Oil and gas reservoir engineering quality single well evaluation method

Abstract

The invention provides an oil and gas reservoir engineering quality single well evaluation method which comprises the following steps: collecting core samples at different depth points, and carrying out a rock mechanics experiment to obtain rock mechanics parameters; a BP neural network method is used for establishing a quantitative relation between experiment numerical values of different depth points and corresponding logging curve scale values, and a single-well full-depth section rock mechanical parameter profile is constructed; and determining weights of different rock mechanical parameters by using an entropy weight method, constructing reservoir engineering quality single-well evaluation indexes, dividing evaluation levels, and quantitatively evaluating the single-well reservoir engineering quality. According to the method, by directly establishing the relation between the rock mechanical parameters and the multiple logging curves, the response characteristics of the logging curves to the reservoir rock mechanical parameters are improved, and the applicability and practicability to a single well are higher; and performing engineering quality evaluation on the oil and gas reservoirs of different depth sections by using the quantified reservoir engineering quality. And the method has more accurate guiding significance for optimization of an oil and gas reservoir transformation scheme.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas geology, and in particular relates to a single-well evaluation method for oil and gas reservoir engineering quality. Background Art

[0002] my country is rich in oil and gas resources. On the basis of exploring and determining the oil and gas sweet spots, how to effectively develop them becomes the key. However, when faced with poor reservoir engineering quality conditions, even if there are good oil and gas sweet spots, it is sometimes difficult to achieve efficient development results. Therefore, it is urgent to carry out oil and gas reservoir engineering quality evaluation.

[0003] The invention patent with authorization announcement number CN112502701B proposes a comprehensive geological-engineering classification evaluation method for low permeability reservoirs, including constructing reservoir geological classification evaluation indicators: using the gray correlation method to obtain the main controlling factors of reservoir geology that affect the fracturing effect of low permeability reservoirs, and based on the main controlling factors of reservoir geology, using the principal component analysis method to construct low permeability reservoir geological classification evaluation indicators; constructing reservoir engineering classification evaluation indicators: using the gray correlation method to obtain the main controlling factors of geomechanics that affect the fracturing effect of low permeability reservoirs, and based on the main controlling factors of geomechanics, using the hierarchical analysis method to construct low permeability reservoir engineering classification evaluation indicators; dividing reservoir categories according to the classification boundary values ​​of reservoir geological classification evaluation indicators and reservoir engineering classification evaluation indicators. When performing logging analysis of rock mechanical parameters, the above method only uses acoustic time difference and density, and does not comprehensively consider the response of other logging data to rock mechanical parameters, and the accuracy is relatively low.

[0004] Authorization announcement number CN109102180B proposed a comprehensive parameter evaluation method for double sweet spot evaluation of tight sandstone reservoirs, including the following steps: 1) establishing a three-dimensional geological structure model; 2) establishing a three-dimensional geological attribute model; 3) selecting evaluation indicators; 4) selecting evaluation methods, including hierarchical analysis method and fuzzy comprehensive discrimination method. However, no quantitative indicators were proposed for evaluation. Summary of the invention

[0005] The purpose of the present invention is to provide a single well evaluation method for oil and gas reservoir engineering quality, so as to overcome the above-mentioned technical problems existing in the prior art.

[0006] To this end, the technical solution provided by the present invention is as follows:

[0007] A single well evaluation method for oil and gas reservoir engineering quality comprises the following steps:

[0008] Step 1) collecting at least 15 groups of drilling core samples at different depths, each group of samples contains at least 4 samples, and the samples are plunger samples with a height of at least 5 cm;

[0009] Step 2) collecting logging data of drilling, the logging data extraction interval is 0.1-0.125m, the logging data at least includes well diameter CAL, acoustic wave time difference AC, natural gamma GR, natural potential SP, deep lateral resistivity RD, shallow lateral resistivity RS, compensated neutron CNL and density DEN, and adopts the eigenvalue correspondence method to perform depth correction using core data;

[0010] Step 3) performing logarithmic processing on the deep lateral resistivity RD and the shallow lateral resistivity RS collected in step 2); extracting the logging data of the three locations of the corresponding depth and the depth above and below the depth of the sample collected in step 1), calculating the average value thereof, and using it as the scale value of the logging curve corresponding to the experimental value at different depth points;

[0011] Step 4) According to the rock mechanics test experimental standards, triaxial rock mechanics experiments, fracture toughness experiments and Brazilian splitting experiments are carried out to obtain the rock elastic modulus E at different depths. j , Poisson's ratio μ j 、Internal friction angle φ j , fracture toughness K ICj and tensile strength σ tj , where j represents different depths;

[0012] Step 5) randomly select at least 14 groups of rock mechanics experimental results obtained in step 4) as the initial data body, and jointly establish a training set with eight types of logging calibration values ​​of wellbore CAL, acoustic wave time difference AC, natural gamma GR, natural potential SP, deep lateral resistivity RD, shallow lateral resistivity RS, compensated neutron CNL and density DEN calculated in step 3), and use the BP neural network method to construct a quantitative relationship model between the experimental values ​​in the training set and the corresponding logging curve calibration values:

[0013] Step 6) For the unselected rock mechanics experimental results, the relationship model constructed in step 5) is used to perform error analysis. When the experimental test results and the logging calculation results meet the set conditions, it means that the relationship constructed in step 5) is accurate, otherwise, return to step 5) to obtain a new relationship model;

[0014] Step 7) Based on the quantitative relationship model constructed in step 5), eight types of logging data, namely, wellbore CAL, acoustic wave time difference AC, natural gamma GR, natural potential SP, deep lateral resistivity RD, shallow lateral resistivity RS, compensated neutron CNL and density DEN at different depths, are used to obtain the corresponding five logging data, namely, elastic modulus, Poisson's ratio, internal friction angle, fracture toughness and tensile strength, so as to construct the rock mechanical parameter profile of elastic modulus, Poisson's ratio, internal friction angle, fracture toughness and tensile strength of the whole depth section of a single well;

[0015] Step 8) normalizing the elastic modulus, Poisson's ratio, internal friction angle, fracture toughness and tensile strength of the single well at full depth calculated in step 7), and then using the entropy weight method to determine the weight of each parameter to construct a reservoir engineering single well evaluation index W;

[0016] Step 9) Calculate the reservoir engineering single well evaluation index W obtained in step 8), and divide the reservoir engineering quality evaluation level into 4 levels, level I: W>2.0, level II: 2.0>W>1.5, level III: 1.5>W>1.0, level IV: 1.0>W>0; during development, the priority order is level I, level II, level III, level IV.

[0017] Step 4) tensile strength σ tj The calculation formula is as follows:

[0018]

[0019] In the formula, σ tj is the maximum tensile stress at the center of the specimen, that is, the tensile strength; p is the ultimate pressure when the specimen is destroyed; d and t are the diameter and thickness of the pressure disk respectively;

[0020] Fracture toughness K ICj The calculation formula is as follows:

[0021]

[0022] Where P max is the maximum breaking load value, D is the sample diameter, B is the sample thickness; Y min* is the dimensionless critical stress intensity factor of the sample, which is determined only by the geometric parameters of the rock sample.

[0023] Step 4) Rock elastic modulus E j , Poisson's ratio μ j The following calculation formulas are obtained respectively:

[0024] E j =(σ1-σ3) (50) / ε h(50)

[0025] μ j =ε d(50) / ε h(50)

[0026] Where, (σ1-σ3) (50) is 50% of the maximum principal stress difference of the specimen, ε h(50) =(σ1-σ3) (50) The corresponding axial compressive strain, ε d(50) =(σ1-σ3) (50) The corresponding circumferential compressive strain.

[0027] The quantitative relationship model obtained in step 5) is:

[0028] R ij =f(r ij )=f(CAL j , A.C. j , G.R. j , S.P. j ,DEN j , CNL j , lnRD j , inRS j )

[0029] Specifically:

[0030]

[0031] Where: R represents the rock mechanical parameter value obtained from the experiment, elastic modulus E j , Poisson's ratio μ j 、Internal friction angle φ j , fracture toughness K ICj and tensile strength σ tj All are obtained indirectly through experiments, i represents different types of rock mechanics parameters, j represents different depth points, and r represents the corresponding logging curve scale value.

[0032] The setting condition in step 6) is |calculated value - measured value| / measured value×100%<10%.

[0033] The normalization formula in step 8) is as follows:

[0034]

[0035] In the formula, z ij represents the rock mechanics parameter curve value obtained in step 7), z ijmax and z ijmin Respectively represent the curve data volume z ij The maximum and minimum values ​​in Z ij For z ij The standardized logging curve data value.

[0036] The reservoir engineering single well evaluation index W in step 8) is W = ∑W j , where W j For the reservoir engineering single well evaluation index at each depth point, the calculation formula is as follows:

[0037]

[0038] In the formula, α iRepresents the weight of the influence of the i-th rock mechanical parameter on the reservoir engineering quality.

[0039] Rock mechanics parameter weight α i The calculation formula is as follows:

[0040]

[0041] Among them, Σα i =1,e i is the information entropy of the i-th rock mechanical parameter, and the calculation formula is:

[0042]

[0043] Where m represents the number of depth points j, p ij It represents the proportion of the value of the i-th rock mechanical parameter at depth j and the probability used in entropy calculation.

[0044] p ij The calculation formula is as follows:

[0045]

[0046] In the formula, Z ij is the standardized logging curve data value.

[0047] The beneficial effects of the present invention are:

[0048] The present invention improves the response characteristics of reservoir rock mechanical parameters by using logging curves by directly establishing the relationship between rock mechanical parameters and multiple logging curves, and has stronger applicability and practicality for single wells; the quantitative reservoir engineering quality evaluation technology is used to evaluate the engineering quality of oil and gas reservoirs at different depths according to reservoir evaluation indicators, and when evaluating using the reservoir engineering quality comprehensive evaluation indicator w, the rock mechanical layer segments with better identification are further subdivided. It has more accurate guiding significance for the optimization of oil and gas reservoir transformation plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of the method of the present invention;

[0050] Figure 2 It is a comprehensive bar chart of the engineering quality evaluation results of Well X in the study area of ​​the embodiment. DETAILED DESCRIPTION

[0051] The following describes the implementation of the present invention through specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification.

[0052] The exemplary embodiments of the present invention are now described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention.

[0053] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0054] Example 1

[0055] This embodiment provides a single well evaluation method for oil and gas reservoir engineering quality, such as Figure 1 As shown, the following steps are included:

[0056] Step 1) collecting at least 15 groups of drilling core samples at different depths, each group of samples contains at least 4 samples, and the samples are plunger samples with a height of at least 5 cm;

[0057] Step 2) collecting logging data of drilling, the logging data extraction interval is 0.1-0.125m, the logging data at least includes well diameter CAL, acoustic wave time difference AC, natural gamma GR, natural potential SP, deep lateral resistivity RD, shallow lateral resistivity RS, compensated neutron CNL and density DEN, and adopts the eigenvalue correspondence method to perform depth correction using core data;

[0058] Step 3) performing logarithmic processing on the deep lateral resistivity RD and the shallow lateral resistivity RS collected in step 2); extracting the logging data of the three locations of the corresponding depth and the depth above and below the depth of the sample collected in step 1), calculating the average value thereof, and using it as the scale value of the logging curve corresponding to the experimental value at different depth points;

[0059] Step 4) According to the rock mechanics test experimental standards, triaxial rock mechanics experiments, fracture toughness experiments and Brazilian splitting experiments are carried out to obtain the rock elastic modulus E at different depths. j , Poisson's ratio μ j 、Internal friction angle φ j , fracture toughness K ICj and tensile strength σ tj , where j represents different depths;

[0060] Step 5) randomly select at least 14 groups of rock mechanics experimental results obtained in step 4) as the initial data body, and jointly establish a training set with eight types of logging calibration values ​​of wellbore CAL, acoustic wave time difference AC, natural gamma GR, natural potential SP, deep lateral resistivity RD, shallow lateral resistivity RS, compensated neutron CNL and density DEN calculated in step 3), and use the BP neural network method to construct a quantitative relationship model between the experimental values ​​in the training set and the corresponding logging curve calibration values:

[0061] Step 6) For the unselected rock mechanics experimental results, the relationship model constructed in step 5) is used to perform error analysis. When the experimental test results and the logging calculation results meet the set conditions, it means that the relationship constructed in step 5) is accurate, otherwise, return to step 5) to obtain a new relationship model;

[0062] Step 7) Based on the quantitative relationship model constructed in step 5), eight types of logging data, namely, wellbore CAL, acoustic wave time difference AC, natural gamma GR, natural potential SP, deep lateral resistivity RD, shallow lateral resistivity RS, compensated neutron CNL and density DEN at different depths, are used to obtain the corresponding five logging data, namely, elastic modulus, Poisson's ratio, internal friction angle, fracture toughness and tensile strength, so as to construct the rock mechanical parameter profile of elastic modulus, Poisson's ratio, internal friction angle, fracture toughness and tensile strength of the whole depth section of a single well;

[0063] Step 8) normalizing the elastic modulus, Poisson's ratio, internal friction angle, fracture toughness and tensile strength of the single well at full depth calculated in step 7), and then using the entropy weight method to determine the weight of each parameter to construct a reservoir engineering single well evaluation index W;

[0064] Step 9) Calculate the reservoir engineering single well evaluation index W obtained in step 8), and divide the reservoir engineering quality evaluation level into 4 levels, level I: W>2.0, level II: 2.0>W>1.5, level III: 1.5>W>1.0, level IV: 1.0>W>0; during development, the priority order is level I, level II, level III, level IV.

[0065] During development, relatively good layers are preferred for fracturing transformation, that is, development and transformation are carried out according to the priority order of level I, level II, level III, and level IV.

[0066] There are few existing methods for evaluating reservoir engineering quality. Well logging calculates rock-related mechanical parameters using empirical formulas, and only uses two logging curves, acoustic time difference and density. These two logging curves do not respond comprehensively to the mechanical parameters of reservoir rocks. For example, when the reservoir contains oil, gas and water fluids, its rock mechanical properties will change with the filling degree and properties of the fluid. When only acoustic time difference and density are used for rock mechanical logging analysis, its recognition accuracy is limited and cannot accurately reflect the changes in rock mechanical parameters. Second, the existing evaluation technology lacks quantitative evaluation indicators, which means that it responds poorly to the changes in rock mechanical parameters in the same layer. Therefore, the engineering quality evaluation of the reservoir is in the quantitative evaluation stage and does not have the accuracy of quantitative evaluation. When selecting the reservoir transformation layer, subjective selection is more often used, lacking the support of comprehensive evaluation indicator data for reservoir engineering quality.

[0067] The method of the present invention provides a single-well evaluation method for the engineering quality of oil and gas reservoirs with simple steps and high accuracy for tight oil and gas reservoirs. Its practical significance lies in that, firstly, by directly establishing the relationship between rock mechanical parameters and multiple well logging curves, the response characteristics of reservoir rock mechanical parameters using well logging curves are improved, and the applicability and practicality for single wells are stronger; using quantitative reservoir engineering quality evaluation technology, the engineering quality of oil and gas reservoirs at different depths is evaluated according to reservoir evaluation indicators, and when evaluating using the reservoir engineering quality comprehensive evaluation indicator w, it is further subdivided on the basis of identifying better rock mechanical layers. It has more accurate guiding significance for the optimization of oil and gas reservoir transformation plans.

[0068] Example 2

[0069] Based on Example 1, this example provides a single well evaluation method for oil and gas reservoir engineering quality, in which the tensile strength σ tj The calculation formula is as follows:

[0070]

[0071] In the formula, σ tj is the maximum tensile stress at the center of the specimen, that is, the tensile strength; p is the ultimate pressure when the specimen is destroyed; d and t are the diameter and thickness of the pressure disk respectively;

[0072] Fracture toughness K ICj The calculation formula is as follows:

[0073]

[0074] Where P max is the maximum breaking load value, D is the sample diameter, B is the sample thickness; Y min* is the dimensionless critical stress intensity factor of the sample, which is determined only by the geometric parameters of the rock sample.

[0075] Rock elastic modulus E j , Poisson's ratio μ j The following calculation formulas are obtained respectively:

[0076] E j =(σ1-σ3) (50) / ε h(50)

[0077] μ j =ε d(50) / ε h(50)

[0078] Where, (σ1-σ3) (50) is 50% of the maximum principal stress difference of the specimen, ε h(50) =(σ1-σ3) (50) The corresponding axial compressive strain, ε d(50) =(σ1-σ3) (50) The corresponding circumferential compressive strain.

[0079]

[0080] In the formula, |τ f | is the shear strength of the rock shear surface, τ o is the inherent shear strength of rock, σ n is the normal stress on the shear surface, and φ is the internal friction angle.

[0081] The quantitative relationship model obtained in step 5) is:

[0082] R ij =f(r ij )=f(CAL j , A.C. j , G.R. j , S.P. j ,DEN j , CNL j , lnRD j , lnRS j )

[0083] Specifically:

[0084]

[0085] Where: R represents the rock mechanical parameter value obtained from the experiment, elastic modulus E j , Poisson's ratio μ j 、Internal friction angle φ j , fracture toughness K ICj and tensile strength σ tjAll of them are obtained indirectly through experiments, i represents different types of rock mechanics parameters, j represents different depth points, and r represents the corresponding logging curve scale value.

[0086] The setting condition in step 6) is |calculated value - measured value| / measured value×100%<10%.

[0087] The normalization formula in step 8) is as follows:

[0088]

[0089] In the formula, z ij represents the rock mechanics parameter curve value obtained in step 7), z ijmax and z ijmin Respectively represent the curve data volume z ij The maximum and minimum values ​​in Z ij For z ij The standardized logging curve data value.

[0090] The reservoir engineering single well evaluation index W in step 8) is W = ∑W j , where W j For the reservoir engineering single well evaluation index at each depth point, the calculation formula is as follows:

[0091]

[0092] In the formula, α i Represents the weight of the influence of the i-th rock mechanical parameter on the reservoir engineering quality.

[0093] Rock mechanics parameter weight α i The calculation formula is as follows:

[0094]

[0095] Among them, Σα i =1,e i is the information entropy of the i-th rock mechanical parameter, and the calculation formula is:

[0096]

[0097] Where m represents the number of depth points j, p ij It represents the proportion of the value of the i-th rock mechanical parameter at depth j and the probability used in entropy calculation.

[0098] p ij The calculation formula is as follows:

[0099]

[0100] In the formula, Z ijis the standardized logging curve data value.

[0101] Example 3

[0102] In order to further illustrate the effect of the present invention, this embodiment takes Well X in the study area as an example for specific introduction.

[0103] Step 1) Collect 15 groups of drilling samples at different depths from Well X in the study area, with 4 samples in each group. The samples are plunger samples with a diameter of 2.5 cm and a height of 5 cm;

[0104] Step 2) collecting logging data of the wells drilled in step 1, the logging data extraction interval is 0.025m, the logging data should at least include well diameter CAL, acoustic wave time difference AC, natural gamma GR, natural potential SP, deep lateral resistivity RLLD, shallow lateral resistivity RLLS, compensated neutron CNL and density DEN, and calibrate them using core data to achieve depth correction;

[0105] Step 3) performing logarithmic processing on the deep lateral resistivity and shallow lateral resistivity collected in step 2), performing equivalent processing on the well diameter, acoustic wave time difference, natural gamma, natural potential, compensated neutron and density, extracting the processed logging data above and below the corresponding depth of the sample collected in step 1, calculating the average value thereof, and using it as the scale value of the logging curve corresponding to the experimental value at different depth points in step 1;

[0106] Step 4) According to the "Methods for Determination of Physical and Mechanical Properties of Coal and Rock" (GB / T 23561.1-2009), refer to the "Recommended Methods for Rock Mechanics Tests" to carry out triaxial rock mechanics, fracture toughness and Brazilian splitting tests to obtain the rock elastic modulus E at different depths. j , Poisson's ratio μ j 、Internal friction angle φ j , fracture toughness K ICj and tensile strength σ tj , where j represents different depths. The results are shown in Table 1.

[0107] Tensile strength tj The calculation formula is as follows:

[0108]

[0109] In the formula, σ tj is the maximum tensile stress at the center of the specimen, that is, the tensile strength; p is the ultimate pressure when the specimen is destroyed; d and t are the diameter and thickness of the pressure disk respectively;

[0110] Fracture toughness K ICj The calculation formula is as follows:

[0111]

[0112] Where P max is the maximum breaking load value, D is the sample diameter, B is the sample thickness; Y min* is the dimensionless critical stress intensity factor of the sample, which is determined only by the geometric parameters of the rock sample.

[0113] Rock elastic modulus E j , Poisson's ratio μ j The following calculation formulas are obtained respectively:

[0114] E j =(σ1-σ3) (50) / ε h(50)

[0115] μ j =ε d(50) / ε h(50)

[0116] Where, (σ1-σ3) (50) is 50% of the maximum principal stress difference of the specimen, ε h(50) =(σ1-σ3) (50) The corresponding axial compressive strain, ε d(50) =(σ1-σ3) (50) The corresponding circumferential compressive strain.

[0117] |τ f |=τ0+σ n ·tgφ

[0118] In the formula, |τ f | is the shear strength of the rock shear surface, τ o is the inherent shear strength of rock, σ n is the normal stress on the shear surface, and φ is the internal friction angle.

[0119] Table 1 Rock mechanics test results

[0121] Step 5) randomly select at least 14 groups of rock mechanics experimental results obtained in step 4) as the initial data body, and jointly establish a training set with the eight types of logging calibration values ​​obtained by processing and calculating in step 3), including wellbore diameter, acoustic wave time difference, natural gamma, natural potential, deep lateral resistivity, shallow lateral resistivity, compensated neutron and density, and use the BP neural network method to construct the quantitative relationship between the experimental values ​​in the training set and the corresponding logging curve calibration values:

[0122] R ij =f(r ij )=f(CAL j , A.C. j , G.R. j, S.P. j , DEN j , CNL j , lnRD j , inRS j )

[0123] Specifically:

[0124]

[0125] R represents the rock mechanical parameter value obtained from the experiment, elastic modulus E j , Poisson's ratio μ j , internal friction angle Fracture toughness K ICj and tensile strength σ tj All of them are obtained indirectly through experiments, i represents different types of rock mechanics parameters, j represents different depth points, and r represents the corresponding logging curve scale value.

[0126] Step 6) uses the unselected rock mechanics experimental results and the relationship model constructed in step 5) to perform error analysis. When the experimental test results and the logging calculation results meet the condition of (|calculated value-measured value| / measured value×100%)<10%, it means that the relationship constructed in step 5) is accurate, otherwise return to step 5) to obtain a new relationship model.

[0127] Step 7) Taking advantage of the vertical continuity of logging data, based on the quantitative relationship between the experimental values ​​and the logging scale values ​​constructed in step 5), eight types of logging data, including wellbore diameter, acoustic wave time difference, natural gamma, natural potential, deep lateral resistivity, shallow lateral resistivity, compensated neutron and density at different depths, are used to construct a rock mechanical parameter profile of elastic modulus, Poisson's ratio, internal friction angle, fracture toughness and tensile strength for the full depth section of a single well.

[0128] Step 8) The elastic modulus, Poisson's ratio, internal friction angle, fracture toughness and tensile strength calculated in step 7) are normalized. On this basis, the entropy weight method is used to determine the weight of each parameter to construct a single well evaluation index W for reservoir engineering.

[0129] The calculation process of the data in steps 5), 6), 7) and 8) is implemented by the MATLAB software programming language. The data are shown in Table 2. The comprehensive bar chart of the evaluation results is shown in Figure 2 The fracturability index in Table 2 is the same as the engineering quality evaluation index W.

[0130] Table 2 Test results at different depths

[0131]

[0132]

[0133] The above examples are merely illustrative of the present invention and do not constitute a limitation on the protection scope of the present invention. All designs that are the same or similar to the present invention fall within the protection scope of the present invention.

Claims

1. A single well evaluation method for oil and gas reservoir engineering quality, characterized in that: The following steps are involved: Step 1) collecting at least 15 groups of drilling core samples at different depths, each group of samples contains at least 4 samples, and the samples are plunger samples with a height of at least 5 cm; Step 2) collecting logging data of drilling, the logging data extraction interval is 0.1-0.125m, the logging data at least includes well diameter CAL, acoustic wave time difference AC, natural gamma GR, natural potential SP, deep lateral resistivity RD, shallow lateral resistivity RS, compensated neutron CNL and density DEN, and adopts the eigenvalue correspondence method to perform depth correction using core data; Step 3) performing logarithmic processing on the deep lateral resistivity RD and the shallow lateral resistivity RS collected in step 2); extracting the logging data of the three locations of the corresponding depth and the depth above and below the depth of the sample collected in step 1), calculating the average value thereof, and using it as the scale value of the logging curve corresponding to the experimental value at different depth points; Step 4) According to the rock mechanics test experimental standards, triaxial rock mechanics experiments, fracture toughness experiments and Brazilian splitting experiments are carried out to obtain the rock elastic modulus E at different depths. j , Poisson's ratio μ j 、Internal friction angle φ j , fracture toughness K ICj and tensile strength σ tj , where j represents different depths, i represents different rock mechanical parameter types, i = 1, 2, ..., 5; Step 5) randomly select at least 14 groups of rock mechanics experimental results obtained in step 4) as the initial data body, and jointly establish a training set with eight types of logging calibration values ​​of wellbore CAL, acoustic wave time difference AC, natural gamma GR, natural potential SP, deep lateral resistivity RD, shallow lateral resistivity RS, compensated neutron CNL and density DEN calculated in step 3), and use the BP neural network method to construct a quantitative relationship model between the experimental values ​​in the training set and the corresponding logging curve calibration values: Step 6) For the unselected rock mechanics experimental results, the relationship model constructed in step 5) is used to perform error analysis. When the experimental test results and the logging calculation results meet the set conditions, it means that the relationship constructed in step 5) is accurate, otherwise, return to step 5) to obtain a new relationship model; Step 7) Based on the quantitative relationship model constructed in step 5), eight types of logging data, namely, wellbore CAL, acoustic wave time difference AC, natural gamma GR, natural potential SP, deep lateral resistivity RD, shallow lateral resistivity RS, compensated neutron CNL and density DEN at different depths, are used to obtain the corresponding five logging data, namely, elastic modulus, Poisson's ratio, internal friction angle, fracture toughness and tensile strength, so as to construct the rock mechanical parameter profile of elastic modulus, Poisson's ratio, internal friction angle, fracture toughness and tensile strength of the whole depth section of a single well; Step 8) normalizing the elastic modulus, Poisson's ratio, internal friction angle, fracture toughness and tensile strength of the single well at full depth calculated in step 7), and then using the entropy weight method to determine the weight of each parameter to construct a reservoir engineering single well evaluation index W; Step 9) Calculate the reservoir engineering single well evaluation index W obtained in step 8), and divide the reservoir engineering quality evaluation level into 4 levels, level I: W>2.0, level II: 2.0>W>1.5, level III: 1.5>W>1.0, level IV: 1.0>W>0; during development, the priority order is level I, level II, level III, level IV.

2. A single well evaluation method for oil and gas reservoir engineering quality according to claim 1, characterized in that: Step 4) tensile strength σ tj The calculation formula is as follows: In the formula, σ tj is the maximum tensile stress at the center of the specimen, that is, the tensile strength; p is the ultimate pressure when the specimen is destroyed; d and t are the diameter and thickness of the pressure disk respectively; Fracture toughness K ICj The calculation formula is as follows: Where P max is the maximum breaking load value, D is the sample diameter, B is the sample thickness; Y min * is the dimensionless critical stress intensity factor of the specimen, which is determined only by the geometric parameters of the rock specimen.

3. The single well evaluation method for oil and gas reservoir engineering quality according to claim 1, characterized in that: Step 4) Rock elastic modulus E j , Poisson's ratio μ j The following calculation formulas are obtained respectively: E j =(σ1-σ3) (50) / e h(50) m j =e d(50) / e h(50) Where, (σ1-σ3) (50) is 50% of the maximum principal stress difference of the specimen, ε h(50) =(σ1-σ3) (50) The corresponding axial compressive strain, ε d(50) =(σ1-σ3) (50) The corresponding circumferential compressive strain.

4. The single well evaluation method for oil and gas reservoir engineering quality according to claim 1, characterized in that: The quantitative relationship model obtained in step 5) is: R ij =f(r ij )=f(CAL j ,AC j ,GR j ,SP j ,DEN j ,CNL j ,lnRD j ,mRS j ) Specifically: Where: R represents the rock mechanical parameter value obtained from the experiment, elastic modulus E j , Poisson's ratio μ j 、Internal friction angle φ j , fracture toughness K ICj and tensile strength σ tj All of them are obtained indirectly through experiments, i represents different types of rock mechanics parameters, j represents different depth points, and r represents the corresponding logging curve scale value.

5. The single well evaluation method for oil and gas reservoir engineering quality according to claim 1, characterized in that: The setting condition in step 6) is |calculated value - measured value| / measured value×100%<10%.

6. The single well evaluation method for oil and gas reservoir engineering quality according to claim 1, characterized in that: The normalization formula in step 8) is as follows: In the formula, z ij represents the rock mechanics parameter curve value obtained in step 7), z ijmax and z ijmin Respectively represent the curve data volume z ij The maximum and minimum values ​​in Z ij For z ij The standardized logging curve data value.

7. The single well evaluation method for oil and gas reservoir engineering quality according to claim 1, characterized in that: The reservoir engineering single well evaluation index W in step 8) is W = ∑W j , where W j For the reservoir engineering single well evaluation index at each depth point, the calculation formula is as follows: In the formula, α i Represents the weight of the influence of the i-th rock mechanical parameter on the reservoir engineering quality.

8. A single well evaluation method for oil and gas reservoir engineering quality according to claim 7, characterized in that: Rock mechanics parameter weight α i The calculation formula is as follows: Among them, ∑α i =1,e i is the information entropy of the i-th rock mechanical parameter, and the calculation formula is: Where m represents the number of depth points j, p ij It represents the proportion of the value of the i-th rock mechanical parameter at depth j and the probability used in entropy calculation.

9. A single well evaluation method for oil and gas reservoir engineering quality according to claim 8, characterized in that: p ij The calculation formula is as follows: In the formula, Z ij is the standardized logging curve data value.

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