A quantitative prediction method for the distribution of lamellae

Abstract

The present invention provides a quantitative prediction method for the development and distribution of lamellae fractures, which is applicable to the field of shale oil and gas geology. Based on the drilling core and cast thin sections, the distribution depth and development degree of macro and micro lamellae fractures are determined respectively, and the response characteristics of the logging curves are found out, and sensitivity analysis is performed. The logging curves with strong responses are selected, and the weights and thresholds of macro and micro lamellae fractures are obtained repeatedly under the constraints of the measured lamellae fracture data. The macro and micro lamellae fracture development and distribution indicators of the full depth section of a single well are calculated to achieve quantitative prediction of the development and distribution of lamellae fractures in a single well. The present invention has clear principles, strong operability, and high credibility, and can be used to guide the improvement of the efficiency of shale oil and gas exploration and development.

Description

Technical Field

[0001] The invention relates to a quantitative prediction method for the development and distribution of lamellae fractures, and is particularly applicable to the field of shale oil and gas geology. Background Art

[0002] my country has abundant shale oil and gas resources, with a wide distribution range and great development potential. It is currently the main area of ​​unconventional oil and gas exploration and development. Shale reservoirs have developed natural fractures, which are important storage spaces and seepage channels for oil and gas, and seriously affect the enrichment and accumulation of shale oil and gas. Bedding fractures are a common and special type of natural fractures in shale reservoirs. They mainly refer to fractures formed during the sedimentation and diagenesis process and parallel to the bedding layers. They have an important impact on shale oil and gas exploration and development.

[0003] The invention patent with the authorization announcement number CN109870743B proposes a method and device for predicting the spatial distribution of lamellae fractures, which takes the longitudinal distribution of shale fracture phases in a single well as a constraint, establishes an equation with the shale fracture phase index as the objective function, and predicts the spatial distribution of lamellae fractures based on it; the invention patent with the authorization announcement number CN110850502B provides a method, device and system for predicting the distribution law of lamellae fractures in shale oil and gas reservoirs. The patent obtains the development intensity of lamellae fractures in a single well, establishes its relationship with the main controlling factors of lamellae fractures, and predicts the distribution law of lamellae fractures in shale oil and gas reservoirs based on this relationship. The above methods mainly predict lamellae fractures from the perspective of the main controlling factors of lamellae fracture development, and neither of them distinguishes between macro lamellae fractures and micro lamellae fractures, nor highlights the response of logging curves to lamellae fractures, resulting in poor prediction accuracy. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention provides a quantitative prediction method for the development and distribution of lamellae with simple steps and high reliability, the core of which is to determine the prediction weights and thresholds of macroscopic lamellae and microscopic lamellae.

[0005] In order to achieve the above objectives, a quantitative prediction method for the development and distribution of lamellae fractures of the present invention is provided. In the detection area, drilling cores are obtained by drilling, and casting thin sections are made by using the drilling cores. The distribution depth and development degree of macroscopic lamellae fractures in the detection area are determined by using the drilling cores, and the distribution depth and development degree of microscopic lamellae fractures in the detection area are determined by using casting thin sections. Then, the logging curve response characteristics of the macro- and micro-lamellae fracture development depth section are obtained through logging data, and sensitivity analysis is carried out for the macro- and micro-lamellae fracture responses, and the logging curves with strong responses are preferred. The preferred logging curves are standardized, and the weights and thresholds of the macro- and micro-lamellae fractures are repeatedly calculated under the constraints of the measured lamellae fracture data. Finally, the weights and thresholds of the macro- and micro-lamellae fractures are used to calculate the macro- and micro-lamellae fracture development and distribution indicators of the full depth section of a single well, respectively, to achieve quantitative prediction of the development and distribution of lamellae fractures in a single well in the detection area.

[0006] The specific steps are as follows:

[0007] Step 1, obtain drilling core data, identify and determine the distribution depth and development degree of macroscopic foliation fractures at a scale of fracture aperture greater than 10 microns, and record it as data sample H;

[0008] Step 2, using the drilling core obtained in step 1, prepare a casting thin section, identify and determine the distribution depth and development degree of micro-laminated fractures at a scale of fracture opening less than or equal to 10 microns, and record it as data sample W;

[0009] Step 3, obtain logging data, use the logging data to determine the logging curve response characteristics of the lamellae fracture depth section in steps 1 and 2, conduct sensitivity analysis of all logging curves to macroscopic lamellae fractures and microscopic lamellae fractures, select the five logging curves with the strongest response, and use the following formula for standardization:

[0010]

[0011] Where: z represents any preferred well logging curve data body, z i is the data value of the optimal logging curve data volume z, z max and z min represent the maximum and minimum values ​​in the preferred well logging curve data volume z, respectively, and Z is the standardized well logging curve data volume of z;

[0012] Step 4: Take the five standardized logging data with the strongest response to macroscopic lamella fractures as the initial known parameters A = (a 1 , a 2 , a 3 , a 4 , a 5 ), let S = (s 1 ,s 2 ,s 3 ,s 4 ,s 5 ) is the macroscopic starting known parameter A=(a 1 , a 2 , a 3 , a 4 , a 5 ) corresponding weight, let α be the threshold of A, and process A according to the following formula and record it as the macro transformation parameter C:

[0013]

[0014] Step 5: Take the five standardized logging data with the strongest response to microscopic lamella fractures as the initial known parameters B = (b 1 , b 2 , b3 , b 4 , b 5 ), let T = (t 1 , t 2 , t 3 , t 4 , t 5 ) is the microscopic starting known parameter B=(b 1 , b 2 , b 3 , b 4 , b 5 ) corresponding weight, let β be the threshold of B, and record B as the micro transformation parameter D after processing it according to the following formula:

[0015]

[0016] Step 6: Compare the macro transformation parameter C obtained in step 4 with the macro foliation data sample H obtained in step 1, and compare the micro transformation parameter D obtained in step 5 with the micro foliation data sample W obtained in step 2. Assume that the deviation P = |CH| / H = |DW| / W. If P>0.1, then modify the weights and thresholds in steps 4 and 5 based on the experience of the deviation summary of multiple attempts, recalculate and obtain new macro transformation parameters and micro transformation parameters, repeat the above process until P≤0.1, and record the macro foliation weight S at this time respectively. f With threshold α f , Micro-page crack weight T f With threshold β f ;

[0017] Step 7: Based on the standardized logging curve data obtained in step 3 and the macroscopic and microscopic lamellae fracture weights and thresholds for the well obtained in step 6, the following formula is used to calculate the macroscopic lamellae fracture development distribution index H of the full depth section of the single well: f , microscopic lamellar fracture development and distribution index W f :

[0018]

[0019]

[0020] Ultimately, quantitative prediction of lamellae development and distribution can be achieved.

[0021] Furthermore, the degree of lamellae development in step 1 and step 2 is characterized by the lamellae line density, that is, the number of lamellae per unit length. The number of macroscopic lamellae obtained in step 1 is no less than 20, and the number of microscopic lamellae obtained in step 2 is no less than 20.

[0022] Furthermore, the logging data in step 3 include well diameter, acoustic wave time difference, natural gamma, natural potential, deep lateral resistivity, shallow lateral resistivity, compensated neutron and density curve information, and the logging data extraction interval is 0.025 meters.

[0023] Beneficial effects: Based on drilling cores and cast thin sections, this method can determine the distribution depth and development degree of macro- and micro-laminated fractures, find out the response characteristics of their logging curves, and conduct sensitivity analysis. Five logging curves with strong responses are selected respectively. Under the constraints of the measured laminated fracture data, the weights and thresholds of macro- and micro-laminated fractures are repeatedly calculated, and the development and distribution indicators of macro- and micro-laminated fractures in the full depth section of a single well are calculated, so as to realize the quantitative prediction of the development and distribution of laminated fractures in a single well. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the process of the quantitative prediction method of lamellae development distribution of the present invention. DETAILED DESCRIPTION

[0025] The embodiments of the present invention are further described below in conjunction with the accompanying drawings:

[0026] like Figure 1 As shown, the quantitative prediction method for the development and distribution of lamellae fractures of the present invention uses drilling to obtain drilling cores in the detection area, and uses the drilling cores to make casting thin sections, and uses the drilling cores to determine the distribution depth and development degree of macroscopic lamellae fractures in the detection area, and uses the casting thin sections to determine the distribution depth and development degree of microscopic lamellae fractures in the detection area; then, the logging curve response characteristics of the macro- and micro-lamellae fracture development depth section are obtained through logging data, and sensitivity analysis is carried out for the macro- and micro-lamellae fracture responses, and the logging curves with strong responses are preferred, and the preferred logging curves are standardized, and the weights and thresholds of the macro- and micro-lamellae fractures are repeatedly calculated under the constraints of the measured lamellae fracture data; finally, the weights and thresholds of the macro- and micro-lamellae fractures are used to calculate the macro- and micro-lamellae fracture development distribution indicators of the full depth section of a single well, so as to achieve quantitative prediction of the development and distribution of lamellae fractures in a single well in the detection area.

[0027] The specific steps are as follows:

[0028] Step 1, obtain drilling core data, identify and determine the distribution depth and development degree of macroscopic foliation fractures at a scale of fracture opening greater than 10 microns, and record it as data sample H;

[0029] Step 2, using the drilling core obtained in step 1, prepare a casting thin section, identify and determine the distribution depth and development degree of micro-laminated fractures at a scale of fracture opening less than or equal to 10 microns, and record it as data sample W;

[0030] The degree of lamination development is characterized by the lamination line density, that is, the number of laminations per unit length. The number of macro and micro laminations is not less than 20.

[0031] Step 3, obtain logging data, which includes well diameter, acoustic wave time difference, natural gamma, natural potential, deep lateral resistivity, shallow lateral resistivity, compensated neutron and density curve information, and the logging data extraction interval is 0.025 meters; use the logging data to determine the logging curve response characteristics of the lamellae fracture depth section in steps 1 and 2, and conduct sensitivity analysis of all logging curves to macro lamellae fractures and micro lamellae fractures respectively, select the five logging curves with the strongest response respectively, and use the following formula for standardization:

[0032]

[0033] Where: z represents any preferred well logging curve data body, z i is the data value of the optimal logging curve data volume z, z max and z min represent the maximum and minimum values ​​in the preferred well logging curve data volume z, respectively, and Z is the standardized well logging curve data volume of z;

[0034] Step 4: Take the five standardized logging data with the strongest response to macroscopic lamella fractures as the initial known parameters A = (a 1 , a 2 , a 3 , a 4 , a 5 ), let S = (s 1 ,s 2 ,s 3 ,s 4 ,s 5 ) is the macroscopic starting known parameter A=(a 1 , a 2 , a 3 , a 4 , a 5 ) corresponding weight, let α be the threshold of A, and process A according to the following formula and record it as the macro transformation parameter C:

[0035]

[0036] Step 5: Take the five standardized logging data with the strongest response to microscopic lamella fractures as the initial known parameters B = (b 1 , b 2 , b 3 , b 4 , b 5 ), let T = (t 1 , t 2 , t 3 , t4 , t 5 ) is the microscopic starting known parameter B=(b 1 , b 2 , b 3 , b 4 , b 5 ) corresponding weight, let β be the threshold of B, and record B as the micro transformation parameter D after processing it according to the following formula:

[0037]

[0038] Step 6: Compare the macro transformation parameter C obtained in step 4 with the macro foliation data sample H obtained in step 1, and compare the micro transformation parameter D obtained in step 5 with the micro foliation data sample W obtained in step 2. Assume that the deviation P = |CH| / H = |DW| / W. If P>0.1, then modify the weights and thresholds in steps 4 and 5 based on the experience of the deviation summary of multiple attempts, recalculate and obtain new macro transformation parameters and micro transformation parameters, repeat the above process until P≤0.1, and record the macro foliation weight S at this time respectively. f With threshold α f , Micro-page crack weight T f With threshold β f ;

[0039] Step 7: Based on the standardized logging curve data obtained in step 3 and the macroscopic and microscopic lamellae fracture weights and thresholds for the well obtained in step 6, the following formula is used to calculate the macroscopic lamellae fracture development distribution index H of the full depth section of the single well: f , microscopic lamellar fracture development and distribution index W f :

[0040]

[0041]

[0042] Ultimately, quantitative prediction of lamellae development and distribution can be achieved.

Claims

1. A quantitative prediction method for the distribution of lamellae development, characterized in that The specific steps are as follows: Step 1, obtain drilling core data, identify and determine the distribution depth and development degree of macroscopic foliation fractures at a scale of fracture aperture greater than 10 microns, and record it as data sample H; Step 2, using the drilling core obtained in step 1, prepare a casting thin section, identify and determine the distribution depth and development degree of micro-laminated fractures at a scale of fracture opening less than or equal to 10 microns, and record it as data sample W; Step 3, obtain logging data, use the logging data to determine the logging curve response characteristics of the lamellae fracture depth section in steps 1 and 2, conduct sensitivity analysis of all logging curves to macro lamellae fractures and micro lamellae fractures, select the five logging curves with the strongest response, and use the following formula for standardization: Where: z represents any selected well logging curve data volume, z i To select the data value of the well logging curve data volume z, z max and z min They represent the maximum and minimum values ​​in the selected well logging curve data volume z, respectively, and Z is the standardized well logging curve data volume of z; Step 4: Take the five standardized logging data with the strongest response to the macroscopic lamella fractures as the starting known parameters A = (a1, a2, a3, a4, a5), set S = (s1, s2, s3, s4, s5) as the corresponding weight of the macroscopic starting known parameters A = (a1, a2, a3, a4, a5), set α as the threshold of A, and record A as the macroscopic transformation parameter C after processing according to the following formula: Step 5, take the five standardized logging data with the strongest response to micro-lamellar fractures as the starting known parameters B = (b1, b2, b3, b4, b5), set T = (t1, t2, t3, t4, t5) as the corresponding weight of the micro-starting known parameters B = (b1, b2, b3, b4, b5), set β as the threshold of B, and record B as the micro-transformation parameter D after processing according to the following formula: Step 6: Compare the macro transformation parameter C obtained in step 4 with the macro foliation data sample H obtained in step 1, and compare the micro transformation parameter D obtained in step 5 with the micro foliation data sample W obtained in step 2. Assume that the deviation P = |CH| / H = |DW| / W. If P>0.1, then modify the weights and thresholds in steps 4 and 5 based on the experience of the deviation summary of multiple attempts, recalculate and obtain new macro transformation parameters and micro transformation parameters, repeat the above process until P≤0.1, and record the macro foliation weight S at this time respectively. f With threshold α f , Micro-page crack weight T f With threshold β f ; Step 7: Based on the standardized logging curve data obtained in step 3 and the macroscopic and microscopic lamellae fracture weights and thresholds for the well obtained in step 6, the following formula is used to calculate the macroscopic lamellae fracture development distribution index H of the full depth section of the single well: f , microscopic lamellar fracture development and distribution index W f： : Ultimately, quantitative prediction of lamellae development and distribution can be achieved.

2. A quantitative prediction method for the distribution of lamellae development according to claim 1, characterized in that: The degree of lamellae development in step 1 and step 2 is characterized by the lamellae line density, that is, the number of lamellae per unit length. The number of macroscopic lamellae obtained in step 1 is no less than 20, and the number of microscopic lamellae obtained in step 2 is no less than 20.

3. A quantitative prediction method for the distribution of lamellae development according to claim 1, characterized in that: The logging data in step 3 include wellbore diameter, acoustic wave time difference, natural gamma, natural potential, deep lateral resistivity, shallow lateral resistivity, compensated neutron and density curve information. The logging data extraction interval is 0.025 meters.

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