A method for evaluating formation fracture parameters using array lateral logging

Abstract

The present invention discloses a method for evaluating formation fracture parameters by array lateral logging, including: S1: qualitatively identifying fracture positions based on the change characteristics of resistivity measurement values ​​in the deep detection mode of array lateral logging; S2: extracting resistivity measurement values ​​of each detection mode of array lateral logging at the identified fracture positions, and calculating fracture dip information according to the resistivity measurement values; S3: constructing an array lateral logging fracture porosity response chart using numerical simulation technology based on the calculated fracture dip and different mud resistivity conditions; S4: extracting apparent resistivity and mud resistivity data of deep detection of array lateral logging of actual oil and gas reservoirs, and interpolating reservoir fracture porosity information based on the array lateral logging fracture porosity response chart. The present invention constructs a fracture parameter quantitative evaluation model based on array lateral logging based on the response characteristics of array lateral logging of reservoirs with different fracture parameters, which can provide a theoretical reference for array lateral logging evaluation of fracture reservoirs.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and more specifically, to an array lateral logging formation fracture parameter evaluation method. Background Art

[0002] Fracture reservoirs have a variety of storage spaces such as pores, fractures and their combinations, and are characterized by obvious anisotropy and strong heterogeneity. Compared with seismic and geological data, logging data has the characteristics of high resolution. Dual lateral logging has a strong current focusing ability and is widely used in the identification and evaluation of fractures. However, dual lateral logging has limitations in the evaluation of fractures in reservoirs with complex pore structures. Array lateral logging is a new technology that is improved based on dual lateral logging. It solves the defects of conventional three-lateral and dual lateral logging resolution, has a strong characterization capability for abnormal geological bodies such as wellside fractures, can obtain rich resistivity measurement data, and is more suitable for the evaluation of fractures. Array lateral logging has become one of the important means of characterizing and evaluating fracture reservoirs.

[0003] However, the current evaluation of fracture parameters by array lateral logging is still at the level of qualitative identification and characterization, and a quantitative evaluation model and method for fracture parameters has not yet been formed. Summary of the invention

[0004] In order to overcome the problem that the prior art lacks a quantitative evaluation method for fracture parameters, the present invention provides an array lateral logging formation fracture parameter evaluation method.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for evaluating formation fracture parameters by array lateral logging, comprising the following steps:

[0006] S1: Qualitatively identify fracture locations based on the resistivity measurement value variation characteristics of array lateral logging deep detection mode;

[0007] S2: At the identified fracture location, extract the resistivity measurement values ​​of each detection mode of the array lateral logging, and calculate the fracture dip information based on the resistivity measurement values;

[0008] S3: Based on the calculated fracture dip angle and different mud resistivity conditions, the array laterolog fracture porosity response chart is constructed using numerical simulation technology;

[0009] S4: Extract the actual oil and gas reservoir array lateral logging deep detection apparent resistivity and mud resistivity data, and interpolate the reservoir fracture porosity information based on the array lateral logging fracture porosity response map.

[0010] Preferably, in step S1, the crack discrimination condition is:

[0011] MRL4(i) <Rb (1); MRL4(i) - MRL4(i - 1) < 0 (2);

[0012] MRL4(i + 1) - MRL4(i) > 0 (3), where MRL4 is the resistivity measurement value of the deep detection mode of the array laterolog, with the unit of Ω·m; R b is the resistivity of the bedrock, with the unit of Ω·m; i is the depth, with the unit of m.

[0013] Preferably, in the step S2, the fracture dip angle MRL1 are respectively the resistivity measurement values of the deep and shallow detection modes of the array laterolog, with the unit of Ω·m.

[0014] Preferably, in the step S2, when Y < 0, the fracture dip angle is less than 50 degrees, which is a low-angle fracture; when 0 < Y < 0.1, the fracture dip angle ranges from 50 degrees to 74 degrees, which is an inclined fracture; when Y > 0.1, the fracture dip angle is greater than 74 degrees, which is a high-angle fracture.

[0015] Preferably, in the step S3, according to the fracture dip angle information obtained in the step S2, simulate the array laterolog response characteristics under different mud resistivity conditions, and construct an array laterolog fracture porosity response chart for the corresponding dip angle.

[0016] Preferably, in the step S4, if there is a response curve corresponding to the mud resistivity in the chart, then calculate the ratio of the apparent resistivity R a of the array laterolog to the mud resistivity R m , and calculate the fracture porosity according to the array laterolog fracture porosity response chart; if there is no response curve corresponding to the mud resistivity in the chart, then select the two curves with the mud resistivity closest to the actual mud resistivity value of the oil and gas reservoir in the chart for linear interpolation, and calculate the ratio of the apparent resistivity of the array laterolog to the mud resistivity according to the array laterolog fracture porosity response curve corresponding to the actual mud resistivity value obtained by interpolation, and then calculate the fracture porosity.

[0017] Preferably, the method of the linear interpolation is as follows:

[0018] First, to obtain the value of the unknown function f at P(x, y), assume that the values of the known function f at Q 11 (x1, y1), Q 12 (x1, y2), Q 21 (x2, y1), Q 22 (x2, y2) are known;

[0019] Then, let P(x, y), R1(x, y1), R2(x, y2), and interpolate from two directions respectively:

[0020]

[0021]

[0022] Another aspect of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0023] Preferably, the memory is a memory composed of semiconductor devices or a memory made of magnetic materials.

[0024] Another aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program, and when the program is executed by a processor, the processor executes the steps of the method described above.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows: based on the array lateral logging response characteristics of reservoirs with different fracture parameters, the present invention constructs a fracture parameter quantitative evaluation model based on array lateral logging, which can provide a theoretical reference for the evaluation of array lateral logging in fractured reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of the array lateral logging formation fracture parameter evaluation method of the present invention;

[0027] Figure 2 is a schematic diagram of crack identification in Example 5 of the present invention;

[0028] Figure 3 is the calculation result of the fracture parameters of the actual well logging data in Example 5 of the present invention;

[0029] Figure 4 It is the array laterolog fracture porosity response chart in Example 5 of the present invention;

[0030] Figure 5 is a flow chart of interpolation calculation in Embodiment 5 of the present invention;

[0031] Figure 6 It is a schematic diagram of the interpolation calculation of fracture porosity in the lateral MRL4 mode of the actual logging data array in Example 5 of the present invention. DETAILED DESCRIPTION

[0032] The drawings are only for illustrative purposes and cannot be construed as limiting the present invention. To better illustrate the present embodiment, some parts of the drawings may be omitted, enlarged, or reduced, and do not represent the size of the actual product. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the drawings. The positional relationships described in the drawings are only for illustrative purposes and cannot be construed as limiting the present invention.

[0033] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "long", "short" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limitations on this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0034] The technical solution of the present invention is further described in detail below through specific embodiments and in conjunction with the accompanying drawings:

[0035] Example 1

[0036] like Figure 1 As shown, a method for evaluating formation fracture parameters by array lateral logging comprises the following steps:

[0037] S1: Qualitatively identify fracture locations based on the resistivity measurement value variation characteristics of array lateral logging deep detection mode;

[0038] S2: At the identified fracture location, extract the resistivity measurement values ​​of each detection mode of the array lateral logging, and calculate the fracture dip information based on the resistivity measurement values;

[0039] S3: Based on the calculated fracture dip angle and different mud resistivity conditions, the array laterolog fracture porosity response chart is constructed using numerical simulation technology;

[0040] S4: Extract the actual oil and gas reservoir array lateral logging deep detection apparent resistivity and mud resistivity data, and interpolate the reservoir fracture porosity information based on the array lateral logging fracture porosity response map.

[0041] Example 2

[0042] A method for evaluating formation fracture parameters by array lateral logging comprises the following steps:

[0043] S1: Qualitatively identify fracture locations based on the resistivity measurement value variation characteristics of array lateral logging deep detection mode;

[0044] S2: At the identified fracture location, extract the resistivity measurement values ​​of each detection mode of the array lateral logging, and calculate the fracture dip information based on the resistivity measurement values;

[0045] S3: Based on the calculated fracture dip angle and different mud resistivity conditions, the array laterolog fracture porosity response chart is constructed using numerical simulation technology;

[0046] S4: Extract the actual oil and gas reservoir array lateral logging deep detection apparent resistivity and mud resistivity data, and interpolate the reservoir fracture porosity information based on the array lateral logging fracture porosity response map.

[0047] Wherein, in step S1, the crack discrimination condition is:

[0048] MRL4(i) <R b (1); MRL4(i)-MRL4(i-1)<0 (2);

[0049] MRL4(i+1)-MRL4(i)>0(3), where MRL4 is the resistivity measurement value of array lateral logging deep detection mode, in Ω.m; R b is the bedrock resistivity, in Ω.m; i is the depth, in m.

[0050] Example 3

[0051] A method for evaluating formation fracture parameters by array lateral logging comprises the following steps:

[0052] S1: Qualitatively identify fracture locations based on the resistivity measurement value variation characteristics of array lateral logging deep detection mode;

[0053] S2: At the identified fracture location, extract the resistivity measurement values ​​of each detection mode of the array lateral logging, and calculate the fracture dip information based on the resistivity measurement values;

[0054] S3: Based on the calculated fracture dip angle and different mud resistivity conditions, the array laterolog fracture porosity response chart is constructed using numerical simulation technology;

[0055] S4: Extract the actual oil and gas reservoir array lateral logging deep detection apparent resistivity and mud resistivity data, and interpolate the reservoir fracture porosity information based on the array lateral logging fracture porosity response map.

[0056] Wherein, in step S1, the crack discrimination condition is:

[0057] MRL4(i) <R b(1); MRL4(i) - MRL4(i - 1) < 0 (2);

[0058] MRL4(i + 1) - MRL4(i) > 0(3), where MRL4 is the resistivity measurement value in the deep detection mode of the array laterolog, with the unit of Ω·m; R b is the resistivity of the bedrock, with the unit of Ω·m; i is the depth, with the unit of m.

[0059] In addition, in the step S2, the fracture dip angle MRL4 and MRL1 are respectively the resistivity measurement values in the deep and shallow detection modes of the array laterolog, with the unit of Ω·m.

[0060] Among them, in the step S2, when Y < 0, the fracture dip angle is less than 50 degrees, which is a low-angle fracture; when 0 < Y < 0.1, the fracture dip angle ranges between 50 degrees and 74 degrees, which is an inclined fracture; when Y > 0.1, the fracture dip angle is greater than 74 degrees, which is a high-angle fracture.

[0061] In addition, in the step S3, according to the fracture dip angle information obtained in the step S2, simulate the array laterolog response characteristics under different mud resistivity conditions, and construct an array laterolog fracture porosity response chart for the corresponding dip angle.

[0062] Example 4

[0063] An evaluation method for formation fracture parameters of an array laterolog, comprising the following steps:

[0064] S1: Qualitatively identify the fracture position based on the change characteristics of the resistivity measurement value in the deep detection mode of the array laterolog;

[0065] S2: At the identified fracture position, extract the resistivity measurement values of each detection mode of the array laterolog, and calculate the fracture dip angle information based on the resistivity measurement values;

[0066] S3: Based on the calculated fracture dip angle, construct an array laterolog fracture porosity response chart using numerical simulation technology under different mud resistivity conditions;

[0067] S4: Extract the deep detection apparent resistivity and mud resistivity data of the actual oil and gas reservoir array laterolog, and interpolate the reservoir fracture porosity information based on the array laterolog fracture porosity response chart.

[0068] Among them, in the step S4, if there is a corresponding response curve of the mud resistivity in the chart, then calculate the array laterolog apparent resistivity R a and the mud resistivity R mThe fracture porosity is calculated according to the array lateral logging fracture porosity response chart; if there is no corresponding mud resistivity response curve in the chart, the two curves closest to the mud resistivity value in the chart and the actual oil and gas reservoir mud resistivity value are selected for linear interpolation, and the ratio of array lateral logging apparent resistivity to mud resistivity is calculated according to the array lateral logging fracture porosity response curve corresponding to the actual oil and gas reservoir mud resistivity value obtained by interpolation, and then the fracture porosity is calculated.

[0069] In addition, the linear interpolation method is as follows:

[0070] First, to find the value of the unknown function f at P(x,y), assume that the known function f at Q 11 (x1,y1),Q 12 (x1,y2),Q 21 (x2,y1),Q 22 The value of (x2,y2);

[0071] Then, let P(x,y), R1(x,y1), R2(x,y2), and interpolate from two directions:

[0072]

[0073] Example 5

[0074] An array lateral logging formation fracture parameter evaluation method comprises the following steps:

[0075] Step 1: Based on the change characteristics of the resistivity measurement value in the array lateral logging deep detection mode, the fracture location is qualitatively identified. The fracture identification condition is: MRL4(i) <R b (1); MRL4(i)-MRL4(i-1)<0(2); MRL4(i+1)-MRL4(i)>0(3). Where MRL4 is the resistivity measurement value of array lateral logging deep detection mode, in Ω.m; R b is the bedrock resistivity, in Ω.m; i is the depth, in m. Figure 2 The following is a schematic diagram of fracture identification. Based on actual well data, a study on reservoir fracture parameter evaluation was conducted. The fracture reservoir was evaluated using array lateral logging data combined with imaging logging fracture parameter evaluation results. Figure 3 The fracture characteristic parameters are used to evaluate the effect of fracture reservoir. Figure 3 As shown in the figure, in the depth section from 3582m to 3600m, the resistivity measurement values ​​in the array lateral logging deep detection mode have obvious variation characteristics, and the imaging logging indicates that obvious fractures are developed in the reservoir.

[0076] Step 2: At the identified fracture position, extract the resistivity measurement values ​​of each detection mode of the array lateral logging, and calculate the fracture dip information: According to the identified fracture position, extract the resistivity measurement values ​​of each detection mode of the array lateral logging to calculate the fracture dip:

[0077]

[0078] Where, MRL4 and MRL1 are the resistivity measurement values ​​of deep and shallow detection modes of array lateral logging, respectively, in Ω.m. The upper No. 1 layer and the lower No. 2 layer are calculated to be mainly less than zero, and the fracture dip angle is less than 50 degrees, which is mainly low-angle fractures.

[0079] Step 3, simulate the array laterolog response characteristics under different mud resistivity conditions when the fracture dip angle is low, and construct the array laterolog fracture porosity response plate of the corresponding dip angle, such as Figure 4 shown.

[0080] Step 4: According to the ratio of the apparent resistivity of the array lateral logging to the mud resistivity, the fracture porosity is interpolated and calculated in the corresponding map: the mud mineralization information is converted based on the wellhead mud resistivity data, and the mud resistivity value is converted to 0.025Ω.m according to the current layer depth. According to the apparent resistivity values ​​of the array lateral logging of layer segments 1 and 2, the apparent resistivity and mud resistivity ratio information required for interpolation in the map is calculated. Figure 5 Flowchart for interpolation calculation. Figure 6 Schematic diagram of interpolating the array laterolog mode to obtain fracture porosity for the No. 1 and No. 2 intervals with the same fracture dip angles. Figure 6 There is no response curve with a mud resistivity of 0.025Ω.m in the figure. The two curves of mud resistivity of 0.0316Ω.m and 0.01Ω.m in the figure are used to interpolate the values ​​of layer 1 and layer 2 respectively. Figure 6 As shown in the horizontal axis corresponding to the dotted arrow on the right side of the figure, the final interpolation calculation shows that the fracture porosity of layer 1 is 0.3%, and the fracture porosity of layer 2 is 0.02%. The change trend of the calculated results is consistent with the change trend of the fracture porosity extracted by imaging logging.

[0081] Example 6

[0082] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the following method are implemented, the steps being:

[0083] S1: Qualitatively identify fracture locations based on the resistivity measurement value variation characteristics of array lateral logging deep detection mode;

[0084] S2: At the identified fracture location, extract the resistivity measurement values ​​of each detection mode of the array lateral logging, and calculate the fracture dip information based on the resistivity measurement values;

[0085] S3: Based on the calculated fracture dip angle and different mud resistivity conditions, the array laterolog fracture porosity response chart is constructed using numerical simulation technology;

[0086] S4: Extract the actual oil and gas reservoir array lateral logging deep detection apparent resistivity and mud resistivity data, and interpolate the reservoir fracture porosity information based on the array lateral logging fracture porosity response map.

[0087] Wherein, the memory is a memory composed of semiconductor devices or a memory made of magnetic materials.

[0088] Example 7

[0089] A computer-readable storage medium stores a program. When the program is executed by a processor, the processor performs the steps of the method described above. The steps are:

[0090] S1: Qualitatively identify fracture locations based on the resistivity measurement value variation characteristics of array lateral logging deep detection mode;

[0091] S2: At the identified fracture location, extract the resistivity measurement values ​​of each detection mode of the array lateral logging, and calculate the fracture dip information based on the resistivity measurement values;

[0092] S3: Based on the calculated fracture dip angle and different mud resistivity conditions, the array laterolog fracture porosity response chart is constructed using numerical simulation technology;

[0093] S4: Extract the actual oil and gas reservoir array lateral logging deep detection apparent resistivity and mud resistivity data, and interpolate the reservoir fracture porosity information based on the array lateral logging fracture porosity response map.

[0094] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for evaluating formation fracture parameters by array lateral logging, characterized in that: Including the following steps: S1: Qualitatively identify the fracture location based on the variation characteristics of the resistivity measurement values in the deep detection mode of the array laterolog; S2: At the identified fracture location, extract the resistivity measurement values of each detection mode of the array laterolog, and calculate the fracture dip angle information based on the resistivity measurement values; S3: Based on the calculated fracture dip angle, construct an array laterolog fracture porosity response chart using numerical simulation technology under different mud resistivity conditions; S4: Extract the deep detection apparent resistivity and mud resistivity data of the actual oil and gas reservoir array laterolog, and interpolate the reservoir fracture porosity information based on the array laterolog fracture porosity response chart.

2. The array lateral logging formation fracture parameter evaluation method according to claim 1, characterized in that: In the step S1, the fracture discrimination condition is: MRL4(i) <R b (1); MRL4(i)-MRL4(i-1)<0 (2); MRL4(i+1)-MRL4(i)>0(3), where MRL4 is the resistivity measurement value of array lateral logging deep detection mode, in Ω.m; R b is the bedrock resistivity, in Ω.m; i is the depth, in m.

3. The array lateral logging formation fracture parameter evaluation method according to claim 2, characterized in that: In step S2, the crack inclination MRL4 and MRL1 are the resistivity measurement values ​​of array lateral logging in deep and shallow detection modes, respectively, in Ω.m.

4. The array lateral logging formation fracture parameter evaluation method according to claim 3, characterized in that: In the step S2, when Y < 0, the fracture dip angle is less than 50 degrees, which is a low-angle fracture; when 0 < Y < 0.1, the fracture dip angle ranges from 50 degrees to 74 degrees, which is an inclined fracture; when Y > 0.1, the fracture dip angle is greater than 74 degrees, which is a high-angle fracture.

5. The array lateral logging formation fracture parameter evaluation method according to claim 1, characterized in that: In the step S3, according to the fracture dip angle information obtained in step S2, simulate the array laterolog response characteristics under different mud resistivity conditions, and construct an array laterolog fracture porosity response chart corresponding to the dip angle.

6. The array lateral logging formation fracture parameter evaluation method according to claim 1, characterized in that: In step S4, if there is a corresponding mud resistivity response curve in the chart, the array laterolog apparent resistivity R is calculated. a and mud resistivity R m The fracture porosity is calculated according to the array lateral logging fracture porosity response chart; if there is no corresponding mud resistivity response curve in the chart, the two curves closest to the mud resistivity value in the chart and the actual oil and gas reservoir mud resistivity value are selected for linear interpolation, and the ratio of array lateral logging apparent resistivity to mud resistivity is calculated according to the array lateral logging fracture porosity response curve corresponding to the actual oil and gas reservoir mud resistivity value obtained by interpolation, and then the fracture porosity is calculated.

7. The array lateral logging formation fracture parameter evaluation method according to claim 6, characterized in that: The method of the linear interpolation is as follows: First, to find the value of the unknown function f at P(x,y), assume that the known function f at Q 11 (x1,y1),Q 12 (x1,y2),Q 21 (x2,y1),Q 22 The value of (x2,y2); Then, set P(x, y), R1(x, y1), R2(x, y2), and perform interpolation from two directions respectively:

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

9. The computer device according to claim 8, characterized in that The memory is a memory composed of semiconductor devices or a memory made of magnetic materials.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, and when the program is executed by the processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 7.

Images