Design method for energy storage refracturing process parameters of integral block

Abstract

The invention relates to an energy storage refracturing process parameter design method for an integral block. The method comprises the following steps: S1, acquiring basic data of the integral block; s2, determining geological main control factors influencing the productivity according to the reservoir physical property data; s3, establishing a reservoir geologic model by using the basic data in the step S1, and dividing different reservoir types by using geologic main control factors; s4, for each reservoir type, a nine-point water injection development well group is selected, and the optimal water drive front position is determined; and S5, according to the optimal water drive front edge position in the step S4, fracturing parameters are optimized by taking well group productivity as a target. According to the method, the problem that water injection energy storage parameter and hydraulic fracturing parameter optimization methods are lacked in the energy storage and refracturing process of the whole block of the low-permeability reservoir is solved, the pertinence is higher, and refined optimization of different types of reservoirs of the whole block is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas field development engineering, in particular to a whole block energy storage and repeated fracturing process parameter design method. BACKGROUND

[0002] In the development process of low permeability reservoirs, there are often problems such as poor reservoir properties, low recovery degree, and rapid production decline. Hydraulic fracturing is one of the most widely used methods for improving tight reservoirs, but there are still problems of rapid production decline and insufficient reservoir energy after hydraulic fracturing. In order to solve the above problems, on the basis of traditional hydraulic fracturing technology, water injection energy storage is used to supplement reservoir energy and improve production.

[0003] At present, the optimization method of energy storage fracturing parameters only discloses a process parameter optimization method with production capacity as the target, and lacks optimization of energy storage process parameters in the repeated fracturing process and consideration of the water drive front position to prevent water channeling. SUMMARY

[0004] In order to overcome the problems in the prior art, the present application provides the following scheme to solve the problem of lack of water injection energy storage parameter and hydraulic fracturing parameter optimization method in the whole block energy storage and repeated fracturing process of low permeability reservoirs.

[0005] In a first aspect, the present application provides a whole block energy storage and repeated fracturing process parameter design method, characterized in that it comprises the following steps:

[0006] Step S1, obtaining basic data of the whole block;

[0007] Step S2, determining the main geological control factors affecting production capacity according to the reservoir property data;

[0008] Step S3, establishing a reservoir geological model using the basic data in step S1, and dividing different reservoir types using the main geological control factors;

[0009] Step S4, for each reservoir type, selecting a nine-point water injection development well group and determining the optimal water drive front position;

[0010] Step S5, optimizing the fracturing parameters with well group production capacity as the target according to the optimal water drive front position in step S4.

[0011] Further, the basic data in step S1 includes logging curve data, microseismic monitoring data, drilling data, and reservoir property data.

[0012] Further, the reservoir property data in step S2 includes oil saturation, oil layer thickness, porosity, and effective permeability.

[0013] Further, the step S2 adopts the Pearson correlation coefficient method to determine the geological main control factor.

[0014] Further, the step S4 adopts the following method to determine the optimal water drive front position: taking the injection-production distance between each oil well and the central injection well as a first parameter set, taking the horizontal fracture half-length of each oil well as a second parameter set, performing difference processing on the first parameter set and the second parameter set to obtain a third parameter set, and selecting the minimum value of the elements in the third parameter set as the optimal water drive front position.

[0015] Further, the horizontal fracture half-length is obtained through microseismic monitoring.

[0016] Further, the step S4 further comprises: taking the optimal water drive front position as a target to optimize the water injection and storage parameters.

[0017] Further, the step S4 comprises the injection speed, the injection time and the well shut-in time as the water injection and storage parameters.

[0018] Further, the orthogonal method is adopted to optimize the water injection and storage parameters.

[0019] Further, the step S5 comprises the horizontal fracture half-length and the fracture conductivity as the fracturing parameters.

[0020] Further, the orthogonal method is adopted to optimize the fracturing parameters.

[0021] In the second aspect, the present application further provides an electronic device, comprising a memory, a processor and a program stored in the memory and running on the processor, wherein the processor implements the steps of the whole block energy storage and repeated fracturing process parameter design method when the program is executed.

[0022] In the third aspect, the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores instructions, and when the instructions run on a terminal device, the terminal device executes the steps of the whole block energy storage and repeated fracturing process parameter design method.

[0023] The present application has the following advantages: (1) The present application optimizes the fracturing parameters of the whole block, classifies the whole block according to the geological main control factor, selects a typical well group for numerical simulation, and is more targeted, thereby realizing the fine optimization of different types of reservoirs in the whole block. (2) In the process of optimizing the energy storage parameters, the present application determines the oil well fracturing fracture range according to the microseismic data, optimizes the optimal water drive front position according to the principle of preventing water channeling, optimizes the energy storage parameters, and further optimizes the repeated fracturing parameters. BRIEF DESCRIPTION OF DRAWINGS

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0026] Figure 2 This is a diagram showing the location of the nine-point well network in an embodiment of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The purpose of this invention is to provide a method for designing process parameters for energy storage and repeated fracturing of an entire block. To make the above-mentioned objectives, features and advantages of this invention more apparent and understandable, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] like Figure 1 As shown, this invention provides a method for designing process parameters for energy storage and repeated fracturing of an entire block, comprising the following steps:

[0030] Step S1: Obtain the basic data of the entire block;

[0031] Step S2: Determine the main geological factors affecting production capacity based on reservoir physical property data;

[0032] Step S3: Use the basic data from step S1 to establish a reservoir geological model and use the main geological control factors to classify different reservoir types;

[0033] Step S4: For each reservoir type, select a nine-point water injection development well group to determine the optimal water drive front location;

[0034] Step S5: Based on the optimal water drive front position in step S4, optimize the fracturing parameters with the well group production capacity as the target.

[0035] In step S1, the basic data of the target reservoir in the overall block includes well logging curve data, microseismic monitoring data, drilling data, and reservoir data, specifically including porosity, permeability, oil saturation, horizontal maximum principal stress, horizontal minimum principal stress, vertical principal stress, rock elastic model, and Poisson's ratio.

[0036] A further technical solution is that the calculation formulas for the maximum horizontal principal stress, the minimum horizontal principal stress, and the vertical stress are as follows:

[0037]

[0038] σ v =0.001×∑ρ bi gΔH i (3)

[0039] In the formula: σ H , σ h The maximum and minimum horizontal ground stresses are given in MPa; r is Poisson's ratio, dimensionless; α is the effective stress coefficient, dimensionless; P p σ is the formation pore fluid pressure, MPa; β1 and β2 are the maximum and minimum principal stress coefficients, dimensionless; v The vertical stress is expressed in MPa; ρ bi The density of the i-th stratum is given in g / cm³. 3 ;ΔH i Let be the thickness of the i-th stratum, in meters (m); g is the gravity constant, 9.81 m / s². 2 n represents the total number of overlying strata.

[0040] A further technical solution is that, in step S2, reservoir property data is selected, and the Pearson correlation coefficient method is used to determine the main geological factors affecting production capacity. The reservoirs of the entire block are then classified according to these main geological factors. The mathematical formula for the Pearson correlation coefficient is as follows:

[0041]

[0042] In the formula: X and Y are two variables, cov(x,y) represents the covariance of variables X and Y, E represents the expectation, and μ X and μ Y These are the means of X and Y, respectively, and σ X and σ Y Let X and Y represent the standard deviations of variables X and Y, respectively.

[0043] The input value for the Pearson correlation coefficient method is between -1 and 1. The closer to -1, the stronger the negative correlation between the two variables. The closer to 1, the more likely there is a strong positive correlation between the two variables. The closer to 0, the weaker the correlation between the two variables or the absence of a linear relationship.

[0044] Further technical solutions are that in step S3, the reservoir geological model is established by comprehensively using geological, seismic, drilling and logging data, and under the condition of existing data in the target area, parameters such as shale content, porosity, permeability and oil saturation are simulated.

[0045] Further technical solutions are that when parameters such as shale content, porosity, permeability and oil saturation are missing, an interpolation method is selected to obtain the missing parameters by interpolation according to the existing parameters.

[0046] Further technical solutions are that in step S4, a typical nine-point water injection development well group is selected according to the reservoir division result, and a nine-point water injection development well group schematic diagram is shown in Figure 2 The horizontal fracture half-axis length of all oil wells in the nine-point well pattern is determined according to the seismic data, recorded as a1, a2, a3, a4, a5, a6, a7, a8 (i.e. the first parameter set), and the injection-production distance between the corresponding oil well and the center water injection well is recorded as b1, b2, b3, b4, b5, b6, b7, b8 (i.e. the second parameter set), the injection-production distance is subtracted from the horizontal fracture half-axis length of the corresponding oil well to obtain c1, c2, c3, c4, c5, c6, c7, c8 (i.e. the third parameter set), which is sorted from small to large, and the minimum value is selected as the maximum position of the water drive front edge of the water injection well, i.e. the best water drive front edge position, which can prevent water channeling.

[0047] Further technical solutions are that in step S4, different injection rates, injection times and soak times are set as targets based on the best water injection front edge to perform orthogonal experiments and optimize the optimal water injection and energy storage parameters.

[0048] Further technical solutions are that in step S5, based on the optimized optimal water injection and energy storage parameters, different horizontal fracture half-axis lengths and different fracture conductivity capacities are set as targets at the well group productivity of the oil well end to perform orthogonal experiment analysis and optimize the optimal horizontal fracture half-axis length and the optimal fracture conductivity capacity.

[0049] Further technical solutions are that for typical well groups of different types of reservoirs, the above steps are repeated to complete the optimization of energy storage and fracturing process parameters for all types of reservoirs.

[0050] Example 1

[0051] In order to facilitate the understanding and application of the technical solutions of the present application by those skilled in the art, the technical solutions of the present application are explained in combination with specific examples.

[0052] Step S1, obtaining the basic data of the target reservoir includes logging curve data, microseismic monitoring data, drilling data and reservoir data, including porosity, permeability, oil saturation, horizontal maximum principal stress, horizontal minimum principal stress, vertical principal stress, rock elastic model, Poisson's ratio.

[0053] The average porosity of the block is 12.3%, the average permeability is 12.3mD, and the rock density is 2.25g / cm 3 . Through the logging data, the Poisson's ratio is 0.23, the effective stress coefficient is 0.8, the maximum tectonic stress coefficient in the horizontal direction is 2.55, and the minimum tectonic stress coefficient is 1.55. The vertical stress is 15.03MPa, the maximum principal stress in the horizontal direction is 23.65MPa, and the minimum principal stress is 18.78MPa, respectively. Since the fracture initiation direction is always perpendicular to the minimum principal stress direction, it is judged that the fracture is a horizontal fracture.

[0054] Step S2, using Pearson correlation coefficient method to determine the main geological control factors affecting productivity, and classifying the reservoirs of the whole block according to the main geological control. The analysis results of the main geological control factors related to the productivity index are as follows: the Pearson correlation coefficient of oil saturation is 0.35, the Pearson correlation coefficient of oil layer thickness is 0.10, the Pearson correlation coefficient of porosity is 0.04, and the Pearson correlation coefficient of effective permeability is 0.03. Therefore, the reservoirs of the whole block are classified according to the oil saturation combined with sand group.

[0055] Step S3, comprehensively using geological, seismic, drilling and logging data to establish a reservoir geological model. Under the condition of existing data in the target area, parameters such as shale content, porosity, permeability and oil saturation are simulated.

[0056] Step S4, according to the reservoir classification results, a relatively regular typical nine-point water injection development well group is selected for each type of reservoir. As shown in Figure 2 , the horizontal fracture half axis length of all oil wells in the nine-point well pattern is determined according to the seismic data, recorded as a1, a2, a3, a4, a5, a6, a7, a8. At the same time, the injection-production distance between the corresponding oil well and the center water injection well is recorded as b1, b2, b3, b4, b5, b6, b7, b8. The injection-production distance is subtracted by the horizontal fracture half axis length of the corresponding oil well to obtain c1, c2, c3, c4, c5, c6, c7, c8, and they are sorted from small to large. The minimum value is selected as the maximum position of the water drive front reached by the water injection well, that is, the best water drive front position. The calculated water drive front distance is 28m, 26m, 33m, 42m, 38m, 29m, 35m, 41m, and the best water drive front position is 26m.

[0057] With the best water injection front 26m as the target, the injection speed, injection time and soaking time are optimized, and the optimal injection speed is 65 to 70m 3 / d, the optimal injection time is about 60d, and the optimal soaking time is 35 to 40d.

[0058] In step S5, according to the optimized injection speed, injection time and soaking time, the production time simulated at the oil well end is set to 3 years, different horizontal fracture half-lengths and different fracture conductivity are optimized and set to achieve the target of capacity, and orthogonal experimental analysis is performed to obtain the optimal horizontal fracture half-length of 60 to 70m and the optimal fracture conductivity of 25 to 27d·cm.

[0059] The above steps are repeated for typical well groups of different types of reservoirs to complete the optimization of energy storage fracturing process parameters for all types of reservoirs.

[0060] As can be seen, the present application provides an energy storage and repeated fracturing process parameter design method for an overall block, under the premise of reservoir classification, the energy storage parameters are optimized by using numerical simulation software with the best water drive front as the target, and the fracturing parameters are optimized with the capacity as the target, the quantitative design of the energy storage and repeated fracturing process parameters of the overall block is realized, and a new idea is provided for the production increase and efficiency improvement of old wells in the overall block.

[0061] In this paper, specific examples are applied to explain the principles and implementation modes of the present application, and the above examples are only used to help understand the method and core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for designing parameters of an energy storage and repeated fracturing process for a whole block, characterized in that, The method comprises the following steps: Step S1, obtaining basic data of the whole block; Step S2, determining the main geological control factor affecting productivity according to reservoir physical property data; Step S3, establishing a reservoir geological model using the basic data in step S1, and dividing different reservoir types according to the main geological control factor; Step S4, for each reservoir type, selecting a nine-point water injection well group and determining the optimal water drive front position; Step S5, according to the optimal water drive front position in step S4, optimizing the fracturing parameters with the well group productivity as the target.

2. The method according to claim 1, wherein the basic data in step S1 comprises well logging curve data, microseismic monitoring data, drilling data, and reservoir physical property data.

3. The method according to claim 1, wherein the reservoir physical property data in step S2 comprises oil saturation, oil layer thickness, porosity, and effective permeability.

4. The method according to claim 1, wherein the Pearson correlation coefficient method is used to determine the main geological control factor in step S2.

5. The method according to claim 1, wherein the method for determining the optimal water drive front position in step S4 is as follows: obtaining the injection-production distance between each oil well and the central water injection well as a first parameter set, obtaining the horizontal fracture half-length of each oil well as a second parameter set, performing difference processing on the first parameter set and the second parameter set to obtain a third parameter set, and selecting the minimum value of the elements in the third parameter set as the optimal water drive front position.

6. The method according to claim 1, wherein the horizontal fracture half-length is obtained through microseismic monitoring.

7. The integrated zonal, energized, refracturing process parameter design method of claim 1, wherein step S4 further comprises:

7. The method according to claim 1, wherein the water injection energy storage parameters are optimized with the optimal water drive front position as the target.

8. The method according to claim 1, wherein the water injection energy storage parameters in step S4 comprise injection rate, injection time, and soak time.

9. An electronic device comprising a memory, a processor, and a program stored on the memory and running on the processor, characterized in that, The processor executes the program to implement the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores instructions, and when the instructions run on the terminal device, the terminal device executes the steps of the method according to the above method.

Images