Deep shale dessert area identification method and device

Abstract

The invention discloses a deep shale dessert area identification method and device. The method comprises the following steps: acquiring three-dimensional seismic data and logging data of a deep shale reservoir; inputting the logging data into a pre-trained fitting model, and outputting a transverse wave time difference curve of the deep shale reservoir; determining an optimal rotation angle by using a multi-well Chi projection angle scanning technology according to the three-dimensional seismic data, the logging data and the shear wave time difference curve; determining an extended elastic impedance inversion curve corresponding to the reservoir parameter curve at the optimal rotation angle; the reservoir parameter curve is two-dimensional data of reservoir parameters; performing inversion on the extended elastic impedance inversion curve corresponding to the reservoir parameter curve to obtain a reservoir parameter body; the reservoir parameter body is three-dimensional data of reservoir parameters; according to the distribution characteristics of the reservoir parameter body, the sweet spot area is identified, the identification accuracy of the sweet spot area in the deep shale reservoir can be improved, and a favorable technical support is provided for reservoir evaluation of deep shale and horizontal well design.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method and apparatus for identifying sweet spots in deep shale formations. Background Technology

[0002] This section is intended to provide background or context for embodiments of the present invention. The description herein is not intended to imply that it is prior art simply because it is included in this section.

[0003] Deep shale gas reservoirs, due to their deep burial depth, poor physical properties, and complex geological structure, make it difficult for traditional exploration techniques to accurately predict high-potential "sweet spots." The main reason is that as burial depth increases, seismic signals gradually attenuate, and background noise increases, leading to a decrease in the signal-to-noise ratio of seismic data, which affects the accuracy and reliability of inversion. Summary of the Invention

[0004] This invention provides a method for identifying sweet spots in deep shale formations, thereby improving the accuracy of sweet spot identification in deep shale reservoirs and providing favorable technical support for reservoir evaluation and horizontal well design in deep shale. The method includes:

[0005] Acquire 3D seismic and well logging data of deep shale reservoirs;

[0006] The logging data is input into a pre-trained fitting model, which outputs the shear wave transit curve of the deep shale reservoir. The fitting model is obtained by training a deep learning model using logging data from known wells and the corresponding shear wave transit curves.

[0007] The optimal rotation angle was determined using multi-well Chi projection angle scanning technology based on 3D seismic data, well logging data, and shear wave time difference curves.

[0008] The extended elastic impedance inversion curve corresponding to the reservoir parameter curve at the optimal rotation angle is determined; the reservoir parameter curve is determined by well logging data; the reservoir parameter curve is two-dimensional data of the reservoir parameters.

[0009] The extended elastic impedance inversion curve corresponding to the reservoir parameter curve is inverted to obtain the reservoir parameter volume; the reservoir parameter volume is the three-dimensional data of the reservoir parameters.

[0010] Based on the distribution characteristics of reservoir parameters, sweet spots are identified.

[0011] This invention also provides a device for identifying sweet spots in deep shale formations, which improves the accuracy of sweet spot identification in deep shale reservoirs and provides favorable technical support for reservoir evaluation and horizontal well design in deep shale. The device includes:

[0012] The acquisition module is used to acquire 3D seismic data and well logging data of deep shale reservoirs;

[0013] The output module is used to input logging data into a pre-trained fitting model and output the shear wave transit curve of the deep shale reservoir. The fitting model is obtained by training a deep learning model using logging data from known wells and the corresponding shear wave transit curves.

[0014] The optimal rotation angle determination module is used to determine the optimal rotation angle based on 3D seismic data, well logging data, and shear wave time difference curves using multi-well Chi projection angle scanning technology.

[0015] The extended elastic impedance inversion curve determination module is used to determine the extended elastic impedance inversion curve corresponding to the reservoir parameter curve at the optimal rotation angle; the reservoir parameter curve is determined by well logging data; the reservoir parameter curve is two-dimensional data of the reservoir parameters.

[0016] The inversion module is used to invert the extended elastic impedance inversion curve corresponding to the reservoir parameter curve to obtain the reservoir parameter volume; the reservoir parameter volume is three-dimensional data of the reservoir parameters.

[0017] The sweet spot identification module is used to identify sweet spots based on the distribution characteristics of reservoir parameters.

[0018] Compared with traditional exploration techniques in the prior art, this invention acquires 3D seismic and well logging data of deep shale reservoirs; inputs the well logging data into a pre-trained fitting model to output the shear wave transit curve of the deep shale reservoir; the fitting model is obtained by training a deep learning model using well logging data and corresponding shear wave transit curves from known wells; using multi-well Chi projection angle scanning technology, the optimal rotation angle is determined based on the 3D seismic data, well logging data, and shear wave transit curve; the extended elastic impedance inversion curve corresponding to the reservoir parameter curve at the optimal rotation angle is determined; the reservoir parameter curve is determined by the well logging data; the reservoir parameter curve is two-dimensional data of the reservoir parameters; the extended elastic impedance inversion curve corresponding to the reservoir parameter curve is inverted to obtain the reservoir parameter volume; the reservoir parameter volume is three-dimensional data of the reservoir parameters; based on the distribution characteristics of the reservoir parameter volume, sweet spots are identified, which can improve the accuracy of sweet spot identification in deep shale reservoirs and provide favorable technical support for deep shale reservoir evaluation and horizontal well design. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0020] Figure 1 This is a flowchart of a method for identifying sweet spots in deep shale provided in an embodiment of the present invention;

[0021] Figure 2 A flowchart illustrating a specific example of a method for identifying sweet spots in deep shale provided in this embodiment of the invention;

[0022] Figure 3 A flowchart illustrating a specific example of a method for identifying sweet spots in deep shale provided in this embodiment of the invention;

[0023] Figure 4 This is a schematic diagram of three-dimensional seismic data before and after gather flattening provided in an embodiment of the present invention;

[0024] Figure 5 This is a schematic diagram of three-dimensional seismic data before and after overlay pre-frequency processing provided in an embodiment of the present invention;

[0025] Figure 6 This is a schematic diagram of three-dimensional seismic data before and after noise suppression processing provided in an embodiment of the present invention;

[0026] Figure 7 This is a schematic diagram of three-dimensional seismic data before and after residual energy compensation processing provided in an embodiment of the present invention.

[0027] Figure 8 This is a flowchart of the calculation of shear wave time difference curve based on deep learning algorithm provided in an embodiment of the present invention;

[0028] Figure 9 This is a schematic diagram of the preferred rotation angle of a single well provided in an embodiment of the present invention;

[0029] Figure 10 This is a schematic diagram of the extended elastic impedance inversion curves corresponding to the multiple reservoir parameter curves provided in the embodiments of the present invention.

[0030] Figure 11 This is a well-connected profile diagram of the inversion results provided in this embodiment of the invention;

[0031] Figure 12 This is the inverted TOC plane distribution diagram provided in this embodiment of the invention;

[0032] Figure 13 This is the inverted POR plane distribution diagram provided in this embodiment of the invention;

[0033] Figure 14 This is the inverted HCFP planar distribution diagram provided in this embodiment of the invention;

[0034] Figure 15 This is the inverted BRIT plane distribution diagram provided in this embodiment of the invention;

[0035] Figure 16 This is a plan view of the dessert area provided in an embodiment of the present invention;

[0036] Figure 17 This is a schematic diagram of a deep shale sweet spot identification device provided in an embodiment of the present invention;

[0037] Figure 18 This is a schematic diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0039] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0040] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0041] In the description of this specification, the terms "comprising," "including," "having," and "containing" are open-ended terms, meaning that they include but are not limited to. The terms "an embodiment," "a specific embodiment," "some embodiments," and "for example," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. The order of steps involved in the various embodiments is used to illustrate the implementation of this application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0042] Extended elastic impedance spectroscopy (EEI) is an effective method developed from traditional AVO (Amplitude Variation with Offset) analysis. It predicts shale gas sweet spots by fitting rock elastic parameters and reservoir properties. This invention proposes a method for identifying deep shale sweet spots based on EEI inversion technology. By acquiring and preprocessing 3D seismic and well logging data from deep shale reservoirs, deep learning algorithms are used to estimate shear wave velocities. Combined with fractal Gaussian models and Kriging interpolation methods, reservoir parameters are inverted to identify and evaluate sweet spots, further improving the development efficiency and economic benefits of shale gas fields.

[0043] Figure 1 This is a flowchart of a method for identifying sweet spots in deep shale provided in an embodiment of the present invention, such as... Figure 1 As shown, the method may include:

[0044] Step 101: Obtain 3D seismic data and well logging data of deep shale reservoirs;

[0045] Step 102: Input the logging data into the pre-trained fitting model and output the shear wave transit curve of the deep shale reservoir; the fitting model is obtained by training a deep learning model using the logging data of known wells and the corresponding shear wave transit curves.

[0046] Step 103: Using multi-well Chi projection angle scanning technology, determine the optimal rotation angle based on 3D seismic data, well logging data, and shear wave time difference curves;

[0047] Step 104: Determine the extended elastic impedance inversion curve corresponding to the reservoir parameter curve at the optimal rotation angle; the reservoir parameter curve is determined by well logging data; the reservoir parameter curve is two-dimensional data of the reservoir parameters.

[0048] Step 105: Invert the extended elastic impedance inversion curve corresponding to the reservoir parameter curve to obtain the reservoir parameter volume; the reservoir parameter volume is the three-dimensional data of the reservoir parameters.

[0049] Step 106: Identify sweet spots based on the distribution characteristics of reservoir parameters.

[0050] Figure 2 A flowchart illustrating a specific example of a method for identifying sweet spots in deep shale provided in this embodiment of the invention is shown below. Figure 2As shown, the method for identifying sweet spots in deep shale formations can specifically include: S1, data acquisition, including acquiring pre-stack (3D) seismic (data) and well logging data; S2, data preprocessing, including gather flattening, pre-stack frequency modulation, noise suppression, and energy compensation for the 3D seismic data; S3, estimating shear wave transit time using deep learning algorithms; S4, extended elastic impedance inversion, including determining the rotation angle, constructing the initial model, and determining reservoir parameters; S5, analyzing the inversion results and identifying sweet spots; S6, outputting the identification results.

[0051] The method provided in this invention effectively improves the accuracy and reliability of identifying sweet spots in deep shale formations, providing technical support for deep shale reservoir evaluation and horizontal well design. Due to its high applicability and flexibility, the method of this invention has broad application prospects in the field of shale gas field exploration and development.

[0052] Figure 3 A flowchart illustrating a specific example of a method for identifying sweet spots in deep shale provided in this embodiment of the invention is shown below. Figure 3 As shown, the pre-stack gather data and well logging data required for identifying sweet spots in deep shale gas can first be obtained through seismic and well logging data. Specifically, this includes acquiring 3D seismic data covering the target reservoir and well logging data from multiple wells, including P-wave transit time and density curves. The 3D seismic data is preprocessed to obtain seismic projection data; shear wave transit time curves are obtained using deep learning algorithms; rock physics analysis is performed on the deep shale reservoir to determine reservoir sensitive parameters and the optimal rotation angle; the extended elastic impedance inversion (EEI) curve corresponding to the reservoir (sensitive) parameter curve at the optimal rotation angle is determined; after calibration and wavelet estimation of the seismic projection data, it is combined with the EEI curve corresponding to the reservoir parameter curve to obtain an initial model based on fractal theory. This initial model, combined with seismic interpretation horizons, is used to invert the extended elastic impedance inversion curve corresponding to the reservoir parameter curve, obtaining the reservoir parameter volume; based on the distribution characteristics of the reservoir parameter volume, sweet spots are identified.

[0053] To ensure the accuracy of extended elastic impedance inversion, meticulous preprocessing and enhancement of pre-stack seismic gather data are required. In one embodiment, after acquiring 3D seismic data and well logging data of deep shale reservoirs, the deep shale sweet spot identification method may further include: optimizing the 3D seismic data; the optimization process includes one or any combination of gather flattening, pre-stack pre-frequency conversion, noise suppression, and residual energy compensation.

[0054] To ensure the reliability of the inversion results, pre-stack gather data needs to be optimized before inversion. Adhering to the principles of fidelity and amplitude preservation, processing steps such as gather flattening, pre-stack frequency pre-frequency modulation, noise suppression, and residual energy compensation were implemented. Through this optimization process, the quality of the pre-stack gather data was ensured, providing a reliable data foundation for extended elastic impedance inversion. This not only helps improve the accuracy of the inversion results but also significantly enhances the identification accuracy of deep shale reservoirs. The optimization of pre-stack gather data through seismic data preprocessing specifically includes:

[0055] (1) Gathering flattening: Gathering flattening is a key step in ensuring the quality of seismic data and improving the accuracy of inversion results. It can effectively eliminate the influence of surface undulations and differences in shot-receiver distances. The three-dimensional velocity field and anisotropic field can be obtained through dynamic correction (NMO) analysis, and the long-range unevenness problem can be solved by well-controlled anisotropic depth migration. The specific steps are as follows: First, the three-dimensional velocity field and anisotropic field are obtained through dynamic correction analysis, and the anisotropic parameters are calculated. At the same time, combined with the well logging velocity information, the anisotropic function is estimated as the control parameter for anisotropic simulation. Then, the problem of stretching distortion caused by long-range unevenness is solved by well-controlled anisotropic depth migration. Figure 4 This is a schematic diagram of three-dimensional seismic data before and after gather flattening provided in an embodiment of the present invention, as shown below. Figure 4 As shown, lline is the main survey line and xline is the connecting survey line. From the dynamic correction gather profiles before and after processing, it can be seen that the phase axis of the far offset gather is significantly flattened.

[0056] (2) Pre-overlap frequency expansion: Harmonic deconvolution technique is used to improve the dominant frequency and bandwidth of seismic data. Harmonic deconvolution frequency expansion technique is used, and continuous wavelet transform time series is adopted. By utilizing the resolution characteristics of the wavelet domain, the harmonic and subharmonic information of the seismic wavelet is calculated according to the idea of ​​harmonic analysis and added to the original seismic record, which effectively improves the dominant frequency and bandwidth of seismic data. Figure 5 This is a schematic diagram of three-dimensional seismic data before and after pre-overlay frequency enhancement provided in an embodiment of the present invention, as shown below. Figure 5 As shown, Figure 5 (a) shows a comparison of gathers before and after pre-stack deconvolution and frequency upsetting. Figure 5 (b) shows a comparison of the spectra before and after pre-stack deconvolution. Harmonic deconvolution effectively improves the resolution of seismic data while maintaining wave group characteristics and without losing low frequencies, achieving the goal of amplitude preservation and broadband. The dominant frequency and bandwidth of the seismic data are improved after deconvolution. The low-frequency band of the pre-stack gather is broadened from 8Hz to 5Hz, and the high-frequency band is broadened from 66Hz to 71Hz. At the same time, the details of thin reservoirs between layers are also greatly improved.

[0057] (3) Noise Suppression: Pre-stack denoising can effectively improve the signal-to-noise ratio of seismic data, thereby significantly improving the quality of pre-stack gathers and providing high-quality basic data for subsequent processing. Pre-stack four-dimensional random noise attenuation technology is employed, treating the three-dimensional data as a four-domain data volume, namely, line number, common center point element, common offset domain, and recording time domain. Prediction operators for each frequency component are obtained and applied to the three-dimensional pre-stack gather data to attenuate random noise. Four-dimensional random noise attenuation predicts and suppresses noise from multiple dimensions, protecting effective information from damage while suppressing noise, improving the signal-to-noise ratio of pre-stack gathers, and thus enhancing data quality. Figure 6 This is a schematic diagram of three-dimensional seismic data before and after noise suppression processing provided in an embodiment of the present invention, such as... Figure 6 As shown, before denoising, the phase axis of the gather is not smooth, while after denoising, the phase axis of the gather is smooth and without jitter, the oblique interference is suppressed, and the signal-to-noise ratio of the data is greatly improved.

[0058] (4) Residual Energy Compensation: Pre-stack amplitude compensation aims to restore the original amplitude of the seismic waveform for more accurate analysis and interpretation of underground structures. Energy compensation technology based on AVO characteristics restores the original amplitude of the seismic waveform. The basic principle is that energy compensation based on AVO characteristics statistically analyzes the changes in offset amplitude energy within a certain time window, compares the AVO model trend with the actual AVO trend, calculates the amplitude factor that varies with offset, and finally applies the factor to the actual gather. Figure 7 This is a schematic diagram of three-dimensional seismic data before and after residual energy compensation processing provided in an embodiment of the present invention, as shown below. Figure 7 As shown, comparing the gather data before and after energy adjustment, this method significantly improves the problem of weak energy at near offsets, and can effectively improve the stability and accuracy of inversion.

[0059] Next, as Figure 3 As shown, shear wave velocity is fundamental data for rock mechanics parameter analysis, directly affecting the accuracy and reliability of pre-stack inversion results. However, shear wave logging data is typically acquired only in a limited number of wells, making accurate prediction of shear waves one of the challenges in pre-stack inversion research. Estimating shear wave velocity using deep learning algorithms can specifically involve: starting with logging data from known wells, using deep learning algorithms to fit the nonlinear relationship between the logging curve and the shear wave curve, calculating the shear wave transit time for unknown wells, and generating a fitted shear wave transit time curve for inversion.

[0060] Figure 8 This is a flowchart of the calculation of shear wave time difference curve based on deep learning algorithm provided in an embodiment of the present invention, such as... Figure 8 As shown, the calculation principle of the shear wave time difference curve is as follows:

[0061] Starting with known wells, a deep learning algorithm is used to fit the nonlinear relationship between existing well logging curves and shear wave transit time curves, and to obtain the shear wave transit time curves for unknown wells. Existing well logging curves and well logging curves for unknown wells can include sonic transit time (DT) curves, clay content (Vsh) curves, porosity curves, and water saturation (Sw) curves, etc.

[0062] In one embodiment, the method for identifying sweet spots in deep shale formations may further include: acquiring logging data and corresponding shear wave transit curves of known wells; and using a deep learning algorithm to train a deep learning model based on the logging data and corresponding shear wave transit curves of the known wells to obtain a fitted model.

[0063] This invention employs a deep learning method to estimate the shear wave transit time (SWT) curve. Starting with a known well, a deep learning algorithm is used to fit the nonlinear relationship between the existing well logging curve and the SWT curve, and then the SWT curve for an unknown well is obtained. The fitted SWT curve matches the original DTS curve well, with a correlation exceeding 95%. The fitted SWT curve can be used for further inversion work.

[0064] like Figure 3 As shown, reservoir sensitive parameters can be determined through rock physical analysis. The reservoir parameters obtained by the final inversion and those used to determine the sweet spot can all be reservoir sensitive parameters, which reflect the reservoir sensitivity.

[0065] In one embodiment, reservoir parameters may include total organic carbon content, porosity, effective hydrocarbon-bearing porosity, and brittleness index.

[0066] By extending the elastic impedance inversion, a reservoir parameter volume reflecting the reservoir sensitivity can be obtained, which may specifically include:

[0067] Figure 9 This is a schematic diagram of the preferred rotation angle of a single well provided in an embodiment of the present invention, such as... Figure 9 As shown, Corr.coef is the correlation coefficient. Through multi-well correlation analysis, the optimal rotation angle Chi of the reservoir sensitive parameter is determined.

[0068] In one embodiment, the logging data includes P-wave transit time curves and density curves. Using multi-well Chi projection angle scanning technology, the optimal rotation angle is determined based on 3D seismic data, logging data, and S-wave transit time curves. This may include: calculating the extended elastic impedance curves corresponding to the reservoir parameter curves at multiple rotation angles using multi-well Chi projection angle scanning technology, based on the P-wave transit time curves, S-wave transit time curves, and density curves; performing correlation analysis between the reservoir parameter curves and the extended elastic impedance curves corresponding to the reservoir parameter curves at multiple rotation angles to determine multiple correlation coefficients; and determining the rotation angle corresponding to the maximum value among the multiple correlation coefficients as the optimal rotation angle.

[0069] like Figure 9 As shown, the principle of extended elastic impedance inversion is as follows: Through multi-well Chi projection angle scanning analysis, based on the P-wave transit time curve, density (RHOB) curve, and shear wave transit time curve (DTS) obtained using deep learning algorithms from the well logging data, the EEI curves corresponding to the reservoir parameter curves at different rotation angles are calculated. The reservoir parameter curves include elastic parameter curves or physical property parameter curves. The obtained EEI curves are correlated with the elastic parameter curves or physical property parameter curves respectively. By calculating the correlation coefficient, the correlation between the EEI curves and reservoir parameters at different rotation angles can be evaluated to determine the optimal rotation angle, thereby determining the EEI curve corresponding to that angle, which is then used for subsequent time-depth relationship calibration. The EEI curves corresponding to the elastic parameters (Poisson's ratio (PR), Young's modulus (YM)) and physical property parameters (Total Organic Carbon content (TOC), Porosity (POR), Effective Hydrocarbon Porosity (HCFP), and Brittleness Index (BRIT)) are calculated using the above method.

[0070] Because incident waves at different angles elicit different responses from the formation, a single elastic impedance (EI) value is insufficient to comprehensively characterize formation properties. To address this issue, extended elastic impedance (EEI) was proposed, which optimizes the comparability of EI values ​​by introducing angular rotation. The core of EEI lies in rotating conventional wave impedance and gradient impedance in angular space to fit the elastic parameters of the rock. [6] Specifically, the calculation of EEI involves the reflection coefficient (R) of seismic waves at different incident angles, which can be expressed by the following formula:

[0071]

[0072] Where z1 and z2 are the acoustic impedances of the upper and lower parts of the stratum, respectively, and θ is the incident angle.

[0073] like Figure 3As shown, EEI seeks an optimal rotation angle X such that the EEI value at that angle best reflects the physical properties of the formation. This optimal angle can be determined by minimizing the fluctuation of the EEI value, i.e.:

[0074]

[0075] Among them, EI i At the incident angle θ i The elastic impedance, EI, is calculated below. mean It is the average value of EEI for all angles, and N is the total number of incident angles.

[0076] In this way, EEI can provide a more stable and comparable parameter for lithology and gas-bearing identification. In practical applications, EEI is often used in conjunction with other seismic and well logging data to improve the accuracy and reliability of reservoir prediction.

[0077] Figure 10 This is a schematic diagram of the extended elastic impedance inversion curves corresponding to multiple reservoir parameter curves provided in the embodiments of the present invention, such as... Figure 10 As shown, Poisson's ratio (PR) reflects the fracturing ability of a rock under external forces, while Young's modulus (YM) reflects the supporting capacity of the rock after fracturing. Generally, the higher the Young's modulus and the lower the Poisson's ratio, the more brittle the rock, and the easier it is to form complex fractures during fracturing. Using the optimized Chi projection angle, the extended elastic impedance curves corresponding to PR, YM, BRIT, TOC, POR, and HCFP for each well were estimated. The EEI curves from single wells show a negative correlation between the PR and HCFP curves, and a positive correlation between the other curves. Furthermore, the correlation between the parameter curves and their corresponding EEI curves is good. Multi-well correlation comparisons revealed that the correlation characteristics of each well are only slightly different; optimizing the rotation angle helps improve the fitting accuracy. Simultaneously, since different layers have different lithological and fluid characteristics, fitting the relationships between different layers significantly improves the fitting accuracy.

[0078] In one embodiment, inverting the extended elastic impedance inversion curve corresponding to the reservoir parameter curve to obtain the reservoir parameter volume may include: establishing an initial extended elastic impedance model based on three-dimensional seismic data and well logging data, using fractal theory and Kriging interpolation algorithm, and inverting the extended elastic impedance inversion curve corresponding to the reservoir parameter curve using the initial extended elastic impedance model as a constraint to obtain the reservoir parameter volume.

[0079] This invention employs a stochastic inversion method for reservoir physical parameters based on a fractal high-frequency initial model and low-frequency prior information. It effectively utilizes well logging data and seismic information to directly invert reservoir physical parameters within a Bayesian theoretical framework. An initial EEI model is constructed using a fractal Gaussian model and Kriging interpolation. Based on a statistical rock physics model, the relationship between elastic parameters and reservoir physical parameters is established, inverting the EEI data volume and reservoir parameter volume that reflect reservoir sensitivity. Fractal theory is a mathematical tool for describing complex geometric shapes and structures in nature. In geophysics, fractal theory can be used to simulate the pore structure of rocks and reservoir characteristics. The fractal high-frequency initial model uses fractal dimension to describe the pore distribution in the reservoir and the surface characteristics of the rock, thereby generating an initial model with fractal properties. Low-frequency prior information typically comes from seismic data, well logging data, and other geological data. This information provides macroscopic characteristics of reservoir physical parameters, such as porosity, permeability, and the elastic properties of the rock. During the inversion process, low-frequency prior information is used to constrain the generation of the high-frequency model, ensuring that the inversion results macroscopically conform to known geological conditions. The inversion of physical property parameters based on fractal high-frequency initial models and low-frequency prior information combines fractal Gaussian model algorithms, Kriging interpolation, SA-PSO optimization algorithms, and statistical rock physics theory within a Bayesian theoretical framework. Even with relatively low signal-to-noise ratios, reasonable reservoir physical property parameter information can still be inverted, thus providing a feasible method for the quantitative prediction of TOC, POR, HCFP, and BRIT.

[0080] Figure 11 This is a well-connected profile diagram of the inversion results provided in this embodiment of the invention. Figure 11 The figures in the middle are the TOC, POR, HCFP, and BRIT data volumes obtained from the inversion. Duvernay and BHLK are the names of the top and bottom surfaces of the target layer. Figure 11 It can be seen that the inversion results are in good agreement with the well data, with high resolution, and thin layers can also be identified well.

[0081] Figure 12 This is the inverted TOC plane distribution diagram provided in this embodiment of the invention. Figure 13 This is the inverted POR plane distribution diagram provided in this embodiment of the invention. Figure 14 This is the inverted HCFP planar distribution diagram provided in this embodiment of the invention. Figure 15 The inverted BRIT plane distribution diagram provided in this embodiment of the invention is as follows: Figures 12-15 As shown, the planar characteristics analysis of the inversion results show that TOC and POR are relatively high in the western, central and northeastern parts of the study area; high HCFP values ​​are mainly distributed in the southwest, central and northeastern parts; and high brittleness index BRIT values ​​are mainly distributed in the northwest, north-central and northeastern parts.

[0082] In one embodiment, identifying sweet spots based on the distribution characteristics of reservoir parameters may include: identifying areas where the reservoir thickness is greater than a first preset threshold, the total organic carbon content is not less than a second preset threshold, the porosity is not less than a third preset threshold, the effective hydrocarbon-containing porosity is not less than a fourth preset threshold, and the brittleness index is not less than a fifth preset threshold as sweet spots.

[0083] The sweet spot identification process may specifically include: identifying geological sweet spots and engineering sweet spots based on the distribution characteristics of total organic carbon content (TOC), porosity (POR), effective hydrocarbon-bearing porosity (HCFP), and brittleness index (BRIT) in the inversion results, providing a reference for reservoir evaluation and horizontal well design.

[0084] like Figures 11-15 As shown, the profile and planar distribution characteristics of total organic carbon (TOC), porosity (POR), effective hydrocarbon-bearing porosity (HCFP), and brittleness index (BRIT) in the inversion results are in good agreement with the well data and meet the inversion accuracy requirements.

[0085] The specific steps for outputting the identification results may include: outputting the sweet spot range and extracting reservoir parameters within the range, including but not limited to the mean, variance, and probability density distribution of total organic carbon content (TOC), porosity (POR), effective hydrocarbon porosity (HCFP), and brittleness index (BRIT).

[0086] Figure 16 This is a planar distribution diagram of the dessert area provided in an embodiment of the present invention, such as... Figure 16 As shown, starting from the TOC and POR curves in the well, areas with TOC ≥ 2.5 and POR ≥ 5, and a reservoir thickness greater than 20 meters, can be considered geological sweet spots. Simultaneously, referencing HCFP ≥ 5, brittleness index BRIT ≥ 35, and a reservoir thickness greater than 20 meters, the planar distribution range of shale reservoir sweet spots is finally delineated. Analysis of longitudinal and transverse profiles and planar views shows that engineering sweet spots are basically included within the geological sweet spots. Therefore, well placement design should primarily focus on geological sweet spots, while the horizontal well section should, as far as possible, remain within a stable and well-developed high-HCFP layer.

[0087] This invention successfully applied extended elastic impedance (EEI) inversion technology to accurately predict key parameters of deep shale reservoirs. By comprehensively analyzing rock physical response characteristics and pre-stack seismic data, the distribution characteristics of total organic carbon (TOC), porosity (POR), effective hydrocarbon saturation (HCFP), and brittleness index (BRIT) were revealed. Accurate prediction of these parameters is crucial for identifying and assessing shale gas "sweet spots."

[0088] The research results show that EEI technology not only improves the accuracy of identifying deep shale "sweet spots" but also enhances the predictive ability of shale reservoir distribution. Compared with traditional methods, EEI technology demonstrates significant advantages in handling complex geological structures and improving seismic resolution. Furthermore, the application of this technology reduces the uncertainty in interpreting deep shale "sweet spots," providing a solid geological and geophysical basis for oil and gas exploration and development decisions. With the continuous advancement of seismic exploration technology, the application prospects of EEI technology are further broadened. In future deep shale reservoir characterization, oil and gas prediction, and oil and gas reservoir dynamic monitoring, EEI technology is expected to play a greater role, demonstrating its unique advantages and enormous application potential. Moreover, further development and improvement of this technology will help promote the effective development of unconventional oil and gas resources, contributing to energy security and sustainable development.

[0089] This invention also proposes a device for identifying deep shale sweet spot areas, the principle of which is similar to the method for identifying deep shale sweet spot areas, and will not be described in detail here.

[0090] Figure 17 This is a schematic diagram of a deep shale sweet spot identification device provided in an embodiment of the present invention, such as... Figure 17 As shown, the deep shale sweet spot identification device may include:

[0091] Module 1701 is used to acquire 3D seismic data and well logging data of deep shale reservoirs;

[0092] The output module 1702 is used to input logging data into a pre-trained fitting model and output the shear wave transit curve of the deep shale reservoir; the fitting model is obtained by training a deep learning model using logging data from known wells and the corresponding shear wave transit curves.

[0093] The optimal rotation angle determination module 1703 is used to determine the optimal rotation angle based on three-dimensional seismic data, well logging data and shear wave time difference curves using multi-well Chi projection angle scanning technology.

[0094] The extended elastic impedance inversion curve determination module 1704 is used to determine the extended elastic impedance inversion curve corresponding to the reservoir parameter curve at the optimal rotation angle; the reservoir parameter curve is determined by well logging data; the reservoir parameter curve is two-dimensional data of the reservoir parameters.

[0095] The inversion module 1705 is used to invert the extended elastic impedance inversion curve corresponding to the reservoir parameter curve to obtain the reservoir parameter volume; the reservoir parameter volume is three-dimensional data of the reservoir parameters.

[0096] The sweet spot identification module 1706 is used to identify sweet spots based on the distribution characteristics of reservoir parameter volumes.

[0097] In one embodiment, the deep shale sweet spot identification device may further include: a training module for:

[0098] Obtain logging data and corresponding shear wave time-of-flight curves for known wells;

[0099] Using deep learning algorithms, the deep learning model is trained based on the well logging data of known wells and the corresponding shear wave time difference curves to obtain a fitted model.

[0100] In one embodiment, the logging data includes P-wave transit time curves and density curves;

[0101] The optimal rotation angle determination module 1703 is specifically used for:

[0102] Based on the P-wave transit time curve, S-wave transit time curve, and density curve, the extended elastic impedance curves corresponding to the reservoir parameter curves at multiple rotation angles are calculated using the multi-well Chi projection angle scanning technology.

[0103] Correlation analysis was performed between the reservoir parameter curves and the extended elastic impedance curves corresponding to the reservoir parameter curves at multiple rotation angles to determine multiple correlation coefficients.

[0104] The rotation angle corresponding to the maximum value among multiple correlation coefficients is determined as the optimal rotation angle.

[0105] In one embodiment, the inversion module 1705 is specifically used for:

[0106] Based on 3D seismic data and well logging data, an initial extended elastic impedance model is established using fractal theory and Kriging interpolation algorithm. Using the initial extended elastic impedance model as a constraint, the extended elastic impedance inversion curve corresponding to the reservoir parameter curve is inverted to obtain the reservoir parameter volume.

[0107] In one embodiment, the deep shale sweet spot identification device may further include: an optimization module for:

[0108] The three-dimensional seismic data is optimized; the optimization process includes one or any combination of gather flattening, overlay pre-frequency modulation, noise suppression, and residual energy compensation.

[0109] In one embodiment, reservoir parameters include total organic carbon content, porosity, effective hydrocarbon-bearing porosity, and brittleness index.

[0110] In one embodiment, the dessert area recognition module 1706 is specifically used for:

[0111] Regions with reservoir thickness greater than the first preset threshold, total organic carbon content not less than the second preset threshold, porosity not less than the third preset threshold, effective hydrocarbon-bearing porosity not less than the fourth preset threshold, and brittleness index not less than the fifth preset threshold are identified as sweet spots.

[0112] Compared with traditional exploration techniques in the prior art, this invention acquires 3D seismic and well logging data of deep shale reservoirs; inputs the well logging data into a pre-trained fitting model to output the shear wave transit curve of the deep shale reservoir; the fitting model is obtained by training a deep learning model using well logging data and corresponding shear wave transit curves from known wells; using multi-well Chi projection angle scanning technology, the optimal rotation angle is determined based on the 3D seismic data, well logging data, and shear wave transit curve; the extended elastic impedance inversion curve corresponding to the reservoir parameter curve at the optimal rotation angle is determined; the reservoir parameter curve is determined by the well logging data; the reservoir parameter curve is two-dimensional data of the reservoir parameters; the extended elastic impedance inversion curve corresponding to the reservoir parameter curve is inverted to obtain the reservoir parameter volume; the reservoir parameter volume is three-dimensional data of the reservoir parameters; based on the distribution characteristics of the reservoir parameter volume, sweet spots are identified, which can improve the accuracy of sweet spot identification in deep shale reservoirs and provide favorable technical support for deep shale reservoir evaluation and horizontal well design.

[0113] This invention, by introducing extended elastic impedance inversion technology and deep learning algorithms, effectively improves the accuracy and reliability of identifying sweet spots in deep shale formations, providing crucial technical support for deep shale reservoir evaluation and horizontal well design. Compared to traditional methods, this invention demonstrates significant advantages in handling complex geological structures and improving seismic resolution.

[0114] This invention, by combining with artificial intelligence methods, provides more comprehensive and accurate data analysis capabilities than traditional shale sweet spot identification, and performs particularly well in handling complex geological environments and refined oil and gas exploration.

[0115] This invention also provides a computer device. Figure 18 This is a schematic diagram of a computer device in an embodiment of the present invention. The computer device 1800 includes a memory 1810, a processor 1820, and a computer program 1830 stored in the memory 1810 and executable on the processor 1820. When the processor 1820 executes the computer program 1830, it implements the above-mentioned method for identifying deep shale sweet spots.

[0116] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for identifying deep shale sweet spot areas.

[0117] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for identifying deep shale sweet spot areas.

[0118] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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.

[0119] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] 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, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying sweet spots in deep shale formations, characterized in that, include: Acquire 3D seismic and well logging data of deep shale reservoirs; The well logging data is input into a pre-trained fitting model, and the shear wave time difference curve of the deep shale reservoir is output. The fitting model is obtained by training a deep learning model using well logging data from known wells and the corresponding shear wave time difference curves. The optimal rotation angle was determined using multi-well Chi projection angle scanning technology based on 3D seismic data, well logging data, and shear wave time difference curves. The extended elastic impedance inversion curve corresponding to the reservoir parameter curve at the optimal rotation angle is determined; the reservoir parameter curve is determined by well logging data; the reservoir parameter curve is two-dimensional data of the reservoir parameters. The extended elastic impedance inversion curve corresponding to the reservoir parameter curve is inverted to obtain the reservoir parameter volume; the reservoir parameter volume is the three-dimensional data of the reservoir parameters. Based on the distribution characteristics of reservoir parameters, sweet spots are identified.

2. The method as described in claim 1, characterized in that, Also includes: Obtain logging data and corresponding shear wave time-of-flight curves for known wells; Using deep learning algorithms, the deep learning model is trained based on the well logging data of known wells and the corresponding shear wave time difference curves to obtain a fitted model.

3. The method as described in claim 1, characterized in that, The logging data includes P-wave time-of-flight curves and density curves; Using multi-well Chi projection angle scanning technology, the optimal rotation angle is determined based on 3D seismic data, well logging data, and shear wave time-of-flight curves, including: Based on the P-wave transit time curve, S-wave transit time curve, and density curve, the extended elastic impedance curves corresponding to the reservoir parameter curves at multiple rotation angles are calculated using the multi-well Chi projection angle scanning technology. Correlation analysis was performed between the reservoir parameter curves and the extended elastic impedance curves corresponding to the reservoir parameter curves at multiple rotation angles to determine multiple correlation coefficients. The rotation angle corresponding to the maximum value among multiple correlation coefficients is determined as the optimal rotation angle.

4. The method as described in claim 1, characterized in that, Inverting the extended elastic impedance inversion curve corresponding to the reservoir parameter curve yields the reservoir parameter volume, including: Based on 3D seismic data and well logging data, an initial extended elastic impedance model is established using fractal theory and Kriging interpolation algorithm. Using the initial extended elastic impedance model as a constraint, the extended elastic impedance inversion curve corresponding to the reservoir parameter curve is inverted to obtain the reservoir parameter volume.

5. The method as described in claim 1, characterized in that, After acquiring 3D seismic and well logging data of deep shale reservoirs, the following is also included: The three-dimensional seismic data is optimized; the optimization process includes one or any combination of gather flattening, overlay pre-frequency modulation, noise suppression, and residual energy compensation.

6. The method as described in claim 1, characterized in that, The reservoir parameters include total organic carbon content, porosity, effective hydrocarbon-bearing porosity, and brittleness index.

7. The method as described in claim 6, characterized in that, Based on the distribution characteristics of reservoir parameters, sweet spots are identified, including: Regions with reservoir thickness greater than the first preset threshold, total organic carbon content not less than the second preset threshold, porosity not less than the third preset threshold, effective hydrocarbon-bearing porosity not less than the fourth preset threshold, and brittleness index not less than the fifth preset threshold are identified as sweet spots.

8. A device for identifying sweet spots in deep shale formations, characterized in that, include: The acquisition module is used to acquire 3D seismic data and well logging data of deep shale reservoirs; The output module is used to input logging data into a pre-trained fitting model and output the shear wave time difference curve of deep shale reservoirs. The fitting model is obtained by training a deep learning model using well logging data from known wells and the corresponding shear wave time difference curves. The optimal rotation angle determination module is used to determine the optimal rotation angle based on 3D seismic data, well logging data, and shear wave time difference curves using multi-well Chi projection angle scanning technology. The extended elastic impedance inversion curve determination module is used to determine the extended elastic impedance inversion curve corresponding to the reservoir parameter curve at the optimal rotation angle; the reservoir parameter curve is determined by well logging data; the reservoir parameter curve is two-dimensional data of the reservoir parameters. The inversion module is used to invert the extended elastic impedance inversion curve corresponding to the reservoir parameter curve to obtain the reservoir parameter volume; the reservoir parameter volume is three-dimensional data of the reservoir parameters. The sweet spot identification module is used to identify sweet spots based on the distribution characteristics of reservoir parameters.

9. The apparatus as claimed in claim 8, characterized in that, Also includes: The training module is used for: Obtain logging data and corresponding shear wave time-of-flight curves for known wells; Using deep learning algorithms, the deep learning model is trained based on the well logging data of known wells and the corresponding shear wave time difference curves to obtain a fitted model.

10. The apparatus as claimed in claim 8, characterized in that, The logging data includes P-wave time-of-flight curves and density curves; The optimal rotation angle determination module is specifically used for: Based on the P-wave transit time curve, S-wave transit time curve, and density curve, the extended elastic impedance curves corresponding to the reservoir parameter curves at multiple rotation angles are calculated using the multi-well Chi projection angle scanning technology. Correlation analysis was performed between the reservoir parameter curves and the extended elastic impedance curves corresponding to the reservoir parameter curves at multiple rotation angles to determine multiple correlation coefficients. The rotation angle corresponding to the maximum value among multiple correlation coefficients is determined as the optimal rotation angle.

11. The apparatus as claimed in claim 8, characterized in that, The inversion module is specifically used for: Based on 3D seismic data and well logging data, an initial extended elastic impedance model is established using fractal theory and Kriging interpolation algorithm. Using the initial extended elastic impedance model as a constraint, the extended elastic impedance inversion curve corresponding to the reservoir parameter curve is inverted to obtain the reservoir parameter volume.

12. The apparatus as claimed in claim 8, characterized in that, Also includes: The optimization module is used for: The three-dimensional seismic data is optimized; the optimization process includes one or any combination of gather flattening, overlay pre-frequency modulation, noise suppression, and residual energy compensation.

13. The apparatus as claimed in claim 8, characterized in that, The reservoir parameters include total organic carbon content, porosity, effective hydrocarbon-bearing porosity, and brittleness index.

14. The apparatus as claimed in claim 13, characterized in that, The dessert area recognition module is specifically used for: Regions with reservoir thickness greater than the first preset threshold, total organic carbon content not less than the second preset threshold, porosity not less than the third preset threshold, effective hydrocarbon-bearing porosity not less than the fourth preset threshold, and brittleness index not less than the fifth preset threshold are identified as sweet spots.

15. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

17. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

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