A method for predicting gas saturation of shale reservoirs based on fracture index

Abstract

The application discloses a method for predicting shale reservoir gas saturation based on a fracture index, and belongs to the technical field of oil and natural gas exploration and development. t And the distance D between the fault lower disc section coordinate and the prediction target area coordinate b ; c, calculating the fracture index F; d, predicting the shale reservoir gas saturation of the prediction target area. The application is based on the fracture index, uses the influence of the fracture zone on the shale gas reservoir, and calculates the relationship between the shale reservoir gas saturation and the distance between the shale reservoir and the fault and the fault size, so that the shale reservoir gas saturation can be quickly predicted, the prediction is more accurate, and the application has strong operability and field practicality.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and in particular to a method for predicting gas saturation in shale reservoirs based on fracture index. Background Technology

[0002] The active geological structure of shale gas in China leads to well-developed fractures and complex oil and gas enrichment patterns. With the development of shale gas exploration in China, gas saturation has become a key evaluation parameter for assessing the exploitability of shale gas reservoirs. Based on shale gas exploration practices in southern Sichuan, a single well with a gas saturation below 45% does not meet the industrial gas flow standard. Currently, methods for predicting and evaluating shale reservoir gas saturation mainly include: direct core measurement, calculation using logging parameters, calculation based on organic carbon, and seismic data inversion. These methods first face the challenge of addressing the low resistivity of shale under the influence of complex fault zones, leading to significant discrepancies in calculation results; secondly, they require drilling to obtain data for calculation, which is a post-exploration evaluation. Currently, there is a lack of an easy-to-operate and rapid method for predicting shale reservoir gas saturation under the influence of complex fault zones, in the early stages of exploration and evaluation before a large number of drilled wells are available.

[0003] Chinese patent document CN104977618A, published on October 14, 2015, discloses a method for evaluating shale gas reservoirs and finding sweet spots, characterized by the following steps:

[0004] 1) Drill core columns in different directions from core columns at different burial depths in all wells in the exploration area, vacuum the core columns and pressurize and saturate them with mineralized water with the same resistivity as the mineralized water in the rock formation.

[0005] 2) Under simulated underground confining pressure and pore pressure conditions in the laboratory, the dynamic and static elastic parameters, elastic wave attenuation coefficient, dispersion effect and P-wave and S-wave velocity anisotropy coefficient of the saturated core column were measured to obtain the conversion relationship between the dynamic and static elastic modulus of the core, and anisotropic rock physics simulation and elastic parameter calculation and intersection were carried out.

[0006] Based on the cross-hatching results, the correlation between the sensitive elastic parameters or combinations of sensitive elastic parameters and the parameters of the shale gas sweet spot is obtained, and the parameters or combinations of parameters of the shale gas sweet spot are calculated and predicted.

[0007] 3) Obtain logging data from all boreholes in the exploration area, and perform correction processing on the logging data in the exploration area to eliminate the influence of factors such as borehole environment, well inclination, well fluid, well temperature and logging instrument errors on the logging curves, so as to obtain the optimal logging curves that can truly reflect the changes in formation physical properties.

[0008] Multimineral analysis and core testing methods were used to calculate the formation mineral composition and content, formation density, P-wave and S-wave velocities and porosity, and a lithology / rock physics model from the surface to the bottom of the well was established based on the geophysical logging curves of the entire well section.

[0009] 4) Perform perturbation analysis on the corrected logging curves by replacing attributes such as fluid, porosity, and lithology data;

[0010] 5) Analyze the mineral composition of the optimal logging curve using the principle of optimal logging combined with matrix solving method to obtain the mineral content and distribution law in the whole well section, and calculate the mineral composition and total formation saturation.

[0011] 6) Establish a lithology / rock physics model for the entire well section. Compare the predicted P-wave velocity, S-wave velocity, density, P-wave and S-wave impedance, and Poisson's ratio curves based on the rock physics model with the measured logging curves. Verify the reliability and rationality of the lithology / rock physics model by the degree of agreement between the predicted and measured curves.

[0012] 7) The dynamic and static elastic parameters, elastic wave attenuation coefficient, dispersion effect and P-wave and S-wave velocity anisotropy coefficient measured by the core column in step 2) are calibrated using the results calculated or predicted by the logging curves.

[0013] 8) Perform rock composition disturbance analysis on well logging data, including total organic carbon content, quartz, and clay minerals;

[0014] 9) Perform multi-attribute cross-plots on various reservoir attribute parameters, obtain the attribute characteristics of favorable shale intervals based on the cross-plot results, and determine the parameters or parameter combinations that can be used to predict shale gas sweet spots.

[0015] 10) Using the full-section rock physics model established in step 6), obtain the artificial seismic composite record or gather of the rock physics model, perform well-seismic calibration processing with the logging data and the artificial seismic composite record or gather, and analyze the amplitude variation with shot-receiver distance and amplitude variation with azimuth near the depth of the shale reservoir.

[0016] 11) Collect omnidirectional or wide-azimuth 3D seismic data in the exploration area;

[0017] 12) Collect two-dimensional moving shot-receiver offset vertical seismic profiles or three-dimensional vertical seismic profiles in wells within the exploration area; or collect two-dimensional moving shot-receiver offset vertical seismic profiles or three-dimensional vertical seismic profiles synchronously with surface three-dimensional seismic data;

[0018] 13) Based on the depth of the downhole geophone and the travel time of seismic waves from the ground to the downhole geophone, perform velocity analysis, migration imaging and inversion on the two-dimensional or three-dimensional vertical seismic profile data in the exploration area to obtain accurate formation velocity, formation attenuation coefficient and anisotropy parameters of each formation velocity.

[0019] 14) Perform high-precision surface integrated modeling on 3D seismic data of all directions or wide azimuth, calculate static correction, and perform static correction processing; use well constraints and vertical seismic profile data in wells to drive the processing of ground seismic data, improve the resolution and accuracy of ground seismic data, then perform fine cutting and iterative velocity calculation, and then complete velocity modeling and 3D pre-stack time migration and 3D pre-stack depth migration imaging processing.

[0020] 15) Improve the resolution of the data after processing the three-dimensional pre-stack depth migration imaging;

[0021] 16) High-resolution processing of seismic traces is performed using a non-parametric spectral analysis method based on statistical adaptive signal theory and a high-resolution subsurface reflection information estimation method with high fidelity.

[0022] 17) Extract accurate burial depth, thickness, occurrence, and planar distribution of shale reservoirs from three-dimensional high-resolution seismic data;

[0023] 18) Invert 3D high-resolution post-stack seismic data to obtain post-stack inverted seismic attribute data volumes for interpreting faults and fractures;

[0024] 19) Use coherence and correlation attributes such as dip angle and dip azimuth attributes, maximum and minimum curvature, positive curvature and negative curvature attributes to describe and characterize the distribution characteristics of underground faults, fractures and fissures and tectonic boundaries.

[0025] 20) Using an unsupervised adaptive statistical model neural network calculation method, attributes such as coherence, minimum and maximum curvature, curvature morphology index, instantaneous dip angle and dip azimuth are automatically classified in a nonlinear manner. Based on the distribution characteristics of crack density, seismic facies are determined, seismic fault facies are established, and fault and fracture zone distribution data are drawn to characterize seismic facies anomalies and fracture zones.

[0026] 21) Automatic tomography picking is performed using post-stack attribute data;

[0027] 22) Perform optimization, denoising, stretching correction, and flattening of pre-stack seismic gathers;

[0028] 23) Perform elliptic velocity inversion of pre-stack seismic data, and determine formation pressure and delineate high-pressure zones in shale reservoirs based on the changes and differences in velocity in the middle layers of the shale reservoir.

[0029] 24) Perform amplitude variation with shot-receiver distance and P-wave and S-wave impedance inversion of 3D pre-stack seismic data;

[0030] 25) Perform elliptic inversion of anisotropic parameters from 3D pre-stack seismic data;

[0031] 26) Perform elliptic inversion of the elastic modulus of pre-stack seismic data to obtain anisotropic elastic modulus. Through rock physics analysis, convert the anisotropic elastic modulus into reservoir parameters of the target layer.

[0032] 27) Joint geological interpretation and calibration of various seismic attributes characterizing faults and fractures;

[0033] 28) Based on the development of fractures in the shale formation, determine the possible formation damage zone after well completion and the possibility of fracturing fluid interfering with adjacent wells;

[0034] 29) Based on the conversion relationship between the dynamic and static elastic moduli of the core in step 2), the dynamic elastic moduli obtained by synchronous inversion of anisotropic elastic waves from the three-dimensional pre-stack seismic data are converted into static elastic moduli.

[0035] 30) By utilizing the correlation between static elastic modulus and rock brittleness, the brittleness distribution law and characteristics of shale reservoirs can be determined, and the completion and fracturing scheme design of horizontal wells can be optimized.

[0036] 31) By utilizing the distribution law of static elastic modulus or derived static elastic modulus in shale reservoirs, the brittle characteristics of shale reservoirs can be determined, the orientation and intensity of local geostress can be obtained, the orientation, direction and density of faults, fractures and fissures in shale reservoirs can be determined, and high total organic carbon content and high formation pressure zones in shale reservoirs can be predicted and delineated.

[0037] 32) By comprehensively obtaining various favorable parameters of shale gas reservoirs and combining them with the accurate burial depth, thickness, occurrence and planar distribution of shale reservoirs, the gas-bearing potential of shale gas reservoirs can be obtained and the sweet spot area for shale gas exploration and development can be delineated.

[0038] The patent document discloses a method for evaluating shale gas reservoirs and locating sweet spots. Based on the accurate burial depth, thickness, occurrence, planar distribution, total organic carbon content or organic matter abundance distribution, and the development, intensity, orientation, and distribution characteristics of fault fractures, the gas-bearing potential of shale gas reservoirs can be evaluated. However, the entire evaluation process is quite cumbersome, and due to the large number of influencing parameters, errors are easily introduced, thus affecting the accuracy of predictions. Summary of the Invention

[0039] To overcome the shortcomings of the prior art, this invention provides a method for predicting the gas saturation of shale reservoirs based on the fracture index. This invention utilizes the influence of fracture zones on shale gas reservoirs by calculating the relationship between the gas saturation of shale reservoirs and the distance between the shale reservoir and the fault, as well as the magnitude of the fault displacement. This method enables rapid and accurate prediction of the gas saturation of shale reservoirs and has strong operability and practical application in the field.

[0040] This invention is achieved through the following technical solution:

[0041] A method for predicting gas saturation in shale reservoirs based on fracture index, characterized by comprising the following steps:

[0042] a. Collect coordinates and geophysical data of the target area for prediction, and select faults around the coordinates of the target area for prediction based on the geophysical data;

[0043] b. Based on the selected target area coordinates and surrounding faults, obtain the fault displacement G and the distance D from the hanging wall coordinates to the target area coordinates using geophysical data. t The distance D between the fault footwall cross-section coordinates and the predicted target area coordinates b ;

[0044] c. Establish the parameter matrix A of m selected faults, perform optimal selection of hanging wall and footwall fault displacements, select effective faults, and calculate the fault index F.

[0045] d. Based on the fracture index F calculated in step c, predict the gas saturation of the shale reservoir in the target area.

[0046] In step a, selecting faults around the predicted target area coordinates based on geophysical data specifically refers to selecting faults within a range of 20 kilometers centered on the predicted target area coordinates.

[0047] In step b, the fault displacement G refers to the vertical elevation difference between the upper and lower surfaces of the fault and the corresponding reservoir strata.

[0048] In step b, the distance D between the coordinates of the fault hanging wall section and the coordinates of the predicted target area is... t It refers to the vertical distance from the target area coordinates to the fault strike to the cross-sectional coordinates of the fault's hanging wall.

[0049] In step b, the distance D between the fault footwall cross-section coordinates and the predicted target area coordinates is... b It refers to the vertical distance from the target area coordinates to the fault strike to the cross-sectional coordinates of the footwall of the fault.

[0050] In step c, the parameter matrix A is given by equation 1;

[0051]

[0052] Among them, G m1 Dt represents the fault displacement of each fault. m2 To predict the distance between the target area coordinates and the coordinates of the hanging wall section of the fault; D bm3 To predict the distance between the target area coordinates and the footwall cross-section coordinates of the fault.

[0053] In step c, the upper plate break distance and the lower plate break distance are optimized using Equation 2.

[0054]

[0055] In step c, calculating the fracture index F refers to the calculation of the output result of Equation 2, Min((D t12+ D b13 ) / 2、(D t22+ D b23 ) / 2、…and (D tm2+ D bm3 ) / 2)), select the smallest ((D) tMIN+ D bMIN If (D) / 2), tMIN -D bMIN If ) > 0, then calculate using Equation 3;

[0056]

[0057] Where A, C, α, and β are all constants; G m D is the discontinuity; bm To predict the distance between the target area coordinates and the coordinates of the hanging wall section of the fault.

[0058] In step c, calculating the fracture index F refers to the calculation of the output result of Equation 2, Min((D t12+ D b13 ) / 2、(D t22+ D b23 ) / 2、…and (D tm2+ D bm3 ) / 2)), select the smallest ((D) tMIN+ D bMIN If (D) / 2), tMIN -D bMIN If ) < 0, then calculate using Equation 4;

[0059] F=Aln(αG m +BD tm +C)-B Formula 4

[0060] Among them, D tm To predict the distance between the target area coordinates and the footwall cross-section coordinates of the fault.

[0061] In step d, the gas saturation of the shale reservoir in the target area is calculated using Equation 5.

[0062] S = Aln(F) + B (Equation 5)

[0063] Where S is the gas saturation of the shale reservoir in the target area, and B is a constant.

[0064] The basic principle of this invention is as follows:

[0065] Existing methods for determining and predicting gas saturation in shale reservoirs, such as direct core sampling, calculation using logging parameters, organic carbon-based calculations, and seismic data inversion, require a large amount of post-drilling data, such as core sampling analysis data and logging data. These methods determine and predict gas saturation in the predicted area only after drilling has been completed. In the early stages of exploration, when a large amount of exploration well data is lacking, other prediction methods become ineffective or have extremely low accuracy. This invention, however, utilizes the influence of fault zones on shale gas reservoirs. By analyzing the distance between the predicted shale reservoir and the fault, and the fault displacement, it predicts the gas saturation of the shale reservoir in the predicted area. The prediction data collection is simple and forward-looking, making it particularly suitable for early-stage evaluation, site selection, and well selection, ensuring a high geological success rate for exploration drilling.

[0066] The beneficial effects of this invention are mainly reflected in the following aspects:

[0067] 1. This invention, based on the fracture index, utilizes the influence of fracture zones on shale gas reservoirs. By calculating the relationship between the gas saturation of shale reservoirs and the distance between the shale reservoir and the fault, as well as the magnitude of the fault displacement, it can quickly predict the gas saturation of shale reservoirs. The prediction is more accurate and has strong operability and practical application in the field.

[0068] 2. This invention utilizes geophysical data from the early stages of exploration and evaluation, as well as the influence of fault zones on shale reservoirs. It establishes a correlation between fault parameters (fault displacement, fault distance, and fault influence index) from the geophysical data, and further establishes a correlation between the fault index and the gas saturation of the shale reservoir. Ultimately, in the early stages of exploration and evaluation, when a large number of exploration wells are lacking, it can predict the gas saturation of shale reservoirs by predicting the parameters of surrounding faults. This reduces exploration risks and is simple and easy to operate.

[0069] 3. The parameters used in this invention are all geophysical data from the early stages of exploration. These parameters are easy to process and collect, do not involve evaluation data from the later stages of exploration, and have universality and predictability.

[0070] 4. Compared with the prior art, the present invention can predict the gas saturation of the target area without a large amount of drilling data, thereby providing effective guidance for shale gas exploration site selection and evaluation deployment.

[0071] 5. This invention only requires the use of the influence of fault zones on shale gas reservoirs to predict the gas saturation of shale reservoirs by predicting the distance between the regional shale reservoir and the fault and the fault displacement data. It is especially suitable for situations where there is a lack of a large number of exploration well data in the early stages of exploration, and the prediction results have a high degree of consistency with the actual exploration results. Attached Figure Description

[0072] The present invention will now be further described in detail with reference to the accompanying drawings and specific embodiments:

[0073] Figure 1 This is a comparison chart of the predicted gas saturation and the actual gas saturation of this invention. Detailed Implementation

[0074] Example 1

[0075] A method for predicting gas saturation in shale reservoirs based on fracture index includes the following steps:

[0076] a. Collect coordinates and geophysical data of the target area for prediction, and select faults around the coordinates of the target area for prediction based on the geophysical data;

[0077] b. Based on the selected target area coordinates and surrounding faults, obtain the fault displacement G and the distance D from the hanging wall coordinates to the target area coordinates using geophysical data. t The distance D between the fault footwall cross-section coordinates and the predicted target area coordinates b ;

[0078] c. Establish the parameter matrix A of m selected faults, perform optimal selection of hanging wall and footwall fault displacements, select effective faults, and calculate the fault index F.

[0079] d. Based on the fracture index F calculated in step c, predict the gas saturation of the shale reservoir in the target area.

[0080] This embodiment is the most basic implementation method. Based on the fracture index, it utilizes the influence of fracture zones on shale gas reservoirs. By calculating the relationship between the gas saturation of shale reservoirs and the distance between the shale reservoir and the fault, as well as the magnitude of the fault displacement, it can quickly predict the gas saturation of shale reservoirs. The prediction is more accurate and has strong operability and practical application in the field.

[0081] Example 2

[0082] A method for predicting gas saturation in shale reservoirs based on fracture index includes the following steps:

[0083] a. Collect coordinates and geophysical data of the target area for prediction, and select faults around the coordinates of the target area for prediction based on the geophysical data;

[0084] b. Based on the selected target area coordinates and surrounding faults, obtain the fault displacement G and the distance D from the hanging wall coordinates to the target area coordinates using geophysical data. t The distance D between the fault footwall cross-section coordinates and the predicted target area coordinates b ;

[0085] c. Establish the parameter matrix A of m selected faults, perform optimal selection of hanging wall and footwall fault displacements, select effective faults, and calculate the fault index F.

[0086] d. Based on the fracture index F calculated in step c, predict the gas saturation of the shale reservoir in the target area.

[0087] In step a, selecting faults around the predicted target area coordinates based on geophysical data specifically refers to selecting faults within a range of 20 kilometers centered on the predicted target area coordinates.

[0088] In step b, the fault displacement G refers to the vertical elevation difference between the upper and lower surfaces of the fault and the corresponding reservoir strata.

[0089] In step b, the distance D between the coordinates of the fault hanging wall section and the coordinates of the predicted target area is... t It refers to the vertical distance from the target area coordinates to the fault strike to the cross-sectional coordinates of the fault's hanging wall.

[0090] In step b, the distance D between the fault footwall cross-section coordinates and the predicted target area coordinates is... b It refers to the vertical distance from the target area coordinates to the fault strike to the cross-sectional coordinates of the footwall of the fault.

[0091] This embodiment is a preferred implementation method. It utilizes geophysical data from the early stages of exploration and evaluation, as well as the influence of fault zones on shale reservoirs. By establishing a correlation between fault parameters such as fault displacement and fault distance from geophysical data and the fault influence index, and by establishing a correlation between the fault index and the gas saturation of shale reservoirs, it is possible to predict the gas saturation of shale reservoirs in the early stages of exploration and evaluation, even when there is a lack of a large number of exploration wells. This method can reduce exploration risks and is simple and easy to operate.

[0092] Example 3

[0093] A method for predicting gas saturation in shale reservoirs based on fracture index includes the following steps:

[0094] a. Collect coordinates and geophysical data of the target area for prediction, and select faults around the coordinates of the target area for prediction based on the geophysical data;

[0095] b. Based on the selected target area coordinates and surrounding faults, obtain the fault displacement G and the distance D from the hanging wall coordinates to the target area coordinates using geophysical data. t The distance D between the fault footwall cross-section coordinates and the predicted target area coordinates b ;

[0096] c. Establish the parameter matrix A of m selected faults, perform optimal selection of hanging wall and footwall fault displacements, select effective faults, and calculate the fault index F.

[0097] d. Based on the fracture index F calculated in step c, predict the gas saturation of the shale reservoir in the target area.

[0098] In step a, selecting faults around the predicted target area coordinates based on geophysical data specifically refers to selecting faults within a range of 20 kilometers centered on the predicted target area coordinates.

[0099] In step b, the fault displacement G refers to the vertical elevation difference between the upper and lower surfaces of the fault and the corresponding reservoir strata.

[0100] In step b, the distance D between the coordinates of the fault hanging wall section and the coordinates of the predicted target area is... t It refers to the vertical distance from the target area coordinates to the fault strike to the cross-sectional coordinates of the fault's hanging wall.

[0101] In step b, the distance D between the fault footwall cross-section coordinates and the predicted target area coordinates is... b It refers to the vertical distance from the target area coordinates to the fault strike to the cross-sectional coordinates of the footwall of the fault.

[0102] In step c, the parameter matrix A is given by equation 1;

[0103]

[0104] Among them, G m1 Dt represents the fault displacement of each fault. m2 To predict the distance between the target area coordinates and the hanging wall section coordinates of the fault; Db m3 To predict the distance between the target area coordinates and the footwall cross-section coordinates of the fault.

[0105] In step c, the upper plate break distance and the lower plate break distance are optimized using Equation 2.

[0106]

[0107] In step c, calculating the fracture index F refers to the calculation of the output result of Equation 2, Min((D t12+ D b13 ) / 2、(D t22+ D b23 ) / 2、…and (D tm2+ D bm3 ) / 2)), select the smallest ((D) tMIN+ D bMIN If (D) / 2), tMIN -D bMIN If ) > 0, then calculate using Equation 3;

[0108]

[0109] Where A, C, α, and β are all constants; G m D is the discontinuity; bm To predict the distance between the target area coordinates and the coordinates of the hanging wall section of the fault.

[0110] This embodiment is another preferred implementation method. The parameters used are all geophysical data from the early stage of exploration. The parameters are easy to process and collect, do not involve evaluation data in the later stage of exploration, and have universality and predictability.

[0111] Example 4

[0112] A method for predicting gas saturation in shale reservoirs based on fracture index includes the following steps:

[0113] a. Collect coordinates and geophysical data of the target area for prediction, and select faults around the coordinates of the target area for prediction based on the geophysical data;

[0114] b. Based on the selected target area coordinates and surrounding faults, obtain the fault displacement G and the distance D from the hanging wall coordinates to the target area coordinates using geophysical data. t The distance D between the fault footwall cross-section coordinates and the predicted target area coordinates b ;

[0115] c. Establish the parameter matrix A of m selected faults, perform optimal selection of hanging wall and footwall fault displacements, select effective faults, and calculate the fault index F.

[0116] d. Based on the fracture index F calculated in step c, predict the gas saturation of the shale reservoir in the target area.

[0117] In step a, selecting faults around the predicted target area coordinates based on geophysical data specifically refers to selecting faults within a range of 20 kilometers centered on the predicted target area coordinates.

[0118] In step b, the fault displacement G refers to the vertical elevation difference between the upper and lower surfaces of the fault and the corresponding reservoir strata.

[0119] In step b, the distance D between the coordinates of the fault hanging wall section and the coordinates of the predicted target area is... t It refers to the vertical distance from the target area coordinates to the fault strike to the cross-sectional coordinates of the fault's hanging wall.

[0120] In step b, the distance D between the fault footwall cross-section coordinates and the predicted target area coordinates is... b It refers to the vertical distance from the target area coordinates to the fault strike to the cross-sectional coordinates of the footwall of the fault.

[0121] In step c, the parameter matrix A is given by equation 1;

[0122]

[0123] Among them, G m1 Dt represents the fault displacement of each fault. m2 To predict the distance between the target area coordinates and the hanging wall section coordinates of the fault; Db m3 To predict the distance between the target area coordinates and the footwall cross-section coordinates of the fault.

[0124] In step c, the upper plate break distance and the lower plate break distance are optimized using Equation 2.

[0125]

[0126] In step c, calculating the fracture index F refers to the calculation of the output result of Equation 2, Min((D t12+ D b13 ) / 2、(D t22+ D b23 ) / 2、…and (D tm2+ D bm3 ) / 2)), select the smallest ((D) tMIN+ D bMIN If (D) / 2), tMIN -D bMIN If ) < 0, then calculate using Equation 4;

[0127] F=Aln(αG m +BD tm +C)-B Formula 4

[0128] Among them, D tm To predict the distance between the target area coordinates and the footwall cross-section coordinates of the fault.

[0129] In step d, the gas saturation of the shale reservoir in the target area is calculated using Equation 5.

[0130] S = Aln(F) + B (Equation 5)

[0131] Where S is the gas saturation of the shale reservoir in the target area, and B is a constant.

[0132] This embodiment is the best implementation method. Compared with the prior art, it can predict the gas saturation of the target area without a large amount of drilling data, and thus provide effective guidance for shale gas exploration site selection and evaluation deployment.

[0133] By simply utilizing the influence of fault zones on shale gas reservoirs and predicting the distance between regional shale reservoirs and faults, as well as the magnitude of fault displacement, it is possible to predict the gas saturation of shale reservoirs. This method is particularly suitable for situations where a large amount of exploration well data is lacking in the early stages of exploration, and the predicted results have a high degree of consistency with the actual exploration results.

[0134] The invention will be illustrated below using its application in the Longmaxi Formation shale reservoir in southern Sichuan as an example:

[0135] Collect coordinates and geophysical data of the target area for prediction. Based on the geophysical data, select faults around the coordinates of the target area, mainly selecting faults within a range of 20 kilometers centered on the coordinates of the target area.

[0136] Based on five faults within a 20-kilometer radius of the selected prediction area coordinates, the fault displacement G of these five faults was obtained using geophysical data. The fault displacement G represents the vertical elevation difference between the hanging wall and the corresponding reservoir strata on the fault's upper and lower surfaces, in meters (m). The shortest vertical distance D from the target area coordinates of these five faults to the hanging wall was also obtained using geophysical data. t The shortest vertical distance D from the target area coordinates perpendicular to the fault strike to the footwall of the fault. b The unit is km;

[0137] Based on the selection of 5 faults G and D t D b Select effective faults and establish parameter matrix A, with fault displacement G ≥ 100m;

[0138]

[0139] in:

[0140] A is a parameter matrix for the five selected faults; the first column is the fault displacement G of each fault. m1 The unit is meters (m); the second column is the distance D from the predicted target area coordinates to the coordinates of the hanging wall section of the fault. t m2 The unit is km; the third column is the distance D from the predicted target area coordinates to the footwall cross-section coordinates of the fault. b m2 The unit is km;

[0141] Optimize the upper and lower plate displacement:

[0142]

[0143] Based on the output, Min((second column + third column) / 2), the smallest effective data is selected as the first row. According to (2) the calculation result of the first row is >0, the distance between the minimum predicted target area coordinates and the cross-sectional coordinates of the footwall of the fault is used to calculate the fault index F according to the following formula:

[0144]

[0145] If the result of the row with the smallest valid data is less than 0, calculate the fracture index F according to the following formula:

[0146] F = 9.4888 * ln((-0.088) * G m +(1.12)*D tm +46.146)+28.717

[0147] Calculate the gas saturation S of the shale reservoir in the target area;

[0148] S = 10.701ln(F) + 26.052

[0149] To predict the gas saturation of shale reservoirs in the target area, specifically the Longmaxi Formation shale reservoir in southern Sichuan, 23 target points in the prediction area were selected for verification. The predicted gas saturation was compared with the gas saturation data from later drilling operations in the target area. Figure 1 It can be seen that the correlation R is as high as 9.4, and the average prediction error rate is less than 6%.

Claims

1. A method for predicting gas saturation in shale reservoirs based on fracture index, characterized in that, Includes the following steps: a. Collect coordinates and geophysical data of the target area for prediction, and select faults around the coordinates of the target area for prediction based on the geophysical data; b. Based on the selected target area coordinates and surrounding faults, obtain the fault displacement G and the distance D from the hanging wall coordinates to the target area coordinates using geophysical data. t The distance D between the fault footwall cross-section coordinates and the predicted target area coordinates b ; c. Establish the parameter matrix A of m selected faults, perform optimal selection of hanging wall and footwall fault displacements, select effective faults, and calculate the fault index F. d. Based on the fracture index F calculated in step c, predict the gas saturation of the shale reservoir in the target area; In step c, the parameter matrix A is given by equation 1; Formula 1; in, The fault displacement of each fault; To predict the distance between the target area coordinates and the coordinates of the hanging wall section of the fault; To predict the distance between the target area coordinates and the coordinates of the footwall section of the fault; In step c, the upper plate break distance and the lower plate break distance are optimized using Equation 2. Formula 2; In step c, calculating the fracture index F refers to the calculation of the Min((D)) value based on the output of Equation 2. t12+ D b13 ) / 2、(D t22+ D b23 ) / 2, ... and (D) tm2+ D bm3 ( ) / 2), select the smallest (D ) tmMIN+ D bmMIN ) / 2, if (D tmMIN -D bmMIN If ) > 0, then calculate using Equation 3; Formula 3; Among them, A, C, , Both B and G are constants; m D is the discontinuity; bm To predict the distance between the target area coordinates and the footwall cross-section coordinates of the fault.

2. The method for predicting gas saturation in shale reservoirs based on fracture index according to claim 1, characterized in that: In step a, selecting faults around the predicted target area coordinates based on geophysical data specifically means selecting faults within a range of 20 kilometers centered on the predicted target area coordinates.

3. The method for predicting gas saturation in shale reservoirs based on fracture index according to claim 1, characterized in that: In step b, the fault displacement G refers to the vertical elevation difference between the hanging wall and footwall sections of the fault and the corresponding reservoir strata.

4. The method for predicting gas saturation in shale reservoirs based on fracture index according to claim 1, characterized in that: In step b, the distance D between the coordinates of the fault hanging wall section and the coordinates of the predicted target area is... t It refers to the vertical distance from the target area coordinates to the fault strike to the coordinates of the hanging wall section of the fault.

5. The method for predicting gas saturation in shale reservoirs based on fracture index according to claim 1, characterized in that: In step b, the distance D between the fault footwall cross-section coordinates and the predicted target area coordinates is... b It refers to the vertical distance from the target area coordinates to the fault strike to the footwall cross-section coordinates.

6. The method for predicting gas saturation in shale reservoirs based on fracture index according to claim 1, characterized in that: In step c, calculating the fracture index F refers to the calculation of the Min((D)) value based on the output of Equation 2. t12+ D b13 ) / 2、(D t22+ D b23 ) / 2, ... and (D) tm2+ D bm3 ( ) / 2), select the smallest (D ) tmMIN+ D bmMIN ) / 2, if (D tmMIN -D bmMIN If ) < 0, then calculate using Equation 4; Equation 4; Among them, A2, B2, C2, and All are constants; To predict the distance between the target area coordinates and the coordinates of the hanging wall section of the fault.

7. The method for predicting gas saturation in shale reservoirs based on fracture index according to claim 1, characterized in that: In step d, the gas saturation of the shale reservoir in the target area is calculated using Equation 5. Formula 5; Where A3 and B3 are constants; S is the gas saturation of the shale reservoir in the target area.

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