Method for predicting fracturability of shale based on rock physics experiment and correlation with fracturing

Abstract

The application discloses a shale fracturability prediction method based on rock physical experiments and associated fracturing, which considers the environmental factors of simulating shale samples in actual strata, establishes a numerical model, a rock mechanics parameter prediction model, draws a well logging diagram, obtains rock mechanics parameters of a horizontal section, determines main control factors of shale fracturability, finally establishes a shale reservoir fracturability prediction model, and verifies the reliability of the method. The prediction method can comprehensively and effectively predict the shale fracturability, improve the accuracy of the fracturability, further improve the fracturing effect, and save the mining cost. The shale fracturability prediction method can be used for shale gas reservoir prediction and shale gas development.

Description

Technical Field

[0001] This invention belongs to the field of seismic exploration technology for oil and gas, and relates to a prediction method for shale gas exploration technology in unconventional natural gas. Specifically, it is a method for predicting the fracturability of shale based on rock physics experiments and associated with fracturing. Background Technology

[0002] With conventional oil and natural gas resources becoming increasingly depleted and exploration and development becoming more challenging, unconventional oil and gas resources are gradually emerging as alternative energy sources. Shale gas is a crucial type of unconventional oil and gas resource, and countries worldwide are placing increasing emphasis on shale gas extraction technologies. As is well known, fracturability (the property of shale in hydraulic fracturing to effectively fracture and increase production) directly determines whether a dense and effective network of complex fractures can be created, and also plays a significant role in shale gas production, making it crucial for shale gas productivity. Therefore, evaluating shale fracturability is of great importance for selecting optimal fracturing intervals in shale gas wells, optimizing shale gas field development plans, and predicting economic benefits.

[0003] Currently, scholars both domestically and internationally have studied methods for predicting the fracturability of shale gas reservoirs from the perspectives of rock mechanical properties, mineral composition, and geological factors. These assessments of shale gas reservoir fracturability primarily rely on post-fracturing production as a basis for evaluation. However, good fracturing results do not necessarily equate to high shale gas well production; shale gas well production is also related to the geological sweet spots of the shale reservoir section. To better reflect the fracturing effect of shale gas reservoirs from an engineering perspective, this field has proposed a shale gas reservoir fracturability evaluation method based on the scale of fracturing (number of microseismic events), further guiding shale gas reservoir development. However, most fracturability prediction or evaluation methods fail to consider the significant differences in fracturability between different fracturing sections of the same horizontal well, resulting in an inability to effectively and comprehensively evaluate the fracturability of shale reservoirs, further leading to indiscriminate exploitation or low exploitation efficiency. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a comprehensive and effective method for predicting the fracturing capability of shale, thereby guiding the design of well locations and fracturing processes during shale gas development. By improving the accuracy of the fracturing capability prediction method, the aim is to improve the fracturing effect and shale gas production while reducing the investment cost of shale gas extraction.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for predicting the fracturability of shale based on rock physics experiments and associated fracturing is proposed. The establishment of this prediction method includes the following steps:

[0007] S1. Establishing a prediction model for rock mechanical parameters of shale formations using multivariate statistical methods.

[0008] S11. Sample collection, sample preparation, and basic data processing

[0009] Collect shale rock samples, prepare them into test specimens, and collect and organize core data, geological data, and well logging data.

[0010] S12. Rock Acoustic Wave and Mechanical Simulation

[0011] Acoustic and mechanical simulations were performed on shale, and triaxial compression numerical models, Brazilian splitting numerical models, and Brazilian disk numerical models of shale were established respectively.

[0012] S13. Determining rock mechanical parameters

[0013] Acoustic measurements were performed on shale samples, and rock mechanical parameters were determined based on three indoor experiments: triaxial compression test, Brazilian splitting test, and Brazilian disk test.

[0014] S14. Establish a rock mechanics parameter prediction model

[0015] Based on the combined results of numerical simulation experiments and laboratory experiments, a rock mechanics parameter prediction model was established.

[0016] S2. Draw well logging diagrams and obtain rock mechanics parameters for the horizontal section.

[0017] S3. Using the Analytic Hierarchy Process (AHP) to identify the main controlling factors of shale fracturing ability.

[0018] S4. Use the analytic hierarchy process (AHP) to obtain the weight coefficients of each influencing factor, and then establish a fracturability index evaluation model.

[0019] S5. Verify the reliability of the model.

[0020] As a limitation of the invention, step S2 includes the following steps: drawing a well logging chart based on the rock mechanics parameter prediction model and well logging data to obtain the rock mechanics parameters of the horizontal section.

[0021] As a second limitation of the invention, step S3 includes the following steps: using the number of microseismic events as a reference sequence, and using four indicators—brittleness index, fracture toughness, uniaxial compressive strength, and tensile strength—as a factor sequence, the grey relational analysis method is used to obtain the grey relational degree of each factor.

[0022] As a third limitation of the invention, step S4 includes the following steps: based on the hierarchical analysis method, according to the gray relational degree, establish a fracturability evaluation index judgment matrix, obtain the weight coefficients of each factor, and then establish a fracturability index evaluation model.

[0023] The fracturability index evaluation model is expressed as follows:

[0024] FI = W1B + W2K ic +W3UCS+W4S t

[0025] Where W1~W4 are weighting coefficients ranging from 0 to 1, dimensionless; B is the fragility index, with units of dimensionless; K ic Fracture toughness, unit: UCS stands for uniaxial compressive strength, measured in MPa; S t Tensile strength, measured in MPa.

[0026] As a fourth limitation of the invention, step S5 includes the following steps: calculating the correlation between the fracturability evaluation index and the number of microseismic events by means of the intersection relationship between the number of microseismic events and the fracturability index, and verifying the reliability of the fracturability index evaluation model.

[0027] As a further limitation of the fourth limitation of the invention, if the fracturability evaluation index and the number of microseismic events show a positive correlation in step S5, it indicates that the constructed fracturability index evaluation model has a certain degree of reliability.

[0028] As a second of the fourth limitations on the invention, the sample in step S11 includes a standard cylindrical sample and a Brazilian disc sample; the logging data includes density data, P-wave data, S-wave data, and microseismic monitoring data.

[0029] The acoustic wave measurement method in step S12 is the acoustic wave transmission method;

[0030] In step S13, the triaxial compression test measures the uniaxial compressive strength, elastic modulus, and Poisson's ratio, and calculates the brittleness index; the Brazilian splitting test measures the tensile strength of the rock sample; and the Brazilian disc test measures the fracture toughness of the rock sample.

[0031] As a third limitation of the fourth limitation on the invention, step S14 includes the following sub-steps:

[0032] S141. Based on the combined results of numerical simulation experiments and indoor experiments, the least squares method is used to establish a tensile strength prediction model based on transverse wave time difference.

[0033] S142. Based on the combined results of numerical simulation and indoor experiments, the least squares method is used to establish a fracture toughness prediction model based on transverse wave time difference and longitudinal wave time difference.

[0034] S143. Based on the combined results of numerical simulation and indoor experiments, the least squares method is used to establish a uniaxial compressive strength prediction model based on the transverse wave time difference and the longitudinal wave time difference.

[0035] S144. Based on the combined results of numerical simulation and indoor experiments, a brittleness index prediction model based on dynamic elastic modulus is established using the least squares method.

[0036] By adopting the above technical solution, the technical progress achieved by this invention compared with the prior art is as follows:

[0037] ① When establishing the fracturability prediction method in this invention, well logging charts are drawn based on the established rock mechanics parameter prediction model and well logging data. The influence of different fracturing segments on fracturability is incorporated into the establishment of the model, thereby increasing the effectiveness of the prediction method.

[0038] ② This invention uses grey relational analysis to analyze the influence of various parameters by combining the number of microseismic events, and then obtains a prediction method. Compared with the traditional method of using the brittleness index alone for prediction, it can more effectively and comprehensively evaluate the fracturability of shale, and the evaluation results are more reliable.

[0039] ③ This invention obtains parameters such as tensile strength, compressive strength, fracture toughness, Poisson's ratio, and elastic modulus by simulating environmental factors of shale rock samples in actual strata and conducting rock physics tests on shale rock samples. Considering the heterogeneity of rocks, the invention further establishes a basic model of shale rock samples by combining numerical simulation and physical experimental test results, so that the established model has a higher degree of fit with reality.

[0040] In summary, the shale fracturing prediction method based on rock physics experiments and associated fracturing provided by this invention fully considers the environmental factors of shale in actual formations, integrates the influence of various parameters, and constructs a shale reservoir fracturing evaluation index based on the correlation between grey relational analysis, hierarchical analysis, and microseismic events. This method can comprehensively and effectively predict shale fracturing, improve the accuracy of fracturing, further enhance fracturing effect, and save mining costs.

[0041] The shale fracturing prediction method provided by this invention can be used for shale gas reservoir prediction and shale gas development. Attached Figure Description

[0042] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0043] Figure 1 This is a diagram of a uniaxial compression numerical model in an embodiment of the present invention;

[0044] Figure 2 This is a diagram of the Brazilian splitting numerical simulation model in an embodiment of the present invention;

[0045] Figure 3 This is a numerical simulation model diagram of the Brazilian herringbone-shaped grooved disk in an embodiment of the present invention;

[0046] Figure 4 This is a graph showing the relationship between the brittleness index and fracture toughness in an embodiment of the present invention.

[0047] Figure 5 This is a graph showing the relationship between tensile strength and fracture toughness in an embodiment of the present invention.

[0048] Figure 6 This is a graph showing the relationship between the brittleness index and uniaxial compressive strength in an embodiment of the present invention.

[0049] Figure 7 This is a graph showing the relationship between uniaxial compressive strength and tensile strength in an embodiment of the present invention.

[0050] Figure 8(a) is a graph showing the relationship between the brittleness index and the dynamic elastic modulus in an embodiment of the present invention;

[0051] Figure 8(b) is a graph showing the relationship between the brittleness index and the static elastic modulus in an embodiment of the present invention.

[0052] Figure 9 This is a well logging diagram from an embodiment of the present invention;

[0053] Figure 10 This is a graph showing the relationship between the number of microseismic events and the fracturability evaluation index in an embodiment of the present invention. Detailed Implementation

[0054] The present invention will be further described in detail below through specific embodiments. It should be understood that the described embodiments are only for explaining the present invention and do not limit the present invention.

[0055] Experimental methods not specified in the examples are generally performed under standard conditions or as recommended by the manufacturer.

[0056] The instruments used in this embodiment of the invention are a multi-frequency ultrasonic measuring instrument, a 50KN testing machine, and an RTR-1000 rock triaxial mechanical testing system.

[0057] The software used in this embodiment of the invention is CIFLog logging software and Matlab software.

[0058] Example: A method for predicting the fracturability of shale based on rock physics experiments and associated with fracturing.

[0059] This embodiment presents a method for predicting the fracturability of shale based on rock physics experiments and related to fracturing, comprising the following steps performed sequentially:

[0060] S1. Establishing a prediction model for rock mechanical parameters of shale formations using multivariate statistical methods.

[0061] S11. Sample collection, sample preparation, and basic data processing

[0062] The shale samples used in this invention were collected from a region in the Sichuan Basin, with the target layer being the Longmaxi Formation. A total of 90 samples were collected, as shown in Table 1.

[0063] Table 1. List of Shale Rock Samples

[0064] Serial Number Sample number Rocks in Brief 1-30 D1-D30 Black, layered 31-60 B1-B30 Black, layered 61-90 L1-L30 Black, layered

[0065] The cylindrical samples used in this experiment were prepared according to the triaxial compression test method recommended by the International Society for Rock Mechanics (ISRM). The samples were prepared into 30 standard cylindrical samples of 25*50 mm, 30 standard cylindrical samples of 25*25 mm, and 30 Brazilian disc samples of 75*50 mm herringbone groove.

[0066] Simultaneously, core data, geological data, and well logging data required for the experiments of this invention were collected and organized.

[0067] The core data includes core descriptions and core photographs;

[0068] Geological data includes stratigraphic information and stratigraphic characteristics;

[0069] Well logging data includes density data, P-wave data, S-wave data, and microseismic monitoring data.

[0070] S12. Rock Acoustic Wave and Mechanical Simulation

[0071] Based on the acoustic simulation results of shale, RFPA software was used to establish uniaxial compression numerical models, Brazilian splitting numerical models, and Brazilian herringbone-grooved disk numerical models of shale. The uniaxial compression numerical model is shown below. Figure 1 As shown, the Brazilian splitting numerical model is as follows: Figure 2 As shown, the numerical model of the Brazilian herringbone-grooved disk is as follows: Figure 3 As shown.

[0072] Uniaxial compression numerical simulation is used to obtain mechanical parameters such as uniaxial compressive strength, elastic modulus and Poisson's ratio; Brazilian splitting numerical simulation is used to obtain tensile strength parameters; and Brazilian herringbone grooved disk simulation is used to obtain fracture toughness parameters.

[0073] S13. Determining rock mechanical parameters

[0074] Acoustic wave transmission method was used to measure the acoustic waves of standard cylindrical specimens and herringbone-grooved Brazilian disc specimens. Based on triaxial compression tests, mechanical parameters such as uniaxial compressive strength, elastic modulus, and Poisson's ratio were obtained. On this basis, the brittleness index was calculated using the secant method. The formula for the brittleness index is as follows:

[0075]

[0076] Where B is the brittleness index, which is dimensionless; α is the adjustment coefficient, which is dimensionless; σ pPeak intensity, in MPa; ε p Peak strain, expressed as a percentage.

[0077] The tensile strength of the rock samples was obtained based on the Brazilian splitting test; the fracture toughness of the rock samples was obtained based on the Brazilian disk test.

[0078] Based on the above experimental results, the relationship between uniaxial compressive strength, tensile strength, elastic modulus, Poisson's ratio, fracture toughness, and brittleness index of the rock samples was studied. Curves showing the relationship between uniaxial compressive strength and tensile strength, and fracture toughness, are presented. The curve showing the relationship between brittleness index and fracture toughness is shown in the figure below. Figure 4 The curve showing the relationship between tensile strength and fracture toughness is shown below. Figure 5 The curve showing the relationship between the brittleness index and uniaxial compressive strength is shown in the figure below. Figure 6 The curve showing the relationship between uniaxial compressive strength and tensile strength is shown below. Figure 7 As shown, the relationship curves between the brittleness index and the dynamic elastic modulus and the static elastic modulus are shown in Figure 8(a) and Figure 8(b), respectively.

[0079] from Figure 4-7 As can be seen from Figures 8(a) and 8(b), the brittleness index is positively correlated with both the dynamic and static elastic moduli, and the correlation between the brittleness index and the dynamic elastic modulus is stronger.

[0080] S14. Establish a rock mechanics parameter prediction model

[0081] S141. Using the least squares method, a tensile strength prediction model based on shear wave time difference is established. The calculation is shown in formula (1), specifically:

[0082] σ t = -0.1119Δt s +33.08(R 2 =0.8216) (1)

[0083] S142. Using the least squares method, a fracture toughness prediction model based on P-wave and S-wave time difference is established. The calculation is shown in formula (2).

[0084] K IC = -0.00163Δt c -0.00209Δt s +1.8709(R 2 =0.7122) (2)

[0085] S143. Using the least squares method, a uniaxial compressive strength prediction model based on longitudinal wave time difference and transverse wave time difference is established. The calculation is shown in formula (3).

[0086] UCS = -0.5304Δt c -0.1544Δt s +283.1488(R 2 =0.683) (3)

[0087] S144. Using the least squares method, a brittleness index prediction model based on dynamic elastic modulus is established. The calculation is shown in formula (4), specifically as follows:

[0088] B = 0.0007E d -6.2791(R 2 =0.5766) (4)

[0089] Where, σ t Δt represents tensile strength, with units of MPa; c This represents the P-wave time difference, expressed in μs / m.

[0090] △t s E represents the transverse wave time difference, in μs / m. d This represents the dynamic elastic modulus, with units of GPa.

[0091] S2. Draw well logging diagrams and obtain rock mechanics parameters for the horizontal section.

[0092] Based on the rock mechanics parameter prediction model and the density, P-wave, S-wave, and microseismic monitoring data from well logging, well log charts were generated using CIFLog software to obtain the rock mechanics parameters of the horizontal section. The generated well log charts are shown below. Figure 9 As shown. Based on Figure 9 The logging diagram shown can obtain the rock mechanical parameters of different fractured sections.

[0093] S3. Using the Analytic Hierarchy Process (AHP) to identify the main controlling factors of shale fracturing ability.

[0094] The number of microseismic events represents the fracturability of shale; the higher the fracturability, the more microseismic events. Using the number of microseismic events as a reference sequence, and rock mechanics factors such as brittleness index, fracture toughness, uniaxial compressive strength, and tensile strength as a factor sequence, grey relational analysis was performed using Matlab software or a program to obtain the grey relational degree of each factor. The resolution coefficient was obtained according to the following method:

[0095] 1) When When outliers exist in the comparison sequence, the weighting coefficient ρ should be taken as a smaller value to suppress them.

[0096] The dominance of ρ is usually taken as 1.5ε(k).

[0097] 2) When When the comparison sequence is relatively stable, ρ should be a larger value than 0.5 to reflect the overall correlation.

[0098] Therefore, when When ρ can take values ​​in the range [1.5ε(k), 2ε(k)], it is usually taken as ρ = 2ε(k). When 2ρ > 1, ρ can take any value in the range [0.8, 1]. When ε(k) = 0, the correlation coefficient ξ is... i The value of (k) is independent of ρ, and ρ can take any value in (0,1].

[0099] Analysis of the correlations between the brittleness index, fracture toughness, uniaxial compressive strength, and tensile strength in this embodiment showed to be 0.79, 0.75, 0.70, and 0.67, respectively.

[0100] S4. Use the analytic hierarchy process (AHP) to obtain the weight coefficients of each influencing factor, and then establish a fracturability index evaluation model.

[0101] The hierarchical analysis method was used with Matlab software. Based on the grey relational degree obtained from grey relational analysis, a judgment matrix for the fracturability evaluation index was established, and the weight coefficients of each factor were obtained. Analysis showed that the weight coefficients for the brittleness index, fracture toughness, uniaxial compressive strength, and tensile strength of the shale sample in this embodiment were 0.39, 0.33, 0.19, and 0.08, respectively. Therefore, the established fracturability index evaluation model is shown in formula (5).

[0102] FI = 0.39B + 0.33K ic +0.19UCS +0.08S t (5)

[0103] Where B is the brittleness index, measured in dimensionless units; K ic Fracture toughness, unit: UCS stands for uniaxial compressive strength, measured in MPa; S t Tensile strength, measured in MPa.

[0104] S5. Verify model reliability

[0105] By analyzing the intersection of the number of microseismic events and the fracturability index, the correlation between the fracturability evaluation index and the number of microseismic events was calculated, verifying the reliability of the fracturability index evaluation model. The relationship between the number of microseismic events and the fracturability index in this embodiment is as follows: Figure 10 As shown, from Figure 10 As can be seen, the calculated fracturability evaluation index is positively correlated with the number of microseismic events, and the correlation R0 is... 2A value greater than 0.6 indicates that the constructed fracturability index evaluation model is reliable.

[0106] Currently, this invention has been practically applied in a block in the Sichuan Basin. The correlation between the shale fracturing index and microseismic events is better than that between the brittleness index and microseismic events. Compared with the traditional evaluation using only the brittleness index, the shale fracturing index can more effectively evaluate the fracturing of shale reservoirs, and the evaluation results are more reliable and comprehensive.

[0107] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is impossible to exhaustively list all embodiments here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A method for predicting the fracturability of shale based on rock physics experiments and associated fracturing, characterized in that, The establishment of this prediction method includes the following steps performed sequentially: S1. Establishing a prediction model for rock mechanical parameters of shale formations using multivariate statistical methods. S11. Sample collection, sample preparation, and basic data processing Shale samples were collected and prepared into test specimens. At the same time, core data, geological data, and well logging data were collected and organized. S12. Rock Acoustic Wave and Mechanical Simulation Acoustic and mechanical simulations were performed on shale, and triaxial compression numerical models, Brazilian splitting numerical models, and Brazilian disk numerical models of shale were established respectively. S13. Determining rock mechanical parameters Acoustic measurements were performed on shale samples, and rock mechanical parameters were determined based on three indoor experiments: triaxial compression test, Brazilian splitting test, and Brazilian disk test. S14. Establish a rock mechanics parameter prediction model Based on the combined results of numerical simulation experiments and laboratory experiments, a rock mechanics parameter prediction model was established. S2. Draw well logging diagrams and obtain rock mechanics parameters for the horizontal section. S3. Using the Analytic Hierarchy Process (AHP) to identify the main controlling factors of shale fracturing ability. Step S3 includes the following steps: Using the number of microseismic events as a reference sequence, and four indicators—brittleness index, fracture toughness, uniaxial compressive strength, and tensile strength—as factor sequences, the grey relational analysis method was used to obtain the grey relational degree of each factor. S4. Use the analytic hierarchy process (AHP) to obtain the weight coefficients of each influencing factor, and then establish a fracturability index evaluation model. Step S4 includes the following steps: Based on the hierarchical analysis method, a fracturability evaluation index judgment matrix is ​​established according to the gray relational degree, the weight coefficients of each factor are obtained, and then a fracturability index evaluation model is established. The fracturability index evaluation model is expressed as follows: FI=W1B+W2K ic +W3UCS+W4S t Where W1~W4 are weighting coefficients ranging from 0 to 1, dimensionless; B is the fragility index, with units of dimensionless; K ic Fracture toughness, unit: UCS stands for uniaxial compressive strength, measured in MPa; S t Tensile strength, expressed in MPa; S5. Verify the reliability of the model.

2. The method for predicting the fracturability of shale based on rock physics experiments and associated fracturing as described in claim 1, characterized in that, Step S2 includes the following steps: Based on the rock mechanics parameter prediction model and well logging data, well logging diagrams are drawn to obtain the rock mechanics parameters of the horizontal section.

3. The method for predicting shale fracturability based on rock physics experiments and associated fracturing, as described in any one of claims 1 or 2, is characterized in that... Step S5 includes the following steps: By analyzing the intersection of the number of microseismic events and the fracturability index, the correlation between the fracturability evaluation index and the number of microseismic events was calculated, thus verifying the reliability of the fracturability index evaluation model.

4. The method for predicting shale fracturability based on rock physics experiments and associated fracturing as described in claim 3, characterized in that, In step S5, the fracturability evaluation model constructed when the fracturability evaluation index is positively correlated with the number of microseismic events has reliability.

5. The method for predicting shale fracturability based on rock physics experiments and associated fracturing as described in claim 3, characterized in that, The samples in step S11 include standard cylindrical samples and Brazilian disk samples; the logging data includes density data, P-wave data, S-wave data, and microseismic monitoring data. The acoustic wave measurement method in step S12 is the acoustic wave transmission method; In step S13, the triaxial compression test measures the uniaxial compressive strength, elastic modulus, and Poisson's ratio, and calculates the brittleness index; the Brazilian splitting test measures the tensile strength of the rock sample; and the Brazilian disc test measures the fracture toughness of the rock sample.

6. The method for predicting shale fracturability based on rock physics experiments and associated fracturing as described in claim 4 or 5, characterized in that, Step S14 includes the following sub-steps: S141. Based on the combined results of numerical simulation experiments and indoor experiments, the least squares method is used to establish a tensile strength prediction model based on transverse wave time difference. S142. Based on the combined results of numerical simulation and indoor experiments, the least squares method is used to establish a fracture toughness prediction model based on transverse wave time difference and longitudinal wave time difference. S143. Based on the combined results of numerical simulation and indoor experiments, the least squares method is used to establish a uniaxial compressive strength prediction model based on the transverse wave time difference and the longitudinal wave time difference. S144. Based on the combined results of numerical simulation and indoor experiments, a brittleness index prediction model based on dynamic elastic modulus is established using the least squares method.

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