A method for predicting sweet spots in shale gas reservoirs

Abstract

The present invention relates to the technical field of shale gas reservoir exploration and development, and discloses a method for predicting sweet spots in shale gas reservoirs, comprising the following steps: S1: collecting well data, including well layer data and well logging curves; collecting three-dimensional seismic data; and collecting velocity volume data; S2: based on the three-dimensional seismic data, performing gather optimization processing on pre-stack CRP gather data, performing layer velocity conversion processing on the root mean square velocity volume, performing high-frequency attenuation gradient attribute processing on pre-stack migration pure wave seismic data, performing structural smoothing processing on post-stack data, and extracting maximum curvature attributes and ant volume attributes, and combining the well data to obtain prediction parameters; obtaining the planar development characteristics and distribution patterns of the shale gas reservoir based on the prediction parameters and classifying and zoning them; and S3: predicting shale reservoir sweet spots based on the classification and zoning of each sweet spot prediction parameter. Compared with existing technologies, the present invention can further improve the accuracy of shale reservoir sweet spot prediction.

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 industrial development stage, 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) Seismic wave resolution is limited, making it difficult to directly identify these thin layer structures, resulting in poor imaging of the reservoir in seismic data and difficulty in effectively extracting many key information. 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 complexity 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] Existing Chinese patent publication number CN116009096A provides a method and apparatus for predicting shale gas sweet spots using multi-parameter fusion inversion. The invention discloses a method and apparatus for predicting shale gas sweet spots using multi-parameter fusion inversion. The method comprises: vertically dividing the target layer into multiple layers based on well logging data; predicting reservoir segment boundaries within the multiple layers based on seismic data; intersecting multiple logging attribute parameters based on the well logging data of the predicted reservoir segment boundaries to obtain multi-parameter fitting formulas for carbon content, gas content, and porosity; applying prestack parameter inversion results, combined with the multi-parameter fitting formula, to obtain profile and planar results for carbon content, gas content, and porosity; and combining the profile and planar results for carbon content, gas content, and porosity to predict shale gas sweet spots. This invention mitigates the impact of the instability of a single parameter and overcomes the instability of single-parameter results caused by the inversion of parameters such as density.

[0005] This existing technology uses pre-stack parameter inversion to obtain data such as Poisson's ratio, longitudinal and shear 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 paleogeomorphological characteristics during the deposition of the reservoir section, and the fracture distribution characteristics of the current reservoir section. In addition, the parameters used in the sweet spot prediction process are also relatively few. For example, parameters such as water saturation and high-quality shale (TOC ≥ 2%) thickness are not considered. 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 method for predicting sweet spots in shale gas reservoirs.

[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 well logging curves, including acoustic wave time difference curves (DTC), shear wave time difference curves (DTS), bulk density curves (DEN), total organic carbon content curves (TOC), and porosity curves (POR); 3D seismic data collection, including pre-stack CRP gather data, pre-stack migration pure wave seismic data, and post-stack result data; velocity volume data collection, including root mean square velocity volume;

[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 including at least the following parameters: paleogeology, micro-scale faults, high-quality shale thickness, TOC, gas content, porosity, brittleness index, and water saturation; obtaining the planar development characteristics and distribution patterns of the shale gas reservoir based on the prediction parameters and performing classification and zoning;

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

[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 entire area;

[0014] S2-2: Using the post-stack data to detect reservoir fracture development characteristics, first, structural smoothing is performed on the post-stack data to suppress random noise in the seismic data. Maximum curvature attributes are extracted from the structurally smoothed data volume to highlight discontinuities in seismic events and improve the resolution of fracture identification. Ant body attributes are then calculated to identify micro-fracture development characteristics.

[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 initial stage of deposition was restored by the impression method;

[0016] S2-4: suppressing 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 moveout;

[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 to prepare for the subsequent pre-stack simultaneous inversion;

[0018] S2-6: establishing a low-frequency model of P-wave impedance, S-wave impedance, and density based on 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, shear wave impedance, longitudinal and shear wave velocity ratio, and density data obtained in step S2-6, obtain the Young's modulus and Poisson's ratio data, and then obtain the brittleness index data;

[0020] S2-8: Optimize the optimal sensitive parameters of the reservoir section's P-wave impedance, S-wave impedance, P-wave velocity ratio, density curve, TOC content curve, and porosity curve. 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 the 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 calculated. The TOC of the high-quality shale is ≥2%. The porosity data volume is then calculated 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 and the P-wave impedance, S-wave impedance, and P-wave and S-wave velocity ratio inversion volume obtained by the pre-stack simultaneous inversion in step S2-6;

[0026] S2-10: Detect gas-bearing properties in 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: Based on 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 oil and gas parameter characteristics of the reservoir section above the well, the slope part of the paleo-geomorphology is determined to be 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. 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] This invention provides a method for predicting sweet spots in shale gas reservoirs. Compared to existing technologies, it can further improve the accuracy of sweet spot prediction in shale reservoir sections. The sweet spot prediction process more fully considers the distribution characteristics and patterns of geological, fault, and reservoir parameters. Based on a sweet spot prediction system based on "regional structure + microscale faults + paleogeography," it ultimately predicts sweet spots using a multi-parameter mutual verification method. This method can reduce and eventually eliminate the multi-solution problem in the sweet spot prediction process, thereby improving the accuracy of sweet spot prediction. This method improves existing methods for predicting sweet spots in shale reservoirs, provides a basis for selecting favorable target areas during shale gas exploration, and thus accelerates the exploration and development of shale gas. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is the comparative tracking of the top and bottom interfaces of a shale reservoir provided by an embodiment of the present invention.

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

[0033] Figure 3 This is the paleo-geomorphology of the early depositional stage of the shale reservoir provided by the embodiment of the present invention.

[0034] Figure 4 This invention provides an embodiment of the pre-stack CRP gather optimization process.

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

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

[0037] Figure 7 This is the SVM water saturation prediction of the reservoir segment provided by the embodiment of the present invention.

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

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

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

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

[0042] Figure 12 This is a technical flow chart provided by the present invention. DETAILED DESCRIPTION

[0043] In the shale reservoir sweet spot prediction process provided by the present invention, pre-stack simultaneous inversion is used to obtain P-wave impedance, S-wave impedance and P-wave velocity ratio data, based on which Poisson's ratio, Young's modulus and brittleness index data are obtained; the optimal sensitive parameters of TOC and porosity are obtained through parameter optimization, based on which TOC and porosity data 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 using ant body seismic attributes; the water saturation data volume is obtained using the SVM (support vector machine) method based on the pre-stack simultaneous inversion results; the gas content of the study area is detected by selecting high-frequency attenuation gradient attributes; in the sweet spot prediction process, the influence of geological, fault and reservoir parameters is fully considered, and then a sweet spot prediction system based on "regional structure + micro-scale fracture + paleogeology" is constructed and classified, and then units are divided according to the planar distribution characteristics and laws of each parameter. Finally, a method of mutual verification of multiple parameters is used to predict the sweet spot. This method can reduce and then eliminate the multi-solution problem in the sweet spot prediction process and improve the accuracy of sweet spot prediction.

[0044] The present invention is described in detail below with reference to the embodiments and accompanying drawings. However, it should be understood that the embodiments and accompanying drawings are merely exemplary descriptions of the present invention and do not constitute any limitation on the scope of protection of the present invention. All reasonable variations and combinations within the scope of the inventive concept of the present invention fall within the scope of protection of the present invention.

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

[0046] Example 1

[0047] according to Figure 12 In this embodiment, a method for predicting sweet spots in a shale gas reservoir is provided based on the project data of a shale gas reservoir in a certain place, including the following steps:

[0048] Step 1: Well data collection, including well layer data, well logging curves (sonic time difference curves (DTC), shear wave time difference curves (DTS), bulk density curves (DEN), total organic carbon content curves (TOC), and porosity curves (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 volume data collection, including root mean square velocity volume;

[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 result, b is the structural smoothing process, c is the maximum curvature attribute, d is the superposition display of ant body attribute and original seismic, and e is the reservoir section slice of 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 pre-stack CRP gather data by using the prediction and denoising method, and then perform gather flattening on the pre-stack 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. 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: Based on 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. 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, as shown in the following example: Figure 5 As shown, a is the longitudinal wave impedance, b is the shear wave impedance, c is the density, and d is the ratio of longitudinal and shear wave velocities;

[0055] Step 8: Using the longitudinal wave impedance, shear wave impedance, longitudinal and shear 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 optimal sensitive parameters of the reservoir section's P-wave impedance, S-wave impedance, P-wave velocity ratio, density curve, TOC content curve, and porosity curve. Finally, select the P-wave velocity ratio curve and TOC content curve to establish a linear fitting formula. The fitting formula is:

[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 the P-wave velocity to the S-wave velocity. The TOC inversion data volume is calculated using the P-wave velocity ratio data volume obtained in step 7, and then the thickness of high-quality shale (TOC ≥ 2%) in the reservoir section is calculated. The porosity data volume is then calculated using the P-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 pre-stack simultaneous inversion in step 7. The water saturation curve calculated by SVM prediction has a high correlation coefficient with the actual water saturation curve, and the correlation coefficient is around 0.9. Figure 7 As shown;

[0062] Step 11: Since the high-frequency energy of seismic waves will be significantly attenuated and the low-frequency energy will increase when passing through the oil and gas reservoir, the gas-bearing property 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 oil and gas parameter characteristics 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. Micro-scale faults have an extremely important influence on shale gas enrichment and accumulation and the effect of shale reservoir fracturing transformation. Combining 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 categories I, II and III. Among them, categories II and III can be further divided into two subcategories, among which category I is the best, category II is the second, and category III is relatively the worst. Figure 9 As shown;

[0065] Step 14: Based on the establishment of the sweet spot prediction system, the sweet spot is classified and zoned according to the planar development characteristics and distribution patterns of parameters such as paleo-geomorphology, micro-scale faults, high-quality shale thickness, TOC, gas content (high-frequency attenuation gradient attribute), porosity, brittleness index, and water saturation obtained in the above steps, such as Figure 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. It 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. Category II can be divided into two sweet spots: II-1 and II-2. Figure 11 shown.

[0067] The above embodiments are merely preferred embodiments of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions that fall within the scope of protection of the present invention are within the scope of protection of the present invention. It should be noted that improvements and modifications that can be made by a person skilled in the art without departing from the principles of the present invention are also considered to be within the scope of protection of the present invention.

Claims

1. A method for predicting sweet spots in shale gas reservoirs, characterized by: The following steps are involved: S1: Well data collection, including well layer data and well logging curves, including acoustic wave time difference curves (DTC), shear wave time difference curves (DTS), bulk density curves (DEN), total organic carbon content curves (TOC), and porosity curves (POR); 3D seismic data collection, including pre-stack CRP gather data, pre-stack migration pure wave seismic data, and post-stack result data; velocity volume data collection, including root mean square velocity volume; S2: Specifically includes 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 entire area; S2-2: Using the post-stack data to detect reservoir fracture development characteristics, first, structural smoothing is performed on the post-stack data to suppress random noise in the seismic data. Maximum curvature attributes are extracted from the structurally smoothed data volume to highlight discontinuities in seismic events and improve the resolution of fracture identification. Ant body attributes are then calculated to identify micro-fracture development characteristics. S2-3: Based on the isochronous deposition interface T obtained in step S2-1 等 and bottom interface T 底 , the paleo-geomorphology of the reservoir section in the study area at the initial stage of deposition was restored by the impression method; S2-4: suppressing 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 moveout; 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 to prepare for the subsequent pre-stack simultaneous inversion; S2-6: establishing a low-frequency model of P-wave impedance, S-wave impedance, and density based on 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, shear wave impedance, longitudinal and shear wave velocity ratio, and density data obtained in step S2-6, obtain the Young's modulus and Poisson's ratio data, and then obtain the brittleness index data; S2-8: Select the optimal sensitive parameters of the reservoir section's P-wave impedance, S-wave impedance, P-wave velocity ratio, density curve, TOC content curve, and porosity curve. 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 the 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 calculated. The TOC of the high-quality shale is ≥2%. The porosity data volume is then calculated 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 and the P-wave impedance, S-wave impedance, and P-wave and S-wave velocity ratio inversion volume obtained by the pre-stack simultaneous inversion in step S2-6; S2-10: Detect gas-bearing properties in the study area by extracting high-frequency attenuation gradient attributes based on pre-stack 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: Based on paleo-geomorphological development characteristics, paleo-geomorphology was divided into three categories: micro-high, slope, and depression. Based on the oil and gas parameters of the reservoir section above the well, the slope of the paleo-geomorphology was determined to be a Class I favorable area for the development of shale gas reservoir sweet spots, the micro-high as a Class II favorable area for the development of shale gas reservoir sweet spots, and the depression as a Class III area for the development of shale gas reservoir sweet spots. Combining 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" was constructed. S3: According to the sweet spot prediction system, shale reservoir sweet spots are predicted based on the classification and zoning of various sweet spot prediction parameters.

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