Ultra-low permeability reservoir horizontal well re-fracturing well selection method

Abstract

The invention discloses an ultra-low permeability reservoir horizontal well refracturing well selection method. The method comprises the following steps that S1, a fine three-dimensional geological model of a target block is established; s2, establishing a three-dimensional geomechanical model of the target block; s3, carrying out reservoir numerical value and geomechanical simulation, and taking a simulation result as a pre-selected data set; s4, determining characteristic indexes influencing the refracturing effect; s5, establishing a refracturing well selection mathematical model, solving the weight of each characteristic index, and calculating the Euclidean distance between the candidate well characteristic indexes and the positive and negative ideal solutions; and S6, calculating the relative attachment degree between each characteristic index of the re-fracturing candidate horizontal well and the ideal solution, and sorting and optimizing the re-fracturing candidate horizontal well. According to the method, the geology and oil reservoir dynamic change process of candidate well initial pressure fracturing can be truly reflected, the accuracy and scientificity of characteristic index value taking are improved, the influence of rock mechanics, reservoir physical properties, fracturing construction and production dynamic parameters on the refracturing yield increasing effect is comprehensively considered, the considered factors are comprehensive, and the accuracy is high.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas field development in petroleum engineering, and relates to a method for selecting wells by repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs. Background Art

[0002] At present, most ultra-low permeability reservoirs have strong heterogeneity, poor reservoir properties, and it is difficult to establish a water drive system. In addition, with the development and production, the initial fracturing cracks are easy to close and block, resulting in a continuous decline in the conductivity of the cracks, which is often manifested as large production declines after production, long-term low production, low recovery, and low oil production rate. It is particularly difficult for oil fields to maintain stable production without drilling new wells. Repeated fracturing can effectively perform secondary transformation on low-yield horizontal wells to achieve the purpose of increasing production and efficiency. Therefore, there is an urgent need for a repeated fracturing well selection method to accurately select horizontal wells with high repeated fracturing production potential to ensure maximum economic benefits.

[0003] There are many factors that affect the effect of repeated fracturing, including the rock mechanical properties of the reservoir, reservoir physical properties, the scale of previous fracturing, production dynamic parameters, etc., and there are interactions between these factors. Therefore, it is necessary to comprehensively evaluate the impact of these factors on the effect of repeated fracturing. At present, the methods for selecting wells for repeated fracturing are divided into five categories: (1) field experience method (2) neural network method (3) statistical analysis method (4) fuzzy comprehensive evaluation method (5) dimensionless parameter method. Among them, the field experience method is highly subjective and the evaluation results are prone to deviation; the training process has high requirements for field data, the sample size is too small, the accuracy is low, and it is also limited by the algorithm itself; the statistical analysis method is a single factor analysis method and does not comprehensively consider the mutual influence and weight distribution between multiple factors; the classification boundaries and weights of each indicator in the fuzzy comprehensive evaluation method depend on experience and need to be adjusted with the changes in the block. It is not universal and has poor generalization; the dimensionless parameter method determines the screening boundaries based on the wells that have been repeatedly transformed. There are few wells that have been repeatedly transformed in the study block, so it is difficult to determine the boundaries. At present, considering that the initial fracturing production of horizontal wells is accompanied by the dynamic changes of reservoir geology and oil reservoirs, the impact of repeated fracturing on the production increase effect is relatively small.

[0004] In summary, the current re-fracturing well selection method has defects such as one-sided evaluation indicators and low accuracy. The present invention proposes a re-fracturing well selection method based on numerical simulation, grey correlation analysis and approximate ideal solution sorting method. The invention has high scientificity and accuracy. Summary of the invention

[0005] The purpose of the present invention is to provide a method for selecting wells for horizontal well re-fracture in ultra-low permeability oil reservoirs, which solves the problems of one-sided evaluation indexes and low accuracy in the prior art re-fracture well selection methods.

[0006] The technical solution adopted by the present invention is a method for selecting wells for repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs, comprising the following steps:

[0007] S1: Establish a detailed 3D geological model of the target area;

[0008] S2: Establish a three-dimensional geomechanical model of the target block;

[0009] S3: Perform reservoir numerical simulation and geomechanical simulation, and use the reservoir numerical simulation and geomechanical simulation results and the on-site initial fracturing operation parameters as pre-selected data sets of characteristic indicators;

[0010] S4: Determine the characteristic indexes that affect the effect of repeated fracturing;

[0011] S5: Establish a mathematical model for well selection for repeated fracturing, obtain the weight of each characteristic index, and use the approximate ideal solution sorting method to calculate the Euclidean distance between the characteristic index of the candidate well and the positive and negative ideal solutions;

[0012] S6: Calculate the relative progress between the characteristic indicators of the candidate horizontal wells for repeated fracturing and the ideal solution, and carry out the sorting and optimization of the candidate wells for the potential for repeated fracturing to increase production.

[0013] The present invention is also characterized in that:

[0014] S1 is to carry out a detailed reservoir description of the target block and obtain the distribution of attributes such as porosity, permeability, and oil saturation under initial conditions. The specific modeling process is as follows:

[0015] S11: Import wellhead data and well trajectory data

[0016] The wellhead data and well data of all wells in the block where the candidate horizontal well is located are sorted out. The wellhead data includes the geodetic coordinates and core elevation of the wells in the block, and the well trajectory data includes the sounding, well inclination and azimuth of the wells in the block. The wellhead data and well trajectory data are imported into the petrel geological modeling software in turn;

[0017] S12: Importing well logging data

[0018] Well logging data mainly include porosity, permeability, shale content, water saturation and acoustic time difference, etc., and outliers in the well logging data are eliminated according to the calculator function of Petrel geological modeling software;

[0019] S13: Stratigraphic division and structural modeling

[0020] The stratum division is carried out by combining the oil group, sand group, sub-layer and sand body division comparison data of each well with the seismic interpretation layer data. The top and bottom surface models of each isochronous layer are generated by interpolation method and layer data. Then, the models of each layer are spatially superimposed to establish the spatial grid of the reservoir. The structural model of the block is established by using the makezone and vertical grid subdivision functions.

[0021] S14: Attribute Modeling

[0022] Firstly, the logging data is discretized to generate corresponding data parameters, and the lithofacies model is used as the constraint condition of the attribute model to establish parameter models such as porosity, permeability, and oil saturation that can reflect the heterogeneity of underground reservoirs. The co-kriging method is added to make the parameter simulation more accurate.

[0023] Based on the three-dimensional fine geological model and with the single well rock mechanics profile as a constraint, the three-dimensional geomechanical model of the work area is finely described, and the rock mechanics distribution characteristics such as the maximum horizontal principal stress, minimum horizontal principal stress, Young's modulus, and Poisson's ratio under initial conditions are obtained. The specific modeling process is as follows:

[0024] S21: Obtaining rock mechanical parameters

[0025] The rock mechanics parameters of a single well are calculated from logging, core and test data. The rock mechanics parameters mainly include reservoir overburden stress, uniaxial compressive strength, uniaxial tensile strength, Young's modulus, Poisson's ratio, shear modulus, internal friction angle, cohesion, maximum horizontal principal stress, and minimum horizontal principal stress. The direction of ground stress is accurately determined by imaging logging. These parameters are directly used to establish the unit properties around the wellbore and are used for quality control of the 3D geomechanical model.

[0026] S22: Establishment of 3D geomechanical model

[0027] The geostress profile of a single well in the work area is imported into the well trajectory in Petrel and the model is coarsened to obtain the discrete geostress field of the work area. The stress field of the entire work area is obtained through Kriging interpolation. The seismic data and geological structure information are used for inter-well interpolation to ensure the reliability of the distribution of geomechanical parameters in three-dimensional space, thereby realizing the establishment of a three-dimensional geomechanical model.

[0028] In S3, reservoir numerical simulation and geomechanics simulation are performed on the target block using a reservoir numerical simulator and a geomechanics simulator to obtain the rock mechanical parameters, reservoir physical property parameters, production dynamic parameters and on-site initial fracturing operation parameters of the candidate repeated fracturing horizontal well at the end of the initial fracturing, and these parameters are used as the pre-selected data set of characteristic indicators;

[0029] Determine the characteristic indicators that affect the effect of repeated fracturing, among which the characteristic indicators of rock mechanics parameters include biaxial stress difference, Young's modulus, and Poisson's ratio; the characteristic indicators of reservoir physical parameters include porosity, permeability, oil saturation, and oil layer thickness; the characteristic indicators of initial fracturing construction parameters include drilling rate, number of fracturing stages, average sand ratio, single-stage fracturing fluid volume, single-stage sand volume, and horizontal section length; the characteristic indicators of production dynamics include average annual decline rate, initial pressure cumulative oil production, and formation pressure;

[0030] In S5, the grey correlation method is used to obtain the weight of each characteristic index. It is necessary to determine the positive and negative ideal solutions that affect the effect of repeated fracturing. On this basis, the approximate ideal solution sorting method is used to calculate the Euclidean distance between the characteristic index of the candidate well and the positive and negative ideal solutions. The specific steps are as follows:

[0031] S51: Establish an initial matrix using the pre-selected data set of characteristic indicators:

[0032]

[0033] Where n represents the number of candidate re-fracturing wells, and m represents the characteristics of each candidate re-fracturing well;

[0034] S52: Standardize the initial matrix to eliminate the inconsistency of magnitude and units between feature indices:

[0035]

[0036] S53: Use the grey correlation analysis method to calculate the correlation of each characteristic index

[0037] Firstly, the matrix formed after dimensionless transformation is established to determine the position of each characteristic index, and the initial pressure cumulative oil production is taken as the parent sequence, and the biaxial stress difference, Young's modulus, Poisson's ratio, porosity, permeability, oil saturation, oil layer thickness, drilling rate, number of fracturing stages, average sand ratio, single-stage fracturing fluid volume, single-stage sand volume, horizontal section length, average annual decline rate, and formation pressure are taken as subsequences;

[0038]

[0039] In the formula, X0 represents the parent sequence, X1-X m represents a subsequence;

[0040] Then calculate the grey correlation degree between each subsequence and the parent sequence:

[0041]

[0042]

[0043] Where ΔX 0i(k) represents the absolute difference between the i-th characteristic index of the k-th candidate well and the corresponding reference value of the well, ρ is the resolution coefficient, ranging from 0.1 to 1, and γ 0i is the average value of grey relational degree;

[0044] S54: Obtain the grey correlation coefficient weight of each characteristic index:

[0045]

[0046] S55: Assign the weight of the grey correlation coefficient to each dimensionless characteristic index to obtain a weighted standardized matrix:

[0047]

[0048] S56: Determine the positive and negative ideal solutions that affect the effect of refracturing:

[0049] S57: Obtain the Euclidean distance between each characteristic index of the candidate well and the positive and negative ideal solutions:

[0050]

[0051]

[0052] The relative progress between the characteristic indicators of the candidate horizontal wells for repeated fracturing and the ideal solution is calculated. The relative progress indicates the degree of proximity between the evaluation object and the ideal object. The characteristic indicators of the candidate wells are taken as an example of the degree of proximity to the positive ideal. The smaller the value, the closer the corresponding characteristic indicators of the candidate horizontal wells for repeated fracturing are to the characteristic indicators of the ideal horizontal wells for repeated fracturing. The relative progress is sorted from small to large. The smaller the relative progress, the greater the re-fracturing potential of the candidate well.

[0053]

[0054] In formula (10), N j It is expressed as the relative progress of the j-th well.

[0055] The beneficial effects of the present invention are:

[0056] 1. The present invention establishes a three-dimensional geological engineering model and uses numerical simulation and geomechanical simulation methods to truly reflect the geological and reservoir dynamic changes of the candidate well initial fracturing, thereby improving the accuracy and scientificity of the characteristic index values;

[0057] 2. The present invention utilizes the grey correlation analysis method to overcome the shortcoming that the weights of well characteristic indicators must be known in the traditional well selection method, and comprehensively considers the influence of rock mechanics, reservoir properties, fracturing construction, and production dynamic parameters on the repeated fracturing production increase effect. It considers all factors and has high accuracy, which has good guiding significance for on-site repeated fracturing well selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of well selection for repeated fracturing of the present invention;

[0059] Figure 2 A fine three-dimensional geological porosity model diagram of the target block established in Example 1 of the present invention;

[0060] Figure 3 A detailed three-dimensional geological permeability model diagram of the target block established in Example 1 of the present invention;

[0061] Figure 4 A fine three-dimensional geological water saturation model diagram of the target block established in Example 1 of the present invention;

[0062] Figure 5 A detailed three-dimensional geological sedimentary facies model map of the target area established in Example 1 of the present invention;

[0063] Figure 6 It is a three-dimensional geomechanical minimum horizontal principal stress model diagram of the target block established in Example 1 of the present invention;

[0064] Figure 7 It is a three-dimensional geomechanical maximum horizontal principal stress model diagram of the target block established in Example 1 of the present invention;

[0065] Figure 8 This is a three-dimensional geomechanical Young's modulus model diagram of the target block established in Example 1 of the present invention;

[0066] Fig. 9 This is a three-dimensional geomechanical Poisson's ratio model diagram of the target block established in Example 1 of the present invention. DETAILED DESCRIPTION

[0067] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0068] The method for selecting wells for repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs comprises the following steps:

[0069] Step 1: Establish a fine 3D geological model of the target block, mainly to carry out a fine reservoir description of the target block, and obtain the distribution of attributes such as porosity, permeability, and oil saturation under initial conditions. The specific modeling process includes the following steps:

[0070] (1) Importing wellhead data and well trajectory data: The wellhead data and well data of all wells in the block where the candidate horizontal well is located are sorted. The wellhead data includes the geodetic coordinates and core elevation of the wells in the block, and the well trajectory data includes the depth, well inclination, and azimuth of the wells in the block. The wellhead data and well trajectory data are imported into the petrel geological modeling software in sequence;

[0071] (2) Importing well logging data: Well logging data mainly includes porosity, permeability, shale content, water saturation and acoustic wave time difference, and outliers in the well logging data are eliminated according to the calculator function of Petrel geological modeling software;

[0072] (3) Stratigraphic division and structural modeling: Stratigraphic division is performed by combining the comparative data of oil groups, sand groups, sub-layers, and sand bodies of each well with the seismic interpretation layer data. The top and bottom surface models of each isochronous layer are generated by interpolation and application of layered data. The models of each layer are then spatially superimposed to establish the spatial grid of the reservoir. The structural model of the block is established using the makezone and vertical grid subdivision functions.

[0073] (4) Attribute modeling: First, the logging data is discretized to generate corresponding data parameters. The lithofacies model is used as the constraint condition of the attribute model to establish parameter models such as porosity, permeability, and oil saturation that can reflect the heterogeneity of the underground reservoir. The co-kriging method is added to make the parameter simulation more accurate.

[0074] Step 2: Establish a three-dimensional geomechanical model of the target area. Based on the three-dimensional fine geological model and with the single well rock mechanics profile as a constraint, the three-dimensional geomechanical model of the work area is finely characterized to obtain the maximum horizontal principal stress, minimum horizontal principal stress, Young's modulus, Poisson's ratio and other rock mechanics distribution characteristics under initial conditions. The specific modeling process includes the following steps:

[0075] (1) Obtaining rock mechanics parameters: The rock mechanics parameters of a single well are calculated from logging, core and test data. The rock mechanics parameters mainly include: reservoir overburden stress, uniaxial compressive strength, uniaxial tensile strength, Young's modulus, Poisson's ratio, shear modulus, internal friction angle, cohesion, maximum horizontal principal stress, minimum horizontal principal stress, and the direction of ground stress is accurately determined by imaging logging. These parameters are directly used to establish the unit properties around the wellbore and are used for quality control of the 3D geomechanical model;

[0076] (2) Establishment of a three-dimensional geomechanical model: The geostress profile of a single well in the work area is imported into the well trajectory in Petrel and the model is coarsened to obtain the discrete geostress field of the work area. The stress field of the entire work area is obtained through Kriging interpolation. Seismic data and geological structure information are used for inter-well interpolation to ensure the reliability of the three-dimensional distribution of geomechanical parameters, thereby realizing the establishment of a three-dimensional geomechanical model.

[0077] Step 3: Conduct reservoir numerical simulation and geomechanical simulation. Specifically, use a reservoir numerical simulator (intersect) and a geomechanical simulator (visage) to conduct reservoir numerical simulation and geomechanical simulation on the target block, obtain the rock mechanical parameters, reservoir physical property parameters, production dynamic parameters and on-site initial fracturing construction parameters of the candidate repeated fracturing horizontal wells at the end of the initial fracturing, and use these parameters as the pre-selected data set of characteristic indicators.

[0078] Step 4: Determine the characteristic indicators that affect the effect of repeated fracturing, specifically: the characteristic indicators of rock mechanics parameters include biaxial stress difference, Young's modulus, and Poisson's ratio; the characteristic indicators of reservoir physical parameters include porosity, permeability, oil saturation, and oil layer thickness; the characteristic indicators of initial fracturing construction parameters include drilling rate, number of fracturing stages, average sand ratio, single-stage fracturing fluid volume, single-stage sand volume, and horizontal section length; the characteristic indicators of production dynamics include average annual decline rate, initial pressure cumulative oil production, and formation pressure;

[0079] Step 5: Establish a mathematical model for well selection for repeated fracturing, and obtain the weights of each characteristic index. Specifically, the gray correlation method is used to obtain the weights. On this basis, the approximate ideal solution sorting method is used to calculate the Euclidean distance between the characteristic index of the candidate well and the positive and negative ideal solutions. The specific steps are as follows:

[0080] (1) Use the pre-selected data set of characteristic indicators to establish the initial matrix:

[0081]

[0082] In formula (1), n ​​represents the number of candidate re-fracturing wells, and m represents the characteristics of each candidate re-fracturing well;

[0083] (2) Standardize the initial matrix to eliminate the inconsistency of magnitude and units between feature indicators:

[0084]

[0085] (3) Use the grey correlation analysis method to calculate the correlation of each characteristic index

[0086] Firstly, the matrix formed after dimensionless transformation is established to determine the position of each characteristic index. The initial pressure cumulative oil production is taken as the parent sequence, and the biaxial stress difference, Young's modulus, Poisson's ratio, porosity, permeability, oil saturation, oil layer thickness, drilling rate, number of fracturing stages, average sand ratio, single-stage fracturing fluid volume, single-stage sand volume, horizontal section length, average annual decline rate, and formation pressure are taken as subsequences.

[0087]

[0088] In formula (3), X0 represents the parent sequence, X1-X m represents a subsequence;

[0089] Then calculate the grey correlation degree between each subsequence and the parent sequence:

[0090]

[0091]

[0092] Where ΔX 0i (k) represents the absolute difference between the i-th characteristic index of the k-th candidate well and the corresponding reference value of the well, ρ is the resolution coefficient, ranging from 0.1 to 1, and γ 0i is the average value of grey relational degree;

[0093] (4) Obtain the grey correlation coefficient weight of each characteristic index:

[0094]

[0095] (5) Assign the weight of the grey correlation coefficient to each dimensionless characteristic index to obtain the weighted standardized matrix:

[0096]

[0097] (6) Determine the positive and negative ideal solutions that affect the refracturing effect:

[0098] The smaller the difference between the two phase stresses, the easier it is to form a re-fracturing fracture network during repeated fracturing; the larger the Young's modulus and the smaller the Poisson's ratio, the better the brittleness of the rock and the easier it is to pressurize the reservoir; the greater the porosity, permeability, oil saturation, and oil layer thickness drilling rate, the better the reservoir properties of the candidate well and the higher the degree of recoverability of repeated fracturing; the smaller the number of fracturing stages, the average sand ratio, the single-stage fracturing fluid volume, and the single-stage sand volume, the smaller the initial fracturing transformation degree of the candidate well and the greater the re-fracturing potential; the smaller the average annual decline rate and the greater the formation pressure, the stronger the fluid supply capacity near the candidate well, the larger the oil leakage area, and the better the pressure supply properties of the rock. Based on the above analysis, the maximum values ​​of Young's modulus, porosity, permeability, oil saturation, oil layer thickness drilling rate, horizontal section length, formation pressure and two-way stress difference, Poisson's ratio, number of fracturing stages, average sand ratio, single-stage fracturing fluid volume, single-stage sand volume, and average annual decline rate are taken as positive ideal solutions, and the negative ideal solution is the opposite.

[0099] (7) Obtain the Euclidean distance between each characteristic index of the candidate well and the positive and negative ideal solutions:

[0100]

[0101]

[0102] Step 6: Calculate the relative progress between the characteristic indicators of the candidate horizontal wells for repeated fracturing and the ideal solution. The relative progress indicates the degree of closeness between the evaluation object and the ideal object. The characteristic indicators of the candidate wells are taken as an example of closeness to the positive ideal degree. The smaller the value, the closer the corresponding characteristic indicators of the candidate horizontal wells for repeated fracturing are to the characteristic indicators of the ideal horizontal wells for repeated fracturing. Sort the relative progress from small to large. The smaller the relative progress, the greater the re-fracturing potential of the candidate well.

[0103]

[0104] In formula (10), N j It is expressed as the relative progress of the j-th well.

[0105] Example 1

[0106] Taking 6 candidate horizontal wells for repeated fracturing in a certain block as an example, the selection of candidate horizontal wells for repeated fracturing is carried out. The specific implementation process is as follows:

[0107] Step 1) Establish a detailed 3D geological model of the target area, such as Figures 2 to 5 shown.

[0108] Step 2) Establish a three-dimensional geomechanical model of the target block. Based on the three-dimensional fine geological model of the target block established in the first step, the single well rock mechanics profile is used as a constraint to achieve a fine characterization of the three-dimensional geomechanical model of the work area, such as Figures 6 to 9 shown.

[0109] Step 3) Perform reservoir numerical simulation and geomechanical simulation to derive the rock mechanical parameters, reservoir physical parameters, production dynamic parameters and on-site initial fracturing operation parameters of the six candidate repeated fracturing horizontal wells at the end of the initial fracturing, and use these parameters as the pre-selected data set of characteristic indicators. As shown in Table 1:

[0110] Table 1 Parameter set of 6 horizontal wells at the end of initial fracturing

[0111]

[0112] Step 4) Determine the characteristic indicators that affect the effect of repeated fracturing. The repeated fracturing production increase effect is mainly affected by four aspects: rock mechanics parameters, reservoir physical parameters, initial fracturing construction parameters and production dynamic parameters. The characteristic indicators of rock mechanics parameters include: biaxial stress difference, Young's modulus, Poisson's ratio; the characteristic indicators of reservoir physical parameters include: porosity, permeability, oil saturation, and oil layer thickness; the characteristic indicators of initial fracturing construction parameters include: drilling rate, number of fracturing clusters, number of fracturing stages, average stage spacing, average sand ratio, single cluster fracturing fluid volume, single cluster sand volume, and horizontal section length; the dynamic characteristic indicators of production include: average annual decline rate, initial pressure cumulative oil production, and formation pressure. The data of each characteristic indicator are from Table 1.

[0113] Step 5) Establish a mathematical model for well selection for repeated fracturing, use the grey correlation method to obtain the weight of each characteristic index, and on this basis use the approximate ideal solution sorting method to calculate the Euclidean distance between the characteristic index of the candidate well and the positive and negative ideal solutions. The specific steps are as follows:

[0114] (1) Use the pre-selected data set of characteristic indicators to establish the initial matrix:

[0115]

[0116] (2) The initial matrix is ​​standardized to eliminate the inconsistency in the magnitude and unit of the characteristic indicators and obtain the standardized matrix b.

[0117]

[0118] (3) Use the grey correlation analysis method to calculate the correlation of each characteristic index

[0119] First, the matrix formed after dimensionless transformation is established to determine the position of each characteristic index. The initial pressure cumulative oil production (X0) is used as the parent sequence, the two-axis stress difference (X1), Young's modulus (X 2) , Poisson's ratio (X3), porosity (X4), permeability (X 5) , oil saturation (X6), oil layer thickness drilling rate (X7), number of fracturing clusters (X8), number of fracturing stages (X9), average stage spacing (X10 ), average sand ratio (X 11 ), single cluster fracturing fluid volume (X 12 ), single cluster sand volume (X 13 ), horizontal segment length (X 14 ), average annual decline rate (X 15 ), formation pressure (X 16 ) as a subsequence.

[0120]

[0121] In the formula, X0 represents the parent sequence, X1-X 16 Represents a subsequence.

[0122] Then, the grey correlation degree between each subsequence and the parent sequence is calculated using formulas (4) and (5), as shown in Table 2 below:

[0123]

[0124]

[0125] Where ΔX 0i (k) represents the absolute difference between the i-th characteristic index of the k-th candidate well and the corresponding reference value of the well, ρ is the resolution coefficient, which is 0.5, and γ 0i is the average value of grey relational degree.

[0126] Table 2 Grey correlation between each characteristic index and initial pressure cumulative oil production

[0127]

[0128] (4) Use formula (6) to obtain the grey correlation coefficient weight of each characteristic index, see Table 3 below:

[0129]

[0130] Table 3 Grey correlation coefficient weights of each characteristic index

[0131]

[0132] (5) Assign the weight of the grey correlation coefficient to each dimensionless characteristic index to obtain the weighted standardized matrix:

[0133]

[0134] (6) Determine the positive and negative ideal solutions of the characteristic indicators that affect the effect of repeated fracturing, see Table 4 below:

[0135] Table 4 Positive and negative ideal solutions of characteristic indicators

[0136]

[0137]

[0138] (7) Using formulas (8) and (9), the Euclidean distances between the characteristic indicators of the candidate wells and the positive and negative ideal solutions are obtained, as shown in Table 5 below:

[0139]

[0140]

[0141] Table 5 Euclidean distances between the characteristic indicators of candidate wells and the positive and negative ideal solutions

[0142] Refracturing candidate wells <![CDATA[D + ]]> <![CDATA[D - <!-- 12 -->]]> J1 0.0225 0.0272 J2 0.0269 0.0204 J3 0.0288 0.0200 J4 0.0256 0.0253 J5 0.0220 0.0216 J6 0.0250 0.0225

[0143] Step 6) Formula (10) is used to calculate the relative progress between the characteristic indicators of the candidate horizontal wells for repeated fracturing and the ideal solution, as shown in Table 6 below, and then the candidate wells are ranked and optimized for their repeated fracturing production increase potential.

[0144]

[0145] In formula (10), N j It is expressed as the relative progress of the j-th well.

[0146] Table 6 Relative progress of each characteristic index of candidate wells and the ideal solution

[0147] Refracturing candidate wells Relative progress J1 0.453 J2 0.569 J3 0.591 J4 0.503 J5 0.504 J6 0.526

[0148] The relative progress indicates the closeness between the evaluation object and the ideal object. The characteristic indicators of the candidate wells are close to the positive ideal solution. The smaller the value, the closer the characteristic indicators of the corresponding candidate re-fracturing horizontal wells are to the characteristic indicators of the ideal re-fracturing horizontal wells. Therefore, it can be concluded that the order of re-fracturing of these 6 horizontal wells is: J1>J4>J5>J6>J2>J3, and the horizontal well most suitable for re-fracturing is J1.

[0149] Example 2

[0150] The method for selecting wells for repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs comprises the following steps:

[0151] S1: Establish a fine 3D geological model of the target block, conduct a fine reservoir description of the target block, and obtain the distribution of attributes such as porosity, permeability, and oil saturation under initial conditions. The specific modeling process is as follows:

[0152] S11: Import wellhead data and well trajectory data

[0153] The wellhead data and well data of all wells in the block where the candidate horizontal well is located are sorted out. The wellhead data includes the geodetic coordinates and core elevation of the wells in the block, and the well trajectory data includes the sounding, well inclination and azimuth of the wells in the block. The wellhead data and well trajectory data are imported into the petrel geological modeling software in turn;

[0154] S12: Importing well logging data

[0155] Well logging data mainly include porosity, permeability, shale content, water saturation and acoustic time difference, etc., and outliers in the well logging data are eliminated according to the calculator function of Petrel geological modeling software;

[0156] S13: Stratigraphic division and structural modeling

[0157] The stratum division is carried out by combining the oil group, sand group, sub-layer and sand body division comparison data of each well with the seismic interpretation layer data. The top and bottom surface models of each isochronous layer are generated by interpolation method and layer data. Then, the models of each layer are spatially superimposed to establish the spatial grid of the reservoir. The structural model of the block is established by using the makezone and vertical grid subdivision functions.

[0158] S14: Attribute Modeling

[0159] Firstly, the logging data are discretized to generate corresponding data parameters. The lithofacies model is used as the constraint condition of the attribute model to establish parameter models such as porosity, permeability, oil saturation, etc. that can reflect the heterogeneity of underground reservoirs. The collaborative Kriging method is added to make the parameter simulation more accurate.

[0160] S2: Establish a three-dimensional geomechanical model of the target block;

[0161] S3: Perform reservoir numerical simulation and geomechanical simulation, and use the reservoir numerical simulation and geomechanical simulation results and the on-site initial fracturing operation parameters as pre-selected data sets of characteristic indicators;

[0162] S4: Determine the characteristic indexes that affect the effect of repeated fracturing;

[0163] S5: Establish a mathematical model for well selection for repeated fracturing, obtain the weight of each characteristic index, and use the approximate ideal solution sorting method to calculate the Euclidean distance between the characteristic index of the candidate well and the positive and negative ideal solutions;

[0164] S6: Calculate the relative progress between the characteristic indicators of the candidate horizontal wells for repeated fracturing and the ideal solution, and carry out the sorting and optimization of the candidate wells for the potential for repeated fracturing to increase production.

[0165] Example 3

[0166] The method for selecting wells for repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs comprises the following steps:

[0167] S1: Establish a detailed 3D geological model of the target area;

[0168] S2: Establish a three-dimensional geomechanical model of the target block. Based on the three-dimensional fine geological model and with the rock mechanics profile of a single well as a constraint, the three-dimensional geomechanical model of the work area is finely described to obtain the rock mechanics distribution characteristics such as the maximum horizontal principal stress, minimum horizontal principal stress, Young's modulus, Poisson's ratio, etc. under the initial conditions. The specific modeling process is as follows:

[0169] S21: Obtaining rock mechanical parameters

[0170] The rock mechanics parameters of a single well are calculated from logging, core and test data. The rock mechanics parameters mainly include reservoir overburden stress, uniaxial compressive strength, uniaxial tensile strength, Young's modulus, Poisson's ratio, shear modulus, internal friction angle, cohesion, maximum horizontal principal stress, and minimum horizontal principal stress. The direction of ground stress is accurately determined by imaging logging. These parameters are directly used to establish the unit properties around the wellbore and are used for quality control of the 3D geomechanical model.

[0171] S22: Establishment of 3D geomechanical model

[0172] The geostress profile of a single well in the work area is imported into the well trajectory in Petrel and the model is coarsened to obtain the discrete geostress field of the work area. The stress field of the entire work area is obtained through Kriging interpolation. Seismic data and geological structure information are used for inter-well interpolation to ensure the reliability of the three-dimensional distribution of geomechanical parameters, thereby realizing the establishment of a three-dimensional geomechanical model.

[0173] S3: Perform reservoir numerical simulation and geomechanical simulation, and use the reservoir numerical simulation and geomechanical simulation results and the on-site initial fracturing operation parameters as pre-selected data sets of characteristic indicators;

[0174] S4: Determine the characteristic indexes that affect the effect of repeated fracturing;

[0175] S5: Establish a mathematical model for well selection for repeated fracturing, obtain the weight of each characteristic index, and use the approximate ideal solution sorting method to calculate the Euclidean distance between the characteristic index of the candidate well and the positive and negative ideal solutions;

[0176] S6: Calculate the relative progress between the characteristic indicators of the candidate horizontal wells for repeated fracturing and the ideal solution, and carry out the sorting and optimization of the candidate wells for the potential for repeated fracturing to increase production.

[0177] Example 4

[0178] The method for selecting wells for repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs comprises the following steps:

[0179] S1: Establish a detailed 3D geological model of the target area;

[0180] S2: Establish a three-dimensional geomechanical model of the target block;

[0181] S3: Perform reservoir numerical simulation and geomechanical simulation, and use the reservoir numerical simulation and geomechanical simulation results and the on-site initial fracturing operation parameters as pre-selected data sets for characteristic indicators. Specifically, use a reservoir numerical simulator and a geomechanical simulator to perform reservoir numerical simulation and geomechanical simulation on the target block, obtain the rock mechanical parameters, reservoir physical property parameters, production dynamic parameters and on-site initial fracturing operation parameters of the candidate repeated fracturing horizontal well at the end of the initial fracturing, and use these parameters as the pre-selected data sets for characteristic indicators;

[0182] S4: Determine the characteristic indexes that affect the effect of repeated fracturing;

[0183] S5: Establish a mathematical model for well selection for repeated fracturing, obtain the weight of each characteristic index, and use the approximate ideal solution sorting method to calculate the Euclidean distance between the characteristic index of the candidate well and the positive and negative ideal solutions;

[0184] S6: Calculate the relative progress between the characteristic indicators of the candidate horizontal wells for repeated fracturing and the ideal solution, and carry out the sorting and optimization of the candidate wells for the potential for repeated fracturing to increase production.

[0185] Example 5

[0186] The method for selecting wells for repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs comprises the following steps:

[0187] S1: Establish a detailed 3D geological model of the target area;

[0188] S2: Establish a three-dimensional geomechanical model of the target block;

[0189] S3: Perform reservoir numerical simulation and geomechanical simulation, and use the reservoir numerical simulation and geomechanical simulation results and the on-site initial fracturing operation parameters as pre-selected data sets of characteristic indicators;

[0190] S4: Determine the characteristic indexes that affect the effect of repeated fracturing, specifically, determine the characteristic indexes that affect the effect of repeated fracturing, among which the characteristic indexes of rock mechanics parameters include biaxial stress difference, Young's modulus, and Poisson's ratio; the characteristic indexes of reservoir physical parameters include porosity, permeability, oil saturation, and oil layer thickness; the characteristic indexes of initial fracturing construction parameters include drilling rate, number of fracturing stages, average sand ratio, single-stage fracturing fluid volume, single-stage sand volume, and horizontal section length; the characteristic indexes of production dynamics include average annual decline rate, initial pressure cumulative oil production, and formation pressure;

[0191] S5: Establish a mathematical model for well selection for repeated fracturing, obtain the weight of each characteristic index, and use the approximate ideal solution sorting method to calculate the Euclidean distance between the characteristic index of the candidate well and the positive and negative ideal solutions;

[0192] S6: Calculate the relative progress between the characteristic indicators of the candidate horizontal wells for repeated fracturing and the ideal solution, and carry out the sorting and optimization of the candidate wells for the potential for repeated fracturing to increase production.

Claims

1. A method for selecting wells for repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs, characterized in that: The following steps are involved: S1: Establish a detailed 3D geological model of the target area; S2: Establish a three-dimensional geomechanical model of the target block; S3: Perform reservoir numerical simulation and geomechanical simulation, and use the reservoir numerical simulation and geomechanical simulation results and the on-site initial fracturing operation parameters as pre-selected data sets of characteristic indicators; S4: Determine the characteristic indexes that affect the effect of repeated fracturing; S5: Establish a mathematical model for well selection for repeated fracturing, obtain the weight of each characteristic index, and use the approximate ideal solution sorting method to calculate the Euclidean distance between the characteristic index of the candidate well and the positive and negative ideal solutions; S6: Calculate the relative progress between the characteristic indicators of the candidate horizontal wells for repeated fracturing and the ideal solution, and carry out the sorting and optimization of the candidate wells for the potential for repeated fracturing to increase production.

2. The method for selecting wells for repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs according to claim 1, characterized in that: S1 is to carry out a detailed reservoir description of the target block to obtain the distribution of properties such as porosity, permeability, oil saturation, etc. under initial conditions. The specific modeling process is as follows: S11: Import wellhead data and well trajectory data The wellhead data and well data of all wells in the block where the candidate horizontal well is located are sorted out. The wellhead data includes the geodetic coordinates and core elevation of the wells in the block, and the well trajectory data includes the sounding, well inclination and azimuth of the wells in the block. The wellhead data and well trajectory data are imported into the petrel geological modeling software in turn; S12: Importing well logging data Well logging data mainly include porosity, permeability, shale content, water saturation and acoustic time difference, etc., and outliers in the well logging data are eliminated according to the calculator function of Petrel geological modeling software; S13: Stratigraphic division and structural modeling The stratum division is carried out by combining the oil group, sand group, sub-layer and sand body division comparison data of each well with the seismic interpretation layer data. The top and bottom surface models of each isochronous layer are generated by interpolation method and layer data. Then, the models of each layer are spatially superimposed to establish the spatial grid of the reservoir. The structural model of the block is established by using the makezone and vertical grid subdivision functions. S14: Attribute Modeling Firstly, the logging data are discretized to generate corresponding data parameters. The lithofacies model is used as the constraint condition of the attribute model to establish parameter models such as porosity, permeability, oil saturation, etc. that can reflect the heterogeneity of underground reservoirs. The collaborative Kriging method is added to make the parameter simulation more accurate.

3. The method for selecting wells for repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs according to claim 1, characterized in that: Based on the three-dimensional fine geological model, with the single well rock mechanics profile as a constraint, the three-dimensional geomechanical model of the work area is finely described, and the rock mechanics distribution characteristics such as the maximum horizontal principal stress, the minimum horizontal principal stress, the Young's modulus, and the Poisson's ratio under the initial conditions are obtained. The specific modeling process is as follows: S21: Obtaining rock mechanical parameters The rock mechanics parameters of a single well are calculated from logging, core and test data. The rock mechanics parameters mainly include reservoir overburden stress, uniaxial compressive strength, uniaxial tensile strength, Young's modulus, Poisson's ratio, shear modulus, internal friction angle, cohesion, maximum horizontal principal stress, and minimum horizontal principal stress. The direction of ground stress is accurately determined by imaging logging. These parameters are directly used to establish the unit properties around the wellbore and are used for quality control of the 3D geomechanical model. S22: Establishment of 3D geomechanical model The geostress profile of a single well in the work area is imported into the well trajectory in Petrel and the model is coarsened to obtain the discrete geostress field of the work area. The stress field of the entire work area is obtained through Kriging interpolation. Seismic data and geological structure information are used for inter-well interpolation to ensure the reliability of the three-dimensional distribution of geomechanical parameters, thereby realizing the establishment of a three-dimensional geomechanical model.

4. The method for selecting wells for repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs according to claim 1, characterized in that: In S3, a reservoir numerical simulator and a geomechanical simulator are used to perform reservoir numerical simulation and geomechanical simulation on the target block to obtain rock mechanical parameters, reservoir physical property parameters, production dynamic parameters and on-site initial fracturing construction parameters of the candidate repeated fracturing horizontal well at the end of the initial fracturing, and these parameters are used as pre-selected data sets of characteristic indicators.

5. The method for selecting wells for repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs according to claim 1, characterized in that: Determine the characteristic indicators that affect the effect of repeated fracturing, among which the characteristic indicators of rock mechanics parameters include biaxial stress difference, Young's modulus, and Poisson's ratio; the characteristic indicators of reservoir physical properties include porosity, permeability, oil saturation, and oil layer thickness; the characteristic indicators of initial fracturing construction parameters include drilling rate, number of fracturing stages, average sand ratio, single-stage fracturing fluid volume, single-stage sand volume, and horizontal section length; the dynamic characteristic indicators of production include average annual decline rate, initial pressure cumulative oil production, and formation pressure.

6. The method for selecting wells for repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs according to claim 1, characterized in that: In S5, the grey correlation method is used to obtain the weight of each characteristic index. It is necessary to determine the positive and negative ideal solutions that affect the repeated fracturing effect. On this basis, the approximate ideal solution sorting method is used to calculate the Euclidean distance between the characteristic index of the candidate well and the positive and negative ideal solutions. The specific steps are as follows: S51: Establish an initial matrix using the pre-selected data set of characteristic indicators: Where n represents the number of candidate re-fracturing wells, and m represents the characteristics of each candidate re-fracturing well; S52: Standardize the initial matrix to eliminate the inconsistency of magnitude and units between feature indices: S53: Use the grey correlation analysis method to calculate the correlation of each characteristic index Firstly, the matrix formed after dimensionless transformation is established to determine the position of each characteristic index, and the initial pressure cumulative oil production is taken as the parent sequence, and the biaxial stress difference, Young's modulus, Poisson's ratio, porosity, permeability, oil saturation, oil layer thickness, drilling rate, number of fracturing stages, average sand ratio, single-stage fracturing fluid volume, single-stage sand volume, horizontal section length, average annual decline rate, and formation pressure are taken as subsequences; In the formula, X0 represents the parent sequence, X1-X m represents a subsequence; Then calculate the grey correlation degree between each subsequence and the parent sequence: Where ΔX 0i (k) represents the absolute difference between the i-th characteristic index of the k-th candidate well and the corresponding reference value of the well, ρ is the resolution coefficient, ranging from 0.1 to 1, and γ 0i is the average value of grey relational degree; S54: Obtain the grey correlation coefficient weight of each characteristic index: S55: Assign the weight of the grey correlation coefficient to each dimensionless characteristic index to obtain a weighted standardized matrix: S56: Determine the positive and negative ideal solutions that affect the effect of refracturing: S57: Obtain the Euclidean distance between each characteristic index of the candidate well and the positive and negative ideal solutions:

7. The method for selecting wells for repeated fracturing of horizontal wells in ultra-low permeability oil reservoirs according to claim 1, characterized in that: Calculate the relative progress between the characteristic indicators of the candidate horizontal wells for repeated fracturing and the ideal solution. The relative progress indicates the degree of proximity between the evaluation object and the ideal object. The characteristic indicators of the candidate wells are taken as an example of the degree of proximity to the positive ideal. The smaller the value, the closer the corresponding characteristic indicators of the candidate horizontal wells for repeated fracturing are to the characteristic indicators of the ideal horizontal wells for repeated fracturing. Sort the relative progress from small to large. The smaller the relative progress, the greater the re-fracturing potential of the candidate well. In formula (10), N j It is expressed as the relative progress of the j-th well.

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