoir heterogeneity zone identification method and apparatus, a storage medium, and a processor. The method comprises: calculating statistical measures of pre-selected seismic attributes in a reservoir seismic horizon; and drawing a reservoir on the basis of the statistical measures of the seismic attributes, so as to identify a reservoir heterogeneity zone. The present invention overcomes the limitations of the prior art, and also provides an innovative capability for reservoir characterization.

Description

Methods, apparatus, storage media and processor for identifying reservoir heterogeneity regions

[0001] Cross-reference to related applications

[0002] This application claims the benefit of Chinese Patent Application No. 202411990869.5, filed on December 31, 2024, the contents of which are incorporated herein by reference. Technical Field

[0003] This invention relates to the field of data processing, and more specifically to a method, apparatus, storage medium, and processor for identifying reservoir heterogeneity regions. Background Technology

[0004] Seismic data is widely used for subsurface characterization because it provides high-resolution imaging of the Earth's structure. However, seismic interpretation is often challenged by the complexity of subsurface geology and the inherent variations in seismic amplitude, phase, and frequency. These variations can be attributed to factors such as changes in rock and fluid properties, noise during acquisition or processing, and geological heterogeneity, particularly in complex reservoirs such as carbonate rocks and fracture systems.

[0005] The applicant notes that we are currently at a critical juncture where modern and traditional seismic workflows coexist, but there is no direct and rapid way to assess heterogeneity indicators from seismic events and seismic properties, given that heterogeneity is the answer to key reservoir management strategies.

[0006] Existing seismic data interpretation solutions primarily involve techniques such as seismic inversion, amplitude versus migration (AVO) analysis, and the use of single seismic attributes like impedance. However, these methods typically focus on one or two attributes, neglecting the combined effects of multiple attributes such as amplitude, phase, and frequency. Furthermore, traditional seismic inversion methods often require significant computational resources and expert input, leading to slow workflows. While some machine learning-based methods have emerged to address heterogeneity issues, these solutions typically require large amounts of labeled data, extensive data preparation, and computational power, making them less practical for certain field applications. Currently known techniques cannot directly indicate reservoir heterogeneity. Summary of the Invention

[0007] The purpose of this invention is to provide a method, apparatus, storage medium, and processor for identifying reservoir heterogeneity regions. This method presents the results in an intuitive format to facilitate decision-making.

[0008] To achieve the above objectives, embodiments of the present invention provide a method for identifying reservoir heterogeneity regions, the method comprising:

[0009] Calculate statistical measures of each pre-selected seismic attribute in the reservoir seismic horizon;

[0010] Reservoirs are plotted based on statistical measures of the aforementioned seismic attributes to identify regions of reservoir heterogeneity.

[0011] Optionally, before calculating the statistical measures of each seismic attribute, the method further includes:

[0012] Input seismic data and preprocess the seismic data;

[0013] Seismic attributes are obtained from the preprocessed seismic data, and seismic attributes are selected for calculating statistical metrics.

[0014] Optionally, calculating the statistical measures of each pre-selected seismic attribute in the reservoir seismic horizon includes: calculating the statistical measures of each seismic attribute using a three-dimensional rolling window method.

[0015] Optionally, the statistical measure is the standard deviation.

[0016] Optionally, the standard deviation of each seismic attribute can be calculated using the three-dimensional rolling window method, including:

[0017] Calculate the standard deviation of each seismic attribute in the three-dimensional scrolling window at the seismic horizon;

[0018] Calculate the weight of each seismic attribute in the three-dimensional scrolling window;

[0019] The standard deviation of each seismic attribute is normalized in the three-dimensional scrolling window.

[0020] Optionally, mapping the reservoir based on statistical measures of each seismic attribute includes:

[0021] The volume heterogeneity index is calculated based on the standard deviation of each seismic attribute, and the reservoir is plotted based on the volume heterogeneity index.

[0022] Optionally, mapping the reservoir based on the volume heterogeneity index includes: mapping a spatial heterogeneity map and a three-dimensional model based on the volume heterogeneity index of the reservoir.

[0023] Another objective of this invention is to provide a reservoir heterogeneity region identification device, the device comprising:

[0024] The calculation module is used to calculate the statistical measures of each pre-selected seismic attribute in the reservoir seismic horizon;

[0025] The mapping module is used to map the reservoir based on the statistical measures of the seismic attributes to identify reservoir heterogeneity regions.

[0026] Preferably, the device further includes:

[0027] The input module is used to input seismic data;

[0028] The preprocessing module is used to preprocess seismic data;

[0029] The selection module is used to obtain seismic attributes from preprocessed seismic data and select the seismic attributes used to calculate statistical measures.

[0030] Preferably, the calculation module is also used to calculate statistical measures of each seismic attribute using a three-dimensional rolling window method.

[0031] Preferably, the statistical measure is the standard deviation.

[0032] Preferably, the calculation module is further configured to calculate the standard deviation of each seismic attribute in the three-dimensional scrolling window at the seismic horizon; calculate the weight of each seismic attribute in the three-dimensional scrolling window; and normalize the standard deviation of each seismic attribute in the three-dimensional scrolling window.

[0033] Preferably, the drawing module is further configured to calculate the volume heterogeneity index based on the standard deviation of each seismic attribute, and draw the reservoir based on the volume heterogeneity index.

[0034] Preferably, the drawing module is also used to draw a spatial non-uniformity map and a three-dimensional model based on the reservoir's volume non-homogeneity index.

[0035] On the other hand, embodiments of this application provide a machine-readable storage medium storing instructions that cause a machine to perform the identification method.

[0036] Finally, embodiments of this application provide a processor for running a program, wherein the program is run to perform the identification method described above.

[0037] Through the above technical solution, this invention has significant advantages, overcoming the limitations of existing technologies and providing innovative capabilities for reservoir characterization. By integrating multiple seismic attributes, this invention provides a comprehensive understanding of heterogeneity, capturing subtle differences missed by single-attribute analyses. Scrolling window analysis improves spatial resolution, enabling detailed understanding of local variations.

[0038] The computational efficiency of this invention supports real-time data processing, making it highly scalable and suitable for large datasets. The visualization tools of this invention simplify complex analyses, presenting results in an intuitive format to facilitate decision-making. Furthermore, the system's adaptability to various reservoir types ensures its broad applicability across diverse geological contexts. These advantages reduce operational risks, optimize resource extraction, and support data-driven reservoir management strategies.

[0039] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0040] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0041] Figure 1 is a flowchart of the existing technical solutions;

[0042] Figure 2 is an example flowchart of the identification method provided in this application;

[0043] Figure 3 is a detailed flowchart of the identification method provided in this application;

[0044] Figure 4 is a schematic diagram of the identification device provided in this application. Detailed Implementation

[0045] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0046] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0047] This invention addresses the challenge of characterizing the heterogeneity of complex reservoirs. Reservoir heterogeneity significantly impacts fluid flow, particularly in processes such as enhanced oil recovery (EOR) using water injection. This invention employs multivariate statistical methods, leveraging information such as seismic amplitude, phase, and frequency, as well as traditional seismic properties, to predict heterogeneity indicators. Multivariate analysis is a statistical technique that simultaneously studies three or more variables related to the subject matter, aiming to identify or elucidate the relationships between them. Traditional methods for analyzing seismic data often involve qualitative interpretations of seismic properties or labor-intensive workflows that may fail to adequately capture small-scale variations. Standard deviation is a robust statistical measure well-suited for quantifying variations in data sets and identifying anomalous patterns. When applied to seismic data and property volumes, standard deviation can provide a quantitative measure of subsurface heterogeneity.

[0048] This invention introduces a method for calculating and visualizing the standard deviation of seismic datasets and attribute volumes, providing geophysicists with a rapid tool for analyzing seismic data variations and reservoir engineers with a deeper understanding of geological heterogeneity.

[0049] This invention may instruct interested seismic inversion experts or quantitative interpretation (QI) geophysicists to rapidly score reservoir heterogeneity, thereby enabling a quick understanding of the main heterogeneity challenges of an oilfield in both time and space. Heterogeneity can guide geophysicists to the necessity of stochasticity in reservoir characterization—for example, reservoirs with higher heterogeneity indices are naturally better candidates for performing stochastic seismic inversions.

[0050] This invention aims to assess the impact of earthquakes and seismic properties on reservoir heterogeneity characteristics, which can serve as potential indicators for understanding water front behavior, predicting oilfield-scale behavior, minimizing bypass areas, and improving reservoir management.

[0051] The purpose of this invention is to provide geoscientists and reservoir engineers with a rapid and effective indicator for understanding and quantifying changes in seismic data and reservoir heterogeneity. By utilizing statistical indicators applied to seismic data and seismic property volumes, particularly standard deviation, this invention enables users to identify and quantify amplitude, phase, and frequency variations in seismic data, thereby better interpreting and processing decisions; providing a seismic-driven proxy for subsurface heterogeneity, thus contributing to reservoir management strategies such as enhanced oil recovery (EOR) and water injection optimization; and linking geophysical insights with reservoir engineering objectives by translating seismic variations into reservoir management metrics.

[0052] Seismic data reveals variations in key reservoir characteristics (such as matrix, porosity, pore interconnectivity, lithofacies, fractures, faults, etc.), including amplitude and phase variations across different frequency bands. Multivariate statistical modeling of seismic events and their properties allows for a fair estimation of the heterogeneity present in seismic data. Therefore, this application can express variations in seismic amplitude, phase, and frequency and convert them into seismically driven heterogeneity indices. Preferably, the results of this application can be validated based on any available evidence, such as well data, production data, or known geological changes in the oilfield.

[0053] Figure 1 shows a flowchart of an existing technical solution, which includes:

[0054] Step 101: Input earthquake data;

[0055] Step 102: Process the data to improve its quality;

[0056] Step 103: Perform seismic inversion to obtain a single property (e.g., impedance);

[0057] Step 104: Analyze attributes individually to identify changes;

[0058] Step 105: Visualize the results of a single attribute based on the base map or cross-section.

[0059] Existing seismic data interpretation solutions primarily involve seismic inversion and the use of impedance. However, these methods typically focus on one or two attributes, neglecting the combined effects of multiple attributes such as amplitude, phase, and frequency. Furthermore, traditional seismic inversion methods often require significant computational resources and expert opinions, leading to slower workflows.

[0060] To overcome the shortcomings of existing solutions, the method of this application aims to quantify subsurface heterogeneity by analyzing changes in seismic data using multivariate statistical methods, with particular focus on seismic properties such as amplitude, phase, and frequency.

[0061] Figure 2 shows an example flowchart of the identification method provided by the present invention, which includes:

[0062] Calculate statistical measures of each pre-selected seismic attribute in the reservoir seismic horizon;

[0063] Reservoirs are plotted based on statistical measures of the aforementioned seismic attributes to identify regions of reservoir heterogeneity.

[0064] In this invention, multiple seismic attributes, such as amplitude, phase, and frequency, are combined with statistical measures (e.g., standard deviation) to generate a heterogeneity index. This invention uses a multivariate approach to capture variations in both the horizontal and vertical directions. Furthermore, it employs a three-dimensional rolling window to calculate the standard deviation of seismic horizons, thereby taking into account local variations and interdependencies often overlooked in conventional methods. This invention provides a direct and interpretable indicator of heterogeneity. Traditional seismic methods tend to analyze attributes in isolation or use simple static models. Dynamic, multi-attribute statistical methods can create more accurate and detailed maps of subsurface variation, offering innovative solutions to the complex problems of reservoir characterization.

[0065] Figure 3 is a detailed flowchart of the identification method provided in this application, which includes the following steps:

[0066] Step 301, Input Data

[0067] The input data may include seismic datasets in SEG-Y format or a similar format, as well as seismic horizons used to depict the analysis window. Optionally, seismic inversion impedance and other seismic properties derived from seismicity, phase, or frequency may also be included as input data.

[0068] Step 302: Preprocessing

[0069] To ensure data quality, seismic data can be constrained and noise levels assessed. Furthermore, a signal-to-noise ratio (SNR) plot can be calculated to identify and reduce noise before further analysis. Optionally, the input data can also be normalized and error-corrected.

[0070] Preferably, the noise level of the seismic data can be understood, and the seismic data can be subject to necessary constraints to ensure that calculations are based on excellent data input.

[0071] Step 303: Attribute Generation and Selection

[0072] This step involves generating seismic properties that reflect key geological and rock physical characteristics, and selecting relevant properties for further analysis.

[0073] The purpose of this invention is to convert seismic data into meaningful indicators to reveal subsurface characteristics such as lithology, porosity, fluid content, and structural complexity.

[0074] Seismic properties are derived from data such as amplitude, phase, frequency, and impedance. These properties are calculated using mathematical operations based on seismic traces or through advanced seismic inversion techniques.

[0075] The selection of appropriate attributes is based on their ability to highlight reservoir variations and heterogeneity. Attribute selection can be accomplished using statistical techniques or domain expertise.

[0076] Choosing appropriate attributes ensures that diagnostic attributes that capture reservoir heterogeneity are included in subsequent statistical calculations.

[0077] The following lists seismic properties and impedance-based properties that are industry standard attributes. This application is not limited to these properties. The following example properties can be used in the workflow of this application:

[0078] Amplitude-based properties: These properties are derived from the amplitude of seismic waves and are commonly used to infer lithology and fluid characteristics. They include root mean square amplitude, instantaneous amplitude, average amplitude, amplitude envelope, and amplitude variance.

[0079] Frequency-based properties: These properties measure the frequency content of seismic signals and help detect changes in strata and structures, including dominant frequency, instantaneous frequency, spectral decomposition (e.g., low-frequency, mid-frequency, and high-frequency components), and frequency bandwidth.

[0080] Phase-based properties: Phase properties are crucial for understanding the continuity and structural features in seismic data, including instantaneous phase, relative phase, Hilbert phase, and phase continuity.

[0081] Impedance-based properties: Impedance properties are crucial for reservoir characterization, especially when seismic inversion data is available, including acoustic impedance, relative acoustic impedance, elastic impedance, and gradient impedance.

[0082] Step 304: Multivariate Statistical Calculation

[0083] To perform multivariate statistical calculations, seismic attributes indicating reservoir characteristics, such as amplitude, frequency, phase, P-wave and S-wave velocities, or externally available seismic attributes can be selected for statistical calculation. Then, a three-dimensional (3D) rolling window method is applied over the selected seismic horizons to calculate the standard deviation of the seismic attributes, thereby capturing variations in both the vertical and horizontal directions.

[0084] To apply the 3D scrolling window method, the window size must first be defined; that is, an appropriate scrolling computation window size must be selected for applying the method, such as the cube product of a seismic bin. Secondly, the standard deviation is calculated using the following steps:

[0085] In this step, a 3D scrolling window W(m×n×p) is applied to the seismic data volume to calculate the standard deviation of each seismic attribute for each seismic box. This method can capture both lateral (horizontal) and longitudinal (stratum direction) heterogeneity.

[0086] in:

[0087] It is earthquake attribute a j The standard deviation of seismic chamber i is given. Seismic chamber i is the central seismic chamber [voxel] of the three-dimensional scrolling window, and calculations are performed in this seismic chamber; i and j are natural numbers.

[0088] N = m × n × p is the total number of seismic boxes in the 3D scrolling window; m, n, and p are natural numbers.

[0089] a j,k It is the seismic attribute a in the seismic chamber k within the 3D scrolling window. j The value of ; k is a natural number;

[0090] The earthquake attribute a in the 3D scrolling window j Average value:

[0091] The 3D rolling window method can be applied to all seismic horizons of the reservoir.

[0092] Step 305: Calculate the global standard deviation and weights of seismic attributes.

[0093] Each seismic attribute contributes differently to characterizing subsurface heterogeneity. Therefore, this step calculates data-driven weights w for each seismic attribute based on global variations across the entire seismic dataset. j :

[0094] in

[0095] It is the earthquake attribute 'a' on the entire earthquake dataset. j The global standard deviation is calculated using the following formula:

[0096] M is the total number of seismic volumes;

[0097] It is earthquake attribute a j The global mean; j is a natural number;

[0098] a j,k It is earthquake attribute a j The value of k in the seismic chamber; k is a natural number.

[0099] Data-driven weighting schemes ensure that attributes with greater global variability have a larger impact on the final heterogeneity metric. This method dynamically adjusts for differences in attribute importance, making the heterogeneity measure more meaningful and reflecting the actual characteristics of the data.

[0100] Step 306: Normalize 3D standard deviation

[0101] To ensure fair comparisons between attributes at different units and scales, the 3D standard deviation is calculated from the local mean of each attribute within a 3D scrolling window. Normalize:

[0102] It is earthquake attribute a j The standard deviation of seismic chamber i is given. Seismic chamber i is the central seismic chamber [voxel] of the three-dimensional scrolling window, and calculations are performed in this seismic chamber; i and j are natural numbers.

[0103] a j,k It is earthquake attribute a j The value of k in the seismic chamber; k is a natural number, and N is the total number of seismic chambers in the 3D scrolling window.

[0104] Normalization eliminates the bias caused by the varying magnitudes of different seismic attributes, allowing all attributes to contribute proportionally to the heterogeneity index. This step ensures that the analysis focuses on relative changes rather than absolute values, which is crucial for identifying areas of enhanced heterogeneity.

[0105] Step 307: Calculate the Volume Heterogeneity Index (VHI)

[0106] The heterogeneity index for each seismic box i is calculated as a weighted sum of the normalized 3D standard deviations of all seismic attributes:

[0107] in:

[0108] w j It is earthquake attribute a j Data-driven weights; j is a natural number;

[0109] It is earthquake attribute a j The normalized 3D standard deviation of seismic chamber i; i is a natural number;

[0110] M represents the total number of earthquake attributes.

[0111] This formula integrates the contributions of all seismic attribute variations into a single heterogeneity measure, weighted according to their importance. The resulting VHI provides a comprehensive volumetric representation of heterogeneity, taking into account spatial complexity and the importance of attributes.

[0112] Step 308: Visualization and Analysis

[0113] The Seismic Heterogeneity Indicator for Reservoir (SHIRe) volume is visualized to map different heterogeneous regions throughout the reservoir. Preferably, SHIRe values ​​are correlated with geological, well, and production data to interpret subsurface heterogeneity and guide reservoir management decisions. The relationship between SHIRe distribution and geological characteristics, reservoir performance, and other relevant factors can be analyzed. SHIRe outputs can be interpreted in conjunction with any available data, such as well data analysis with vertical mapping, poro-perm maps, and production data characterizing reservoir heterogeneity, such as displacement efficiency, consistency, early water-cutting issues, known geology, and sedimentological core description reports.

[0114] Through the above processing, SHIRe effectively quantifies subsurface heterogeneity, providing geophysicists and reservoir engineers with a fast and powerful tool to assess seismic changes and improve reservoir management strategies.

[0115] Furthermore, the present invention provides an apparatus for identifying reservoir heterogeneity regions, as shown in FIG4. The apparatus for identifying reservoir heterogeneity regions includes:

[0116] Calculation module 404 is used to calculate statistical measures of each pre-selected seismic attribute in the reservoir seismic horizon;

[0117] The drawing module 405 is used to draw the reservoir based on the statistical measures of each seismic attribute to identify reservoir heterogeneity regions.

[0118] Preferably, the device further includes:

[0119] Input module 401 is used to input seismic data;

[0120] Preprocessing module 402 is used to preprocess seismic data;

[0121] Select module 403 to obtain seismic attributes from preprocessed seismic data and select seismic attributes for calculating statistical measures.

[0122] The calculation module is also used to calculate statistical measures of the seismic attributes using a three-dimensional rolling window method.

[0123] Preferably, the statistical measure is the standard deviation.

[0124] Preferably, the calculation module is further configured to calculate the standard deviation of each seismic attribute in the three-dimensional scrolling window at the seismic horizon; calculate the weight of each seismic attribute in the entire seismic dataset; and normalize the standard deviation of each seismic attribute in the three-dimensional scrolling window.

[0125] Preferably, the drawing module is further configured to calculate the volume heterogeneity index based on the standard deviation of each seismic attribute, and draw the reservoir based on the volume heterogeneity index.

[0126] Preferably, the drawing module is also used to draw a spatial non-uniformity map and a three-dimensional model based on the reservoir's volume non-homogeneity index.

[0127] The device for identifying heterogeneous regions of a reservoir includes a processor and a memory. The aforementioned calculation module, drawing module, input module, preprocessing module, and selection module are all stored in the memory as program units. The processor executes the aforementioned program units stored in the memory to achieve the corresponding functions.

[0128] The processor contains a kernel that retrieves corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters addresses limitations of existing technologies while providing innovative capabilities for reservoir characterization. By integrating multiple seismic attributes, this invention provides a comprehensive understanding of heterogeneity, capturing subtle differences missed by single-attribute analyses. Scrolling window analysis improves spatial resolution, enabling detailed understanding of local variations.

[0129] The computational efficiency of this invention supports real-time data processing, making it highly scalable and suitable for large datasets. The visualization tools of this invention simplify complex analyses, presenting results in an intuitive format to facilitate decision-making. Furthermore, the system's adaptability to various reservoir types ensures its broad applicability across diverse geological contexts. These advantages reduce operational risks, optimize resource extraction, and support data-driven reservoir management strategies.

[0130] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0131] This invention provides a storage medium storing a program that, when executed by a processor, implements the identification method.

[0132] This invention provides a processor for running a program, wherein the program executes the identification method during runtime.

[0133] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0134] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0135] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0136] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0137] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0138] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0139] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0140] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0141] The above are merely embodiments of this application and are not intended to limit the scope of 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 scope of the claims of this application.

Claims

1. A method for identifying reservoir heterogeneity regions, characterized in that, The method includes: Calculate statistical measures of each pre-selected seismic attribute in the reservoir seismic horizon; Reservoirs are plotted based on statistical measures of the aforementioned seismic attributes to identify regions of reservoir heterogeneity.

2. The identification method according to claim 1, characterized in that, Before calculating the statistical measures of each seismic attribute, the method also includes: Input seismic data and preprocess the seismic data; Seismic attributes are obtained from the preprocessed seismic data, and seismic attributes are selected for calculating statistical metrics.

3. The identification method according to claim 1, characterized in that, Calculating the statistical measures of each pre-selected seismic attribute in the reservoir seismic horizon includes: calculating the statistical measures of each seismic attribute using a three-dimensional rolling window method.

4. The identification method according to any one of claims 1-3, characterized in that, The statistical measure is the standard deviation.

5. The identification method according to claim 4, characterized in that, The standard deviation of each seismic attribute was calculated using the three-dimensional rolling window method, including: Calculate the standard deviation of each seismic attribute in the three-dimensional scrolling window at the seismic horizon; Calculate the weights of each seismic attribute across the entire seismic dataset; The standard deviation of each seismic attribute is normalized in the three-dimensional scrolling window.

6. The identification method according to claim 4, characterized in that, The reservoir is plotted based on statistical measures of the aforementioned seismic attributes, including: The volume heterogeneity index is calculated based on the standard deviation of each seismic attribute, and the reservoir is plotted based on the volume heterogeneity index.

7. The identification method according to claim 6, characterized in that, Drawing reservoirs based on the volume heterogeneity index includes: drawing spatial heterogeneity maps and three-dimensional models based on the volume heterogeneity index of the reservoir.

8. A device for identifying reservoir heterogeneity regions, characterized in that, The device includes: The calculation module is used to calculate the statistical measures of each pre-selected seismic attribute in the reservoir seismic horizon; The mapping module is used to map the reservoir based on the statistical measures of the seismic attributes to identify reservoir heterogeneity regions.

9. The identification device according to claim 8, characterized in that, The device also includes: The input module is used to input seismic data; The preprocessing module is used to preprocess seismic data; The selection module is used to obtain seismic attributes from preprocessed seismic data and select the seismic attributes used to calculate statistical measures.

10. The identification device according to claim 8, characterized in that, The calculation module is also used to calculate statistical measures of the seismic attributes using a three-dimensional rolling window method.

11. The identification device according to any one of claims 8-10, characterized in that, The statistical measure is the standard deviation.

12. The identification device according to claim 11, characterized in that, The calculation module is also used to calculate the standard deviation of each seismic attribute in the three-dimensional scrolling window at the seismic horizon; calculate the weight of each seismic attribute in the entire seismic dataset; and normalize the standard deviation of each seismic attribute in the three-dimensional scrolling window.

13. The identification device according to claim 11, characterized in that, The drawing module is also used to calculate the volume heterogeneity index based on the standard deviation of each seismic attribute, and to draw the reservoir based on the volume heterogeneity index.

14. The identification device according to claim 13, characterized in that, The mapping module is also used to generate spatial non-uniformity maps and 3D models based on the reservoir's volume non-homogeneity index.

15. A machine-readable storage medium having instructions stored thereon for causing a machine to perform the identification method according to any one of claims 1-7.

16. A processor, characterized in that, Used to run a program, wherein the program is run to perform the identification method according to any one of claims 1-7.

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