Shale gas reservoir sweet spot prediction method

Abstract

The invention relates to the technical field of shale gas reservoir exploration and development, and discloses a shale gas reservoir sweet spot prediction method, which comprises the following steps: S1, collecting well data including well layering data and a logging curve; collecting three-dimensional seismic data; collecting speed body data; s2, according to the three-dimensional seismic data, gather optimization processing is carried out on pre-stack CRP gather data, interval velocity conversion processing is carried out on a root mean square velocity body, high-frequency attenuation gradient attribute processing is carried out on pre-stack offset pure wave seismic data, construction smoothing processing is carried out on post-stack result data, and maximum curvature attribute and ant body attribute extraction is carried out; combining well data to obtain prediction parameters; obtaining plane development characteristics and distribution rules of the shale gas reservoir according to the prediction parameters, and grading and zoning the shale gas reservoir; and S3, shale reservoir sweet spot prediction is carried out on the basis of grading and zoning of each sweet spot prediction parameter. Compared with the prior art, the shale reservoir section sweet spot prediction precision can be further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of shale gas reservoir exploration and development, and in particular to a method for predicting sweet spots in shale gas reservoirs. Background Art

[0002] In the process of oil exploration, reservoir "sweet spots" prediction has always been one of the most important technical links in oil and gas exploration and development. At present, although my country's shale gas exploration and development has entered the stage of industrial development, the accurate identification of shale reservoir sweet spots still faces many challenges. For example: (1) Shale reservoirs are thin and highly heterogeneous, with developed fault systems; (2) The resolution of seismic waves is limited, making it difficult to directly identify these thin layer structures, resulting in poor imaging of the reservoir in seismic data, and many key information is difficult to effectively extract. In addition, in the process of seismic reservoir prediction, due to the complexity of geological conditions and the mutual influence of multiple factors, it is easy to cause multiple solutions, which further increases the difficulty of identifying shale reservoir sweet spots.

[0003] Therefore, how to quickly and accurately identify the "sweet spots" of shale reservoirs, reduce the multi-solution potential in the sweet spot identification process, achieve precise well placement, and thereby increase reservoir drilling rates and oil and gas production has become one of the core tasks in the current oil and gas exploration and development field.

[0004] The existing Chinese patent with publication number CN116009096A provides a shale gas sweet spot prediction method and equipment by multi-parameter fusion inversion. The invention discloses a shale gas sweet spot prediction method and equipment by multi-parameter fusion inversion. The method includes: based on well logging data, the target layer is divided into small layers vertically to obtain multiple small layers; based on seismic data, the predicted reservoir segment boundary is obtained in multiple small layers; based on the well logging data of the predicted reservoir segment boundary, multiple well logging attribute parameters are intersected to obtain multi-parameter fitting formulas for carbon content, gas content and porosity respectively; the pre-stack parameter inversion results are applied, combined with the multi-parameter fitting formula, to obtain the profile results and plane results of carbon content, gas content and porosity; the profile results and plane results of carbon content, gas content and porosity are combined to predict shale gas sweet spots. The invention weakens the influence of the instability of a single parameter and overcomes the instability of the single parameter result caused by the inversion results of parameters such as density.

[0005] This prior art uses pre-stack parameter inversion to obtain data such as Poisson's ratio, longitudinal and transverse wave velocity ratio, density, Lame coefficient, impedance, etc., and uses a multi-parameter regression method to calculate TOC, porosity, and gas content, and predicts the shale reservoir sweet spot based on the planar distribution characteristics of these parameters. However, this method does not take into account geological factors, such as the structural characteristics of the current reservoir strata, the paleo-geomorphological characteristics of the reservoir section during deposition, and the current reservoir section fracture distribution characteristics. In addition, the parameters used in the sweet spot prediction process are relatively small, such as water saturation and high-quality shale (TOC ≥ 2%) thickness. The lack of coupling of these prediction parameters will form a systematic deviation in the sweet spot prediction process, ultimately leading to inaccurate prediction results. Summary of the invention

[0006] In order to overcome or alleviate one or more of the above technical problems, the present invention aims to provide a shale gas reservoir sweet spot prediction method.

[0007] The present invention provides the following technical solutions:

[0008] A method for predicting sweet spots in shale gas reservoirs comprises the following steps:

[0009] S1: Well data collection, including well layer data and logging curves, the logging curves include acoustic time difference curve DTC, shear wave time difference curve DTS, volume density curve DEN, total organic carbon content curve TOC and porosity curve POR; 3D seismic data collection, including pre-stack CRP gather data, pre-stack migration pure wave seismic data and post-stack result data; velocity body data collection, including root mean square velocity body;

[0010] S2: Based on the three-dimensional seismic data, by performing gather optimization processing on the pre-stack CRP gather data, performing layer velocity conversion processing on the root mean square velocity body, performing high-frequency attenuation gradient attribute processing on the pre-stack migration pure wave seismic data, performing structural smoothing processing and extracting maximum curvature attributes and ant body attributes on the post-stack result data, and combining the well data, obtaining prediction parameters, the prediction parameters at least include the following parameters: paleo-geomorphology, micro-scale fractures, high-quality shale thickness, TOC, gas content, porosity, brittleness index and water saturation; according to the prediction parameters, the plane development characteristics and distribution law of shale gas reservoirs are obtained and classified and zoned;

[0011] S3: Predict shale reservoir sweet spots based on the classification and zoning of various sweet spot prediction parameters.

[0012] According to some embodiments, step S2 comprises the following steps:

[0013] S2-1: Based on the post-stack data, the top interface T of the shale gas reservoir in the study area is determined by well seismic calibration 顶 and bottom interface T底 and isochronous deposition interface T 等 And conduct layer tracking in the whole area;

[0014] S2-2: Using the post-stack data to detect reservoir fracture development characteristics, firstly, the post-stack data is subjected to structural smoothing to suppress random noise of seismic data, and the maximum curvature attribute is extracted from the data body after structural smoothing to highlight the discontinuity of seismic event axes and improve the resolution of fracture identification, and then ant body attribute calculation is performed to identify the development characteristics of micro-fractures;

[0015] S2-3: Based on the isochronous deposition interface T obtained in step S2-1 等 and T 底 , the paleo-geomorphology of the reservoir section in the study area at the early stage of deposition was restored by using the impression method;

[0016] S2-4: suppressing the random noise and linear interference of the pre-stack CRP gather data by a prediction and denoising method, and then performing gather flattening processing on the pre-stack CRP gather data after prediction and denoising to eliminate the residual time difference;

[0017] S2-5: Use the Dix formula to convert the root mean square velocity volume in the velocity volume data into a layer velocity volume, and then use the layer velocity volume to convert the pre-stack CRP gather data into angle gather data, in preparation for the subsequent pre-stack simultaneous inversion;

[0018] S2-6: establishing a low-frequency model of P-wave impedance, S-wave impedance and density according to the well data and the angle gather data, and then performing pre-stack simultaneous inversion to solve the P-wave impedance, S-wave impedance, P-wave velocity ratio and density data volume;

[0019] S2-7: Using the longitudinal wave impedance, transverse wave impedance, longitudinal and transverse wave velocity ratio and density data volume obtained in step S2-6, obtain the Young's modulus and Poisson's ratio data volume, and then obtain the brittle index volume;

[0020] S2-8: Optimize the best sensitive parameters of the reservoir section P-wave impedance, S-wave impedance, P-wave velocity ratio and density curve, TOC content curve and porosity curve, and finally select the P-wave velocity ratio curve and TOC content curve to establish a linear fitting formula. The fitting formula is:

[0021] TOC=12.624-5.8975*Vp / Vs (1)

[0022] In the above formula, TOC is the total organic carbon content; Vp / Vs is the ratio of longitudinal and transverse wave velocities; the longitudinal and transverse wave velocity ratio data volume obtained in step S2-6 is used to calculate the TOC inversion data volume, and then the thickness of the high-quality shale in the reservoir section is statistically calculated. The TOC of the high-quality shale is ≥2%, and then the porosity data volume is obtained using the longitudinal wave impedance data volume obtained in step S2-6. The fitting formula is:

[0023] POR=13.077-0.0008*Impedance (2)

[0024] In the above formula, POR is porosity; Impedance is longitudinal wave impedance;

[0025] S2-9: Predicting water saturation based on the SVM method, combining the P-wave impedance, S-wave impedance, and P-wave and S-wave velocity ratio inversion volume obtained by the pre-stack simultaneous inversion obtained in step S2-6;

[0026] S2-10: Detect the gas content of the study area by extracting high-frequency attenuation gradient attributes based on pre-stack migration pure wave seismic data;

[0027] S2-11: extracting reservoir segment slices from the ant volume data volume, TOC data volume, porosity data volume, water saturation data volume, brittleness index data volume, and high-frequency attenuation gradient attribute data volume respectively;

[0028] S2-12: According to the development characteristics of paleo-geomorphology, the paleo-geomorphology is divided into three categories: micro-uplift area, slope area and depression area. According to the characteristics of oil and gas parameters of the reservoir section above the well, it is determined that the slope part of the paleo-geomorphology is the Class I favorable area for the development of shale gas reservoir sweet spots, the micro-uplift area is the Class II favorable area for the development of shale gas reservoir sweet spots, and the depression area is the Class III area for the development of shale gas reservoir sweet spots. Combined with the regional structure, paleo-geomorphology and micro-scale fault development characteristics of the study area, a sweet spot prediction system based on "regional structure + micro-scale fault + paleo-geomorphology" is constructed.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present invention provides a shale gas reservoir sweet spot prediction method. Compared with the existing technology, it can further improve the accuracy of shale reservoir sweet spot prediction. In the sweet spot prediction process, it more fully considers the distribution characteristics and laws of geological, fault and reservoir parameters, and finally uses a multi-parameter mutual verification method to predict the sweet spot based on the construction of a sweet spot prediction system based on "regional structure + microscale fault + paleo-geomorphology". This method can reduce and eliminate the multi-solution in the sweet spot prediction process and improve the accuracy of sweet spot prediction. This method improves the existing method for shale reservoir sweet spot prediction, provides a basis for the optimization of favorable target areas in the shale gas exploration process, and thus accelerates the exploration and development process of shale gas. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The present invention provides a comparative tracking of the top and bottom interfaces of a shale reservoir.

[0032] Figure 2 The ant body attribute process provided by the embodiment of the present invention.

[0033] Figure 3 The paleo-geomorphology of the initial deposition of the shale reservoir section provided in the embodiment of the present invention.

[0034] Figure 4 The invention provides a pre-stack CRP gather optimization process.

[0035] Figure 5 This is a pre-stack simultaneous inversion result provided by an embodiment of the present invention.

[0036] Figure 6 This is the intersection analysis of reservoir parameters and elastic parameters provided by the embodiment of the present invention.

[0037] Figure 7 The invention provides a reservoir segment SVM water saturation prediction.

[0038] Figure 8 A high-frequency attenuation gradient attribute well profile provided by an embodiment of the present invention.

[0039] Fig. 9 The "regional structure + micro-scale faults + paleo-geomorphology" provided in the embodiment of the present invention constructs a sweet spot prediction system.

[0040] Fig.10 This is a plane distribution feature of the sweet spot prediction parameters provided by an embodiment of the present invention.

[0041] Fig.11 This is the shale reservoir sweet spot prediction result provided by the embodiment of the present invention.

[0042] Fig.12 The technical flow chart provided for the present invention. DETAILED DESCRIPTION

[0043] In the shale reservoir sweet spot prediction process provided by the present invention, the P-wave impedance, S-wave impedance and P-wave velocity ratio data are obtained by prestack simultaneous inversion, and on this basis, the Poisson's ratio, Young's modulus and brittleness index data body are obtained; the optimal sensitive parameters of TOC and porosity are obtained by parameter optimization, and on this basis, the TOC and porosity data body are obtained, and then the thickness of high-quality shale (TOC≥2%) is obtained; the development characteristics of micro-scale fractures in the reservoir section are obtained by using the ant body seismic attributes; the water saturation data body is obtained by using the SVM (support vector machine) method based on the prestack simultaneous inversion results; the high-frequency attenuation gradient attribute is selected to detect the gas content of the study area; in the sweet spot prediction process, the influence of geology, fractures and reservoir parameters is fully considered, and then a sweet spot prediction system based on "regional structure + micro-scale fractures + paleo-geomorphology" is constructed and classified, and then the units are divided according to the plane distribution characteristics and laws of each parameter, and finally the sweet spot is predicted by a method of mutual verification of multiple parameters, which can reduce and eliminate the multi-solution in the sweet spot prediction process and improve the accuracy of sweet spot prediction.

[0044] The present invention is described in detail below in conjunction with the embodiments and drawings, but it should be understood that the embodiments and drawings are only used to exemplify the present invention and do not constitute any limitation on the protection scope of the present invention. All reasonable changes and combinations within the scope of the inventive concept of the present invention fall within the protection scope of the present invention.

[0045] The present invention will be further described below in conjunction with the accompanying drawings.

[0046] Example 1

[0047] according to Fig.12 In this embodiment, according to the shale gas reservoir project data of a certain place, a shale gas reservoir sweet spot prediction method is provided, including the following steps:

[0048] Step 1: Well data collection, including well layer data, well logging curves (sound wave time difference curve DTC, shear wave time difference curve DTS, volume density curve DEN, total organic carbon content curve TOC and porosity curve POR, etc.); 3D seismic data collection, including pre-stack CRP gather data, pre-stack migration pure wave seismic data and post-stack result data; velocity body data collection, including root mean square velocity body;

[0049] Step 2: Based on the post-stack data, the top interface T of the shale gas reservoir in the study area is determined by well seismic calibration. 顶 and bottom interface T 底 and isochronous deposition interface T 等 And conduct layer tracking in the whole area; Figure 1 In the equation, a is T 顶 , b is T 底.

[0050] Step 3: Use post-stack data to detect reservoir fracture development characteristics. First, perform structural smoothing on the post-stack data to suppress random noise in the seismic data. Extract the maximum curvature attribute from the structurally smoothed data volume to highlight the discontinuity of the seismic event axis and improve the resolution of fracture identification. Then perform ant body attribute calculation to identify the development characteristics of micro-fractures. Figure 2 In the figure, a is the original post-stack seismic, b is the structural smoothing process, c is the maximum curvature attribute, d is the superposition display of the ant body attribute and the original seismic, and e is the reservoir segment slice of the ant body attribute.

[0051] Step 4: Isochronous deposition interface T based on step 2 等 and T 底 , the impression method was used to restore the paleo-geomorphology of the reservoir section in the study area at the early stage of deposition, such as Figure 3 .

[0052] Step 5: Suppress the random noise and linear interference of the prestack CRP gather data by using the prediction and denoising method, and then perform gather flattening on the prestack CRP gather data after prediction and denoising to eliminate the residual time difference, such as Figure 4 As shown;

[0053] Step 6: Use the Dix formula to convert the root mean square velocity volume in the velocity volume data into the layer velocity volume, and then use the layer velocity volume to convert the pre-stack CRP gather data into angle gather data to prepare for the subsequent pre-stack simultaneous inversion;

[0054] Step 7: According to the well data and the angle gather data described in step 6, a low-frequency model of P-wave impedance, S-wave impedance, and density is established, and then a pre-stack simultaneous inversion is performed to solve the P-wave impedance, S-wave impedance, P-wave velocity ratio, and density data volume, such as Figure 5 As shown, a is the longitudinal wave impedance, b is the transverse wave impedance, c is the density, and d is the ratio of longitudinal and transverse wave velocities;

[0055] Step 8: Using the longitudinal wave impedance, transverse wave impedance, longitudinal and transverse wave velocity ratio and density data obtained in step 7, obtain the Young's modulus and Poisson's ratio data, and then obtain the brittle index data;

[0056] Step 9: Optimize the best sensitive parameters of the reservoir section P-wave impedance, S-wave impedance, P-wave velocity ratio and density curve, TOC content curve and porosity curve, and finally select the P-wave velocity ratio curve and TOC content curve to establish a linear fitting formula. Through the fitting formula:

[0057] TOC=12.624-5.8975*Vp / Vs (1)

[0058] In the above formula, TOC is the total organic carbon content; Vp / Vs is the ratio of longitudinal and transverse wave velocities; the TOC inversion data volume is calculated using the longitudinal and transverse wave velocity ratio data volume obtained in step 7, and then the thickness of high-quality shale (TOC ≥ 2%) in the reservoir section is statistically calculated, and then the porosity data volume is obtained using the longitudinal wave impedance data volume obtained in step 7. The fitting formula is:

[0059] POR=13.077-0.0008*Impedance (2)

[0060] In the above formula, POR is porosity; Impedance is longitudinal wave impedance, such as Figure 6 , where a is the intersection of TOC and the ratio of P-wave velocity to S-wave velocity, and b is the intersection of porosity and P-wave impedance.

[0061] Step 10: Based on the SVM (support vector machine) method, the water saturation is predicted by combining the P-wave impedance, S-wave impedance, and P-wave and S-wave velocity ratio inversion volume obtained by the prestack simultaneous inversion described in step 7. The water saturation curve predicted by SVM has a high correlation coefficient with the actual water saturation curve, and the correlation coefficient is about 0.9. Figure 7 As shown;

[0062] Step 11: When seismic waves pass through oil and gas reservoirs, their high-frequency energy will be significantly attenuated and the low-frequency energy will increase. Therefore, the gas content of the study area is detected by extracting high-frequency attenuation gradient attributes based on pre-stack migration pure wave seismic data, such as Figure 8 As shown;

[0063] Step 12: Extract reservoir segment slices from the ant volume data volume, TOC data volume, porosity data volume, water saturation data volume, brittleness index data volume, and high-frequency attenuation gradient attribute data volume, such as Figure 8 As shown;

[0064] Step 13: Regional structure affects the lithology and physical properties of shale reservoirs; paleo-geomorphology has a significant controlling effect on the formation of shale gas reservoir sweet spots. According to the characteristics of paleo-geomorphology development, paleo-geomorphology is divided into three categories: micro-uplift area, slope area and depression area. According to the characteristics of oil and gas parameters in the reservoir section above the well, it is determined that the slope part of the paleo-geomorphology is a Class I favorable area for the development of shale gas reservoir sweet spots, the micro-uplift area is a Class II favorable area for the development of shale gas reservoir sweet spots, and the depression area is a Class III area for the development of shale gas reservoir sweet spots; micro-scale faults have an extremely important influence on shale gas enrichment and accumulation and the effect of shale reservoir fracturing transformation. Combined with the regional structure, paleo-geomorphology and micro-scale fault development characteristics of the study area, a sweet spot prediction system based on "regional structure + micro-scale faults + paleo-geomorphology" is constructed, which is mainly divided into Class I, Class II and Class III. Class II and Class III can be further divided into two sub-categories, among which Class I is the best, Class II is the second, and Class III is relatively the worst. Fig. 9 As shown;

[0065] Step 14: Based on the establishment of the sweet spot prediction system, the sweet spots are classified and zoned according to the planar development characteristics and distribution patterns of the parameters such as paleo-geomorphology, micro-scale faults, high-quality shale thickness, TOC, gas content (high-frequency attenuation gradient attribute), porosity, brittleness index, water saturation, etc. obtained in the above steps, such as Fig.10 As shown;

[0066] Step 15: Based on the classification and zoning of the sweet spot prediction parameters, the shale reservoir sweet spot prediction is carried out, which is mainly divided into 2 major categories and 5 minor categories. Category I can be divided into three sweet spots: I-1, I-2, and I-3, and category II can be divided into two sweet spots: II-1 and II-2. Fig.11 shown.

[0067] The above embodiments are only preferred implementations of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for predicting sweet spots in shale gas reservoirs, characterized in that: The following steps are involved: S1: Well data collection, including well layer data and logging curves, the logging curves include acoustic time difference curve DTC, shear wave time difference curve DTS, volume density curve DEN, total organic carbon content curve TOC and porosity curve POR; 3D seismic data collection, including pre-stack CRP gather data, pre-stack migration pure wave seismic data and post-stack result data; velocity body data collection, including root mean square velocity body; S2: Based on the three-dimensional seismic data, by performing gather optimization processing on the pre-stack CRP gather data, performing layer velocity conversion processing on the root mean square velocity body, performing high-frequency attenuation gradient attribute processing on the pre-stack migration pure wave seismic data, performing structural smoothing processing and extracting maximum curvature attributes and ant body attributes on the post-stack result data, and combining the well data, obtaining prediction parameters, the prediction parameters at least include the following parameters: paleo-geomorphology, micro-scale fractures, high-quality shale thickness, TOC, gas content, porosity, brittleness index and water saturation; according to the prediction parameters, the plane development characteristics and distribution law of shale gas reservoirs are obtained and classified and zoned; S3: Predict shale reservoir sweet spots based on the classification and zoning of various sweet spot prediction parameters.

2. The method for predicting sweet spots in shale gas reservoirs according to claim 1, characterized in that: The step S2 comprises the following steps: S2-1: Based on the post-stack data, the top interface T of the shale gas reservoir in the study area is determined by well seismic calibration 顶 and bottom interface T 底 and isochronous deposition interface T 等 And conduct layer tracking in the whole area; S2-2: Using the post-stack data to detect reservoir fracture development characteristics, firstly, the post-stack data is subjected to structural smoothing to suppress random noise of seismic data, and the maximum curvature attribute is extracted from the data body after structural smoothing to highlight the discontinuity of seismic event axes and improve the resolution of fracture identification, and then ant body attribute calculation is performed to identify the development characteristics of micro-fractures; S2-3: Based on the isochronous deposition interface T obtained in step S2-1 等 and T 底 , the paleo-geomorphology of the reservoir section in the study area at the early stage of deposition was restored by using the impression method; S2-4: suppressing the random noise and linear interference of the pre-stack CRP gather data by a prediction and denoising method, and then performing gather flattening processing on the pre-stack CRP gather data after prediction and denoising to eliminate the residual time difference; S2-5: Use the Dix formula to convert the root mean square velocity volume in the velocity volume data into a layer velocity volume, and then use the layer velocity volume to convert the pre-stack CRP gather data into angle gather data, in preparation for the subsequent pre-stack simultaneous inversion; S2-6: establishing a low-frequency model of P-wave impedance, S-wave impedance and density according to the well data and the angle gather data, and then performing pre-stack simultaneous inversion to solve the P-wave impedance, S-wave impedance, P-wave velocity ratio and density data volume; S2-7: Using the longitudinal wave impedance, transverse wave impedance, longitudinal and transverse wave velocity ratio and density data volume obtained in step S2-6, obtain the Young's modulus and Poisson's ratio data volume, and then obtain the brittle index volume; S2-8: Optimize the best sensitive parameters of the reservoir section P-wave impedance, S-wave impedance, P-wave velocity ratio and density curve, TOC content curve and porosity curve, and finally select the P-wave velocity ratio curve and TOC content curve to establish a linear fitting formula. The fitting formula is: TOC=12.624-5.8975*Vp / Vs (1) In the above formula, TOC is the total organic carbon content; Vp / Vs is the ratio of longitudinal and transverse wave velocities; the longitudinal and transverse wave velocity ratio data volume obtained in step S2-6 is used to calculate the TOC inversion data volume, and then the thickness of the high-quality shale in the reservoir section is statistically calculated. The TOC of the high-quality shale is ≥2%, and then the porosity data volume is obtained using the longitudinal wave impedance data volume obtained in step S2-6. The fitting formula is: POR=13.077-0.0008*Impedance (2) In the above formula, POR is porosity; Impedance is longitudinal wave impedance; S2-9: Predicting water saturation based on the SVM method, combining the P-wave impedance, S-wave impedance, and P-wave and S-wave velocity ratio inversion volume obtained by the pre-stack simultaneous inversion obtained in step S2-6; S2-10: Detect the gas content of the study area by extracting high-frequency attenuation gradient attributes based on prestack migration pure wave seismic data; S2-11: extracting reservoir segment slices from the ant volume data volume, TOC data volume, porosity data volume, water saturation data volume, brittleness index data volume, and high-frequency attenuation gradient attribute data volume respectively; S2-12: According to the development characteristics of paleo-geomorphology, the paleo-geomorphology is divided into three categories: micro-uplift area, slope area and depression area. According to the characteristics of oil and gas parameters of the reservoir section above the well, the slope part of the paleo-geomorphology is determined to be the Class I favorable area for the development of shale gas reservoir sweet spots, the micro-uplift area is the Class II favorable area for the development of shale gas reservoir sweet spots, and the depression area is the Class III area for the development of shale gas reservoir sweet spots. Combined with the regional structure, paleo-geomorphology and micro-scale fault development characteristics of the study area, a sweet spot prediction system based on "regional structure + micro-scale fault + paleo-geomorphology" is constructed.

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