A method for determining mixed water fracturing parameters in tight oil and gas reservoirs

Abstract

The present invention relates to a method for determining mixed water fracturing parameters of tight oil and gas reservoirs, and belongs to the technical field of oil production engineering. The present invention uses a comprehensive compressibility model and rock elastic mechanical parameters to simulate and calculate the regional horizontal two-dimensional stress difference distribution, and uses a three-dimensional fracture model to analyze the fracture extension morphology under various fracturing parameters under the mixed water fracturing process, and optimizes the parameters according to the fracturing transformation volume and the variation range, and finally determines the reasonable mixed water fracturing design parameters. The present invention can more accurately reflect the reservoir heterogeneity, the simulated fracture morphology is less different from the actual geological conditions, and the obtained fracturing parameters are more reliable than conventional methods, and can achieve fracturing expectations.

Description

Technical Field

[0001] The invention relates to a method for determining mixed water fracturing parameters in a tight oil and gas reservoir, belonging to the technical field of oil production engineering. Background Art

[0002] At present, mixed water fracturing technology is widely used in tight oil and gas reservoirs at home and abroad. This technology uses a fracturing fluid system with different viscosities, adopts "large liquid volume, large sand volume, high displacement, low sand ratio" and alternate injection to form a large number of shear fractures, which can effectively increase the complexity of artificial fractures, increase the transformation volume, and thus greatly increase the production of a single well.

[0003] Fracturing parameters play a vital role in the fracturing effect. Therefore, the selection of fracturing parameters is very critical. At present, the selection method of fracturing parameters often uses regional mechanical parameters to establish an ideal model, and simulates and analyzes the single well production under different parameters based on fracturing simulation software and production prediction software. For example, the paper named "Optimization Design of Volume Fracturing for Tight Oil Horizontal Wells in Ordos Basin" (Petroleum Drilling and Production Technology, July 2015, authors include Bai Xiaohu, Qi Yin, etc.) discloses a volume fracturing design method for tight oil horizontal wells. However, the fracture morphology simulated by this design method is relatively simple, which is quite different from the actual geological conditions, thus affecting the production capacity prediction results and the selection of fracturing design parameters, resulting in the single well transformation effect not meeting the expected requirements. Summary of the invention

[0004] The purpose of the present invention is to provide a method for determining mixed water fracturing parameters in tight oil and gas reservoirs, so as to solve the problem that the fracture morphology simulated in the current fracturing parameter determination process is quite different from the actual geological conditions, resulting in the fracturing effect of the determined fracturing parameters failing to meet the expectations.

[0005] In order to solve the above technical problems, the present invention provides a method for determining mixed water fracturing parameters in tight oil and gas reservoirs, and the determination method comprises the following steps:

[0006] 1) Obtain seismic data and well logging data of the target reservoir and normalize the porosity in the well logging data;

[0007] 2) Processing the acquired seismic data to select seismic attributes whose correlation with porosity is greater than a set threshold;

[0008] 3) Determine the nonlinear relationship between the selected seismic attributes and porosity, perform random porosity calculation, and select the average porosity as the effective porosity model of the target reservoir;

[0009] 4) superimposing seismic coherence attributes on the effective porosity model to establish a comprehensive compressibility model;

[0010] 5) Based on the comprehensive compressibility model, the horizontal biaxial stress difference in the region is simulated and calculated according to the rock elastic parameters;

[0011] 6) According to the distribution of regional horizontal biaxial stress difference, determine the change of fracturing transformation volume when different fracturing parameters change, and determine the fracturing parameters according to the size of the fracturing transformation volume, that is, the change range.

[0012] The present invention uses a comprehensive compressibility model and rock elastic mechanics parameters to simulate and calculate the regional horizontal two-dimensional stress difference distribution, and uses a three-dimensional fracture model to analyze the fracture extension morphology under various fracturing parameters under the mixed water fracturing process, and optimizes the parameters according to the fracturing transformation volume and the amplitude of change, and finally determines the reasonable mixed water fracturing design parameters. The present invention can more accurately reflect the reservoir heterogeneity, the simulated fracture morphology is less different from the actual geological conditions, and the obtained fracturing parameters are more reliable than conventional methods, and can achieve fracturing expectations.

[0013] Furthermore, in order to accurately obtain the nonlinear relationship between seismic attributes and porosity, the nonlinear relationship in step 3) is determined by using a neural network calculation method.

[0014] Furthermore, in order to exclude autocorrelation attributes, the seismic attributes screened out in step 2) are also screened for autocorrelation attributes.

[0015] Furthermore, the regional horizontal biaxial stress difference is the difference between the maximum horizontal principal stress and the minimum horizontal well principal stress.

[0016] Furthermore, the rock elastic parameters in step 5) include Young's modulus, Poisson's ratio, fracture toughness, pore pressure and regional average maximum principal stress.

[0017] Furthermore, in order to obtain a more accurate mixed water fracturing crack morphology, the regional horizontal biaxial stress difference is calculated by using the material point method through the three-dimensional fracturing simulation software FracPredicto.

[0018] Furthermore, in order to obtain more reasonable mixed water fracturing design parameters, the step 6) is to use three-dimensional fracturing simulation software to simulate and analyze the crack extension morphology under different fracturing parameters under the mixed water fracturing process, and to delineate the fracturing transformation volume under different fracturing parameters according to the crack extension morphology.

[0019] Furthermore, the set threshold is 30%. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of the method for determining mixed water fracturing parameters for tight oil and gas reservoirs of the present invention;

[0021] Figure 2It is a schematic diagram of the change of fracturing transformation volume under different fracturing stage numbers in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The specific implementation of the present invention will be further described below in conjunction with the accompanying drawings.

[0023] The present invention first uses seismic data to establish a block structural model, conducts an overall ranking of seismic attributes and porosity sensitivity, and optimizes seismic attributes; establishes a correlation between logging porosity and optimized seismic attributes and performs porosity attribute modeling, takes its average value as the effective porosity model, and superimposes it with coherent attributes to finally obtain a comprehensive compressibility model. Using the comprehensive compressibility model and regional elastic mechanics related parameters, the material point algorithm is used to simulate and calculate the regional horizontal two-dimensional stress difference distribution, and a three-dimensional fracture model is used to analyze the fracture extension morphology under different engineering design parameters such as the number of fracturing stages, construction displacement, sand addition scale, front fluid ratio, average sand ratio, and sand addition procedure under the mixed water fracturing process. The parameters are optimized according to the size of the fracturing transformation volume and the amplitude of the change, and finally the reasonable mixed water fracturing design parameters are determined. The implementation process of this method is as follows: Figure 1 As shown, the specific implementation process is as follows.

[0024] 1. Obtain seismic data and well logging data of the target reservoir.

[0025] This example selects the Hangjin Banner block as the target reservoir, obtains seismic data of the block, and simultaneously obtains well logging and well deviation data of the pilot section of 18 vertical wells and 12 horizontal wells in the block. At the same time, a three-dimensional structural model of the target reservoir is established based on the acquired seismic data; the corresponding porosity curve is determined based on the well logging data, and the porosity curve of each well is normalized.

[0026] 2. Process the acquired seismic data and screen out sensitive seismic attributes.

[0027] There are many attributes of seismic data, such as root mean square amplitude, 10Hz single frequency body, relative impedance, frequency peak amplitude, curvature, etc. The present invention screens seismic attributes according to the correlation between each seismic attribute and porosity. This embodiment uses a fuzzy mathematical algorithm to sort the correlation between each seismic attribute and porosity, and selects seismic attributes with a correlation greater than 70%. In addition, in order to exclude autocorrelated attributes, correlation analysis is carried out on the selected seismic attributes, and geological attributes with correlation coefficients of more than 0.8 are excluded. In this embodiment, 8 seismic attributes are finally screened out in the above manner.

[0028] 3. Determine the relationship between the screened seismic attributes and porosity, and establish an effective porosity model.

[0029] Since the nonlinear relationship between seismic attributes and porosity is relatively complex and difficult to describe, in order to accurately describe the relationship between porosity and seismic attributes, the present invention uses an artificial intelligence neural network to determine the relationship between porosity and each seismic attribute. For this embodiment, the seismic data and porosity along the well trajectory are known, and the data encountered by these well points are used as training samples to train the neural network, and a neural network model of porosity and multiple seismic attributes is established. Because the neural network model is a random model, the neural network model obtained by each training may be different. In order to ensure that the porosity distribution close to the actual is obtained, during the training, 70% of the sample data are randomly selected as training data, and the remaining 30% of the sample data are used as test data, and the training data and the test data must reach a certain degree of correlation before accepting this neural network model. At the same time, in order to reflect the relationship between porosity and seismic attributes as much as possible, 100 neural network models that meet the conditions are saved. On the one hand, the relationship between multiple seismic attributes and porosity is obtained by averaging, and a three-dimensional distribution of porosity is established. On the other hand, the results can be analyzed probabilistically and the uncertainty of porosity can be quantitatively evaluated.

[0030] The porosity and the corresponding screened seismic attributes are combined into sample data. 70% of the sample data are used as training samples and 30% of the sample data are used for verification. Random modeling is performed 100 times, and the porosity calculation model with a correlation of more than 70% is selected. The average values ​​of the multiple porosities obtained are used as the effective porosity model of the target reservoir.

[0031] 4. Establish a comprehensive compressibility model based on the effective porosity model.

[0032] The comprehensive compressibility model is formed by superimposing the effective porosity model and the normalized seismic coherent attributes. For this embodiment, the effective porosity distribution model and the coherent attribute distribution of the Box 1 layer are added, and finally the comprehensive compressibility model of the Box 1 layer in the southern part of the Jin 58 well area is established. This model can clearly reflect the changes in structure and physical properties, reflect the heterogeneity of the reservoir to a certain extent, and provide a basis for targeted fracturing design.

[0033] 5. Based on the comprehensive compressibility model, the regional horizontal biaxial stress difference is simulated and calculated according to the rock elastic mechanics parameters.

[0034] The regional horizontal biaxial stress difference is the difference between the horizontal maximum principal stress and the horizontal minimum principal stress. In this embodiment, based on the comprehensive compressibility model of the Box 1 layer in the Jin 58 well area, combined with rock elastic parameters and regional mechanical parameters, the professional three-dimensional fracturing simulation software FracPredictor is used to simulate and calculate the regional horizontal biaxial stress difference distribution using the material point method. The rock elastic mechanical parameters include Young's modulus, Poisson's ratio, fracture toughness, pore pressure and regional average maximum principal stress, which can be obtained through indoor experiments and well logging methods. For this embodiment, the rock elastic mechanical parameters and the calculated horizontal maximum principal stress and azimuth are shown in Table 1.

[0035] Table 1

[0036]

[0037] 6. Determine the fracturing parameters based on the regional horizontal bidirectional stress difference distribution.

[0038] Based on the regional horizontal biaxial stress difference distribution, a three-dimensional fracture model is used to analyze the fracture extension morphology corresponding to different fracturing parameters under the mixed water fracturing process, and the fracturing transformation volume under different parameters is delineated. The parameters are optimized according to the size and variation of the fracturing transformation volume, and reasonable mixed water fracturing design parameters are determined. The fracturing parameters include the number of fracturing stages, construction displacement, sand addition scale, pre-fluid ratio, ground fluid volume, and fracturing fluid viscosity combination. The specific parameter determination steps are as follows: first determine the reasonable number of fracturing transformation sections (fracture section spacing) according to the size of the fracturing transformation volume. After determining the number of fracturing sections (section spacing), perform construction displacement simulation optimization to obtain a reasonable construction displacement. Then, based on the determined number of fracturing transformation sections and construction displacement, simulate the fracturing transformation volume with different amounts of in-ground liquid to obtain a reasonable amount of in-ground liquid. Then, gradually simulate the fracturing transformation volume under parameters such as the pre-fluid ratio, fracturing fluid viscosity combination, and sand addition amount to obtain the corresponding reasonable design parameters. The parameter determination order is: number of fracturing sections (section spacing), construction displacement, in-ground liquid amount, pre-fluid ratio, fracturing fluid viscosity combination, and sand addition amount.

[0039] For this embodiment, the target well J58P2H was selected, and the three-dimensional fracturing simulation software FracPredictor was used to simulate the fracturing crack morphology under different fracturing parameters, and the fracturing transformation volume (SRV) was determined. The parameters were optimized according to the size of the fracturing transformation volume (SRV) and the amplitude of the change, and finally the reasonable mixed water fracturing design parameters were obtained. Taking the optimization of the number of fracturing stages as an example, the simulated morphology of the fracturing cracks under the number of fracturing stages 6-14 was simulated and calculated, and the fracturing transformation volume (SRV) was delineated. When the number of fracturing transformation stages is 6, the fracturing transformation volume is small. As the number of fracturing stages increases, the fracturing transformation volume gradually increases. When the number of fracturing stages reaches 12, the growth of the transformation volume gradually slows down. When the number of fracturing stages reaches 14, the increase in the fracturing transformation volume reaches its limit, such as Figure 2 As shown in the figure, increasing the number of fracturing stages at this time cannot obtain good economic benefits, so the optimal number of fracturing stages is 12.

Claims

1. A method for determining mixed water fracturing parameters in tight oil and gas reservoirs, characterized in that: The determination method comprises the following steps: 1) Obtain seismic data and well logging data of the target reservoir and normalize the porosity in the well logging data; 2) Processing the acquired seismic data to select seismic attributes whose correlation with porosity is greater than a set threshold; 3) Determine the nonlinear relationship between the selected seismic attributes and porosity, perform random porosity calculation, and select the average porosity as the effective porosity model of the target reservoir; 4) superimposing seismic coherence attributes on the effective porosity model to establish a comprehensive compressibility model; 5) On the comprehensive compressibility model, the regional horizontal biaxial stress difference is simulated and calculated according to the rock elastic mechanics parameters, the rock elastic mechanics parameters include Young's modulus, Poisson's ratio, fracture toughness, pore pressure and regional average maximum principal stress; the regional horizontal biaxial stress difference is the difference between the horizontal maximum principal stress and the horizontal minimum principal stress, and the regional horizontal biaxial stress difference is obtained by simulating and calculating the regional horizontal biaxial stress difference distribution through three-dimensional fracturing simulation software and material point method; 6) Determine the fracturing parameters according to the regional horizontal two-dimensional stress difference distribution: Based on the regional horizontal two-dimensional stress difference distribution, a three-dimensional fracture model is used to analyze the fracture extension morphology corresponding to different fracturing parameters under the mixed water fracturing process, and the fracturing transformation volume under different parameters is delineated. The parameters are optimized according to the size and variation of the fracturing transformation volume, and the reasonable mixed water fracturing design parameters are determined; the fracturing parameters include the number of fracturing stages, construction displacement, sand addition scale, pre-fluid ratio, ground fluid volume, and fracturing fluid viscosity combination; the specific parameter determination steps are as follows: first According to the size of the fracturing transformation volume, a reasonable number of fracturing transformation sections is determined. After determining the number of fracturing sections, the construction displacement simulation optimization is carried out to obtain a reasonable construction displacement. Then, based on the determined number of fracturing transformation sections and construction displacement, the fracturing transformation volumes with different amounts of in-ground liquid are simulated to obtain a reasonable amount of in-ground liquid. Then, the fracturing transformation volumes under the parameters of pre-fluid ratio, fracturing fluid viscosity combination, and sand addition are gradually simulated to obtain the corresponding reasonable design parameters. The order of parameter determination is: number of fracturing sections, construction displacement, in-ground liquid volume, pre-fluid ratio, fracturing fluid viscosity combination, and sand addition.

2. The method for determining mixed water fracturing parameters for tight oil and gas reservoirs according to claim 1, characterized in that: The nonlinear relationship in step 3) is determined by using a neural network calculation method.

3. The method for determining mixed water fracturing parameters for tight oil and gas reservoirs according to claim 1, characterized in that: The earthquake attributes screened out in step 2) are also screened by autocorrelation attributes.

4. The method for determining parameters of mixed water fracturing in tight oil and gas reservoirs according to claim 1, characterized in that: The set threshold is 30%.

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