Well logging classification and evaluation method, system, device and medium for shale oil pore structure

Abstract

The application discloses a shale oil pore structure logging classification evaluation method, system, device and medium, solves the problems of difficult shale oil pore structure classification evaluation and lack of effective characterization parameters, and realizes continuous characterization and classification evaluation of shale oil pore structure phases. The method comprises the following steps: obtaining nuclear magnetic resonance experimental data of a core sample; based on the nuclear magnetic resonance experimental data, calculating the movable fluid porosity of the core sample by using a T2 spectrum weight coefficient method, and combining the micro characteristics of the core sample to establish a pore structure evaluation standard; constructing a movable fluid porosity curve based on the movable fluid porosity and a T2 geometric mean value; and based on the movable fluid porosity curve, establishing a pore structure phase logging classification response relationship by using a Fisher discriminant method to complete the classification evaluation of the pore structure.

Description

Technical Field

[0001] This invention belongs to the field of unconventional reservoir pore structure interpretation and evaluation technology, specifically relating to a well logging classification and evaluation method, system, equipment and medium for shale oil pore structure. Background Technology

[0002] In the field of oil and gas development technology, the pore structure of rocks refers to the type, size, distribution, and interconnection of pores and throats within the rock. Shale oil reservoirs are characterized by diverse mineral compositions, complex pore structures, and strong heterogeneity. Pore structure evaluation is an important indicator for characterizing the quality of shale oil reservoirs and a key parameter in well logging evaluation. Studying the pore structure of oil and gas reservoirs and revealing their internal structure is of great significance for oil and gas field exploration and development.

[0003] Currently, the main evaluation methods for pore structure include rock physics experimental analysis and well logging evaluation.

[0004] Rock physics experimental analysis methods include thin section casting, scanning electron microscopy, mercury porosimetry, nuclear magnetic resonance, low-temperature nitrogen adsorption, CT scanning, etc., which can intuitively describe pore connectivity in the form of data and images. However, due to the limitations of core sampling conditions and costs, rock physics experimental analysis methods cannot collect enough continuous samples for experiments, making it difficult to meet the needs of fine evaluation and continuous characterization.

[0005] Commonly used well logging evaluation methods include the comprehensive evaluation index method for pore structure and the pseudo-capillary pressure curve method. The comprehensive evaluation index method is mostly applicable to conventional reservoirs, establishing the relationship between the three-porosity curve and the reservoir quality factor, but it is not suitable for shale oil. The pseudo-capillary pressure curve method, to some extent, ignores the influence of film-bound water on the NMR signal of pores in the small-diameter range, and the high-pressure mercury intrusion test data of most shale samples are distorted, failing to truly reflect the internal pore structure of shale. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a well logging classification and evaluation method, system, equipment, and medium for shale oil pore structure, which solves the problems of difficulty in classifying and evaluating shale oil pore structure and lack of effective characterization parameters, and realizes continuous characterization and classification evaluation of shale oil pore structure phases.

[0007] This invention is achieved through the following technical solution:

[0008] A well logging classification and evaluation method for shale oil pore structure includes the following steps:

[0009] Obtain nuclear magnetic resonance experimental data from core samples;

[0010] Based on nuclear magnetic resonance experimental data, the movable fluid porosity of core samples was calculated using the T2 spectral weighting coefficient method, and a pore structure evaluation standard was established in combination with the microscopic characteristics of the core samples.

[0011] A movable fluid porosity curve is constructed based on movable fluid porosity and T2 geometric mean.

[0012] Based on the porosity curve of movable fluid, the Fisher discriminant method is used to establish the well logging classification response relationship of pore structure phase, and to complete the classification evaluation of pore structure.

[0013] Preferably, the nuclear magnetic resonance experimental data includes the T2 geometric mean, bound water saturation, and pore distribution of the core sample analysis.

[0014] Preferably, the calculation model for the porosity of the movable fluid is as follows:

[0015]

[0016] In the formula, FFI represents the porosity of the movable fluid. For the i-th porosity component, n is the total porosity, W i The weighting coefficients are the porosity-bound components corresponding to each transverse relaxation T2 component.

[0017] Preferably, the weighting coefficient is calculated using the following expression:

[0018]

[0019] In the formula, m and b are parameters obtained from the calculation model of movable fluid porosity in practical applications, obtained through the geometric mean of T2 and the statistical analysis of bound water saturation in nuclear magnetic resonance experimental data, respectively. 2i Let be the i-th component of the transverse relaxation T2.

[0020] Preferably, the model expression for the movable fluid porosity curve is:

[0021] FFI′=aT 2LM +c

[0022] In the formula, FFI′ is the porosity curve of the movable fluid, and T 2LM denoted as the geometric mean of T2 in the nuclear magnetic resonance experimental data, and a and c are the model constant parameters, respectively.

[0023] Preferably, the step of establishing the well logging classification response relationship of pore structure phase based on the movable fluid porosity curve and using the Fisher discriminant method includes:

[0024] The influencing factors of shale oil pore structure were analyzed, and a well logging identification chart of shale oil pore structure facies was constructed using the Fisher discriminant method. The pore structure facies discrimination function was obtained, and the pore structure facies discrimination function was used for classification and evaluation.

[0025] Preferably, the influencing factors of the shale oil pore structure include compensated neutron (CNL), density (DEN), sand content (QUA), and clay content (CLA) in the well logging curve.

[0026] A well logging classification and evaluation system for shale oil pore structure includes:

[0027] The data acquisition module is used to acquire nuclear magnetic resonance experimental data from core samples.

[0028] The movable fluid porosity calculation module is used to calculate the movable fluid porosity of core samples based on nuclear magnetic resonance experimental data and the T2 spectral weighting coefficient method, and to establish a pore structure evaluation standard in combination with the microscopic characteristics of the core samples.

[0029] The movable fluid porosity curve construction module is used to construct movable fluid porosity curves based on movable fluid porosity and T2 geometric mean.

[0030] The module for constructing the well logging classification response relationship of pore structure phases is used to establish the well logging classification response relationship of pore structure phases based on the porosity curve of movable fluid and the Fisher discriminant method, so as to complete the classification evaluation of pore structure.

[0031] A well logging classification and evaluation device for shale oil pore structure includes:

[0032] At least one processor and memory;

[0033] The memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory, causing the at least one processor to execute the well logging classification and evaluation method for shale oil pore structure.

[0034] A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement a well logging classification and evaluation method for the pore structure of shale oil.

[0035] Compared with the prior art, the present invention has the following beneficial technical effects:

[0036] This invention provides a well logging classification and evaluation method for shale oil pore structure. Combining the geological characteristics and well logging response patterns of the study area, it proposes using the T2 spectral weighting coefficient method to calculate the movable fluid porosity of shale oil to characterize the pore structure, thereby achieving continuous characterization and classification evaluation of pore structure facies. Specifically: First, based on NMR experimental data, it was found that the T2 cutoff value varies greatly, and the method of calculating movable fluid porosity using a fixed T2 cutoff value has limitations. Therefore, the T2 spectral weighting coefficient method is proposed to determine the movable fluid porosity of shale oil. Then, a mathematical fitting model is used to establish the relationship between the geometric mean of T2 and the movable fluid porosity, thereby achieving rapid and quantitative calculation of the movable fluid porosity of the predicted well at continuous depths, which can intuitively reflect the quality of the pore structure. Furthermore, based on the performance of pore structure facies in well logging response characteristics, the Fisher discriminant method is applied to establish a well logging classification response relationship of pore structure facies based on core pore structure classification, realizing continuous characterization and classification evaluation of pore structure facies. The well logging classification and evaluation method for shale oil pore structure described in this invention is simple, easy to operate, highly reproducible, and its calculation accuracy can meet the needs of field production. It has good universality and promotion value for evaluating shale oil pore structure. Attached Figure Description

[0037] Figure 1 This is a flowchart of the well logging classification and evaluation method for shale oil pore structure according to the present invention;

[0038] Figure 2 This is a comparison chart of the pore structure classification and evaluation results and the oil test results in an embodiment of the present invention. Detailed Implementation

[0039] The principles and features of the present invention will be further described in detail below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention. It should be noted that the accompanying drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clearly illustrate the purpose of the embodiments of the present invention.

[0040] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or it can be in a centered component. When a component is said to be "connected to" another component, it can be directly connected to the other component or it may also be in a centered component. When a component is said to be "set to" another component, it can be directly set on the other component or it may also be in a centered component.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0042] This invention provides a well logging classification and evaluation method for shale oil pore structure, such as... Figure 1 As shown, it includes the following steps:

[0043] Step (1) Nuclear magnetic resonance experiments were conducted on the core samples from the study area to obtain experimental data such as the T2 geometric mean, bound water saturation, and pore distribution of the core samples.

[0044] Step (2) The T2 spectral weighting coefficient method is used to calculate the porosity of the movable fluid in the sample. The pore structure type is classified in combination with the microscopic characteristics of the sample, and a pore structure evaluation standard is established.

[0045] Step (3) uses a mathematical fitting model to establish the relationship between movable fluid porosity and the geometric mean of T2, calculates the movable fluid porosity curve of the predicted well, and realizes quantitative characterization of pore structure;

[0046] Step (4) By analyzing the influencing factors of pore structure, the Fisher discriminant method is used to establish the pore structure phase logging classification response relationship, and the pore structure classification evaluation is completed.

[0047] Furthermore, the nuclear magnetic resonance experimental data of the core analysis mentioned in step (1) were obtained in accordance with the procedures specified in the standard "SY / T6490-2007 Laboratory Measurement Specification for Nuclear Magnetic Resonance Parameters of Rock Samples".

[0048] Furthermore, the calculation of the movable fluid porosity of the sample using the T2 spectral weighting coefficient method described in step (2) is based on a large amount of nuclear magnetic resonance experimental data from core analysis.

[0049] Specifically, based on the assumption that any pore contains both bound and movable fluid, a weighting coefficient for calculating the bound fluid is given to the partitioned porosity corresponding to each T2 component. 2i The weight of the component corresponding to the porosity confinement can be expressed as:

[0050]

[0051] In the formula, m and b are the parameters that need to be determined in practical applications of the model, obtained by statistical analysis of the geometric mean of T2 and bound water saturation data from core analysis, and are dimensionless; T 2i Let be the i-th component of the transverse relaxation T2, ms.

[0052] Specifically, the calculation model for the porosity of movable fluids is as follows:

[0053]

[0054] In the formula, To correspond to the i-th porosity component, the value of i ranges from 1 to n; W i For each T 2i The component corresponds to the weighting coefficient of the porosity-bound portion.

[0055] Specifically, based on the calculation of the movable fluid porosity of all core samples, the pore structure types are classified according to the microscopic characteristics of the samples, and a classification and evaluation standard for pore structure in the study area is established.

[0056] Furthermore, in step (3), the geometric mean of T2 obtained from core samples is statistically analyzed with the corresponding movable fluid porosity to establish a logging interpretation model for movable fluid porosity based on nuclear magnetic resonance logging in the study area. The model expression is as follows:

[0057] FFI′=aT 2LM +c

[0058] In the formula, T 2LM is the geometric mean of T2 in core analysis, in ms; a and c are model parameters.

[0059] Furthermore, in step (4), the method of analyzing the influencing factors of pore structure and establishing the pore structure phase logging classification response relationship is to use Fisher's discriminant method to establish a pore structure phase logging identification chart, obtain the discriminant function, and then carry out classification evaluation.

[0060] Specifically, the phase discrimination function for pore structure is:

[0061] Z1=A1*CNL+B1*DEN+C1*CLA+D1*QFM+E1

[0062] Z2=A2*CNL+B2*DEN+C2*CLA+D2*QFM+E2

[0063] Z3=A3*CNL+B2*DEN+C3*CLA+D3*QFM+E3

[0064] In the formula, Z1, Z2, and Z3 are the discriminant functions for the three types of pore structures, CNL is the compensated neutron (%), DEN is the compensated density (g / cm3), CLA is the clay content (%), QFM is the sand content (%), and A1, A2, A3, B1, B2, B3, C1, C2, C3, D1, D2, D3, E1, E2, and E3 are the parameters of the pore structure influencing factors in the corresponding pore structure discriminant functions.

[0065] This invention provides a well logging classification and evaluation method for shale oil pore structure. Combining the geological characteristics and well logging response patterns of the study area, it proposes using the T2 spectral weighting coefficient method to calculate the movable fluid porosity of shale oil to characterize the pore structure, thereby achieving continuous characterization and classification evaluation of pore structure facies. Specifically: First, based on NMR experimental data, it was found that the T2 cutoff value varies greatly, and the method of calculating movable fluid porosity using a fixed T2 cutoff value has limitations. Therefore, the T2 spectral weighting coefficient method is proposed to determine the movable fluid porosity of shale oil. Then, a mathematical fitting model is used to establish the relationship between the geometric mean of T2 and the movable fluid porosity, thereby achieving rapid and quantitative calculation of the movable fluid porosity of the predicted well at continuous depths, which can intuitively reflect the quality of the pore structure. Furthermore, based on the performance of pore structure facies in well logging response characteristics, the Fisher discriminant method is applied to establish a well logging classification response relationship of pore structure facies based on core pore structure classification, realizing continuous characterization and classification evaluation of pore structure facies. The well logging classification and evaluation method for shale oil pore structure described in this invention is simple, easy to operate, highly reproducible, and its calculation accuracy can meet the needs of field production. It has good universality and promotion value for evaluating shale oil pore structure.

[0066] This invention also provides a well logging classification and evaluation system for shale oil pore structure, used to implement the well logging classification and evaluation method for shale oil pore structure described in this invention, comprising:

[0067] The data acquisition module is used to acquire nuclear magnetic resonance experimental data from core samples.

[0068] The movable fluid porosity calculation module is used to calculate the movable fluid porosity of core samples based on nuclear magnetic resonance experimental data and the T2 spectral weighting coefficient method, and to establish a pore structure evaluation standard in combination with the microscopic characteristics of the core samples.

[0069] The movable fluid porosity curve construction module is used to construct movable fluid porosity curves based on movable fluid porosity and T2 geometric mean.

[0070] The module for constructing the well logging classification response relationship of pore structure phases is used to establish the well logging classification response relationship of pore structure phases based on the porosity curve of movable fluid and the Fisher discriminant method, so as to complete the classification evaluation of pore structure.

[0071] This invention also provides a well logging classification and evaluation device for shale oil pore structure, comprising:

[0072] At least one processor and memory;

[0073] The memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to execute the well logging classification and evaluation method for shale oil pore structure according to the present invention.

[0074] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the processor executes the computer-executable instructions, it implements the well logging classification and evaluation method for shale oil pore structure described in the present invention.

[0075] Example

[0076] This invention provides a well logging classification and evaluation method for shale oil pore structure, comprising the following steps:

[0077] Step one involves conducting nuclear magnetic resonance (NMR) experiments on core samples from the study area to obtain experimental data such as the geometric mean of T2, bound water saturation, and pore distribution. These data were obtained according to the procedures specified in the standard SY / T 6490-2007 "Laboratory Measurement Specifications for Nuclear Magnetic Resonance Parameters of Rock Samples".

[0078] Step 2: The movable fluid porosity of 66 samples in the study area was calculated using the T2 spectral weighting coefficient method.

[0079]

[0080] In the formula, For the i-th porosity component; W i For each T 2i The component corresponds to the weighting coefficient of the porosity-bound portion.

[0081]

[0082] In the formula, m and b are the model parameters, respectively, and T 2i Let be the i-th component of the transverse relaxation T2, ms.

[0083] By statistically analyzing the relationship between the geometric mean of T2 and the bound water saturation of core samples from multiple wells in the study area using nuclear magnetic resonance experiments, the two parameters m and b in the model were determined using mathematical fitting.

[0084] Where m = -5.3701, b = 53.535, the correlation coefficient of the model reached 0.765.

[0085] The porosity of the movable fluid in the 66 samples is calculated as shown in Table 1.

[0086] Table 1. Calculation results of movable fluid porosity in the samples.

[0087]

[0088]

[0089] Based on the microscopic characteristics of the samples, the pore structure types were classified into three categories. The pore structure classification and evaluation criteria are shown in Table 2.

[0090] Table 2 Classification and Evaluation Criteria for Pore Structure

[0091]

[0092] Step 3: Calculate the porosity of movable fluids based on nuclear magnetic resonance logging, such as... Figure 2 The 6th curve in the middle:

[0093] FFI′=aT 2LM +c

[0094] In the formula, T 2LM is the geometric mean of T2 in core analysis, in ms; a and c are model parameters.

[0095] Statistical analysis was performed on the geometric mean of T2 obtained from core samples and the corresponding movable fluid porosity. The two parameters a and c in the model were determined by mathematical fitting.

[0096] Where a = 0.4115, c = 0.0078, the correlation coefficient of the model reached 0.807;

[0097] Step four, combining the geological characteristics and well logging response patterns of the study area, it is analyzed that the influencing factors of pore structure are related to the compensated neutron (CNL), density (DEN), sand content (QUA), and clay content (CLA) parameters in the well logging curves. Therefore, Fisher's discriminant method is used to establish a well logging identification chart for pore structure facies, and the discriminant function is obtained as follows:

[0098] Z1=20.221*CNL+1952.935*DEN-1.606*CLA+3.068*QFM-2717.868

[0099] Z2=20.254*CNL+1966.643*DEN-1.71*CLA+2.826*QFM-2734.119

[0100] Z3=19.841*CNL+1933.832*DEN-1.116*CLA+2.949*QFM-2674.435

[0101] In the formula, CNL is the compensated neutron, %; DEN is the compensated density, g / cm3; CLA is the clay content, %; and QFM is the sand content, %.

[0102] This method is used to quantitatively classify pore structure phases in well logging, such as... Figure 2 The 9th result shows good consistency with the NMR T2 spectrum and the oil test results.

[0103] In actual data processing, a well logging classification and evaluation method for shale oil pore structure is implemented by writing a program. Figure 2 This image shows a comparison between the pore structure classification evaluation results and the well testing results. Following steps one through four, and combining the geological characteristics and well logging response patterns of the study area, the movable fluid porosity of the shale oil samples was calculated based on core NMR experimental data. A pore structure classification evaluation standard for the study area was established based on microscopic characteristics. Then, a mathematical fitting model was used to establish the relationship between the T2 geometric mean and movable fluid porosity, thereby calculating the movable fluid porosity of the predicted well. Finally, by observing the performance of pore structure facies in well logging response characteristics, Fisher's discriminant method was applied, based on the core pore structure classification, to establish a well logging classification response relationship for pore structure facies, thus achieving continuous characterization and classification evaluation of pore structure facies. The actual data processing results show that the pore structure classification evaluation results, both in terms of shape variation and numerical values, are highly consistent with the T2 NMR spectrum and the well testing results, demonstrating the feasibility of this method.

[0104] The method provided in this invention, combining the geological characteristics and well logging response patterns of the study area, firstly calculates the movable fluid porosity of shale oil samples using the T2 spectral weighting coefficient method based on core NMR experimental data and establishes a pore structure classification and evaluation standard. Then, a mathematical fitting model is used to establish the relationship between the T2 geometric mean and movable fluid porosity, calculating the movable fluid porosity of the predicted well, which can intuitively reflect the quality of the pore structure. Furthermore, by observing the performance of pore structure facies in well logging response characteristics, the Fisher discriminant method is applied, based on the core pore structure classification, to establish a well logging classification response relationship for pore structure facies, thereby achieving continuous characterization and classification evaluation of pore structure facies. This invention is simple, easy to operate, and highly reproducible, possessing good universality and promotional value for shale oil pore structure evaluation.

[0105] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0106] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the above description. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A well logging classification and evaluation method for shale oil pore structure, characterized in that, The steps include the following: Obtain nuclear magnetic resonance experimental data from core samples; Based on nuclear magnetic resonance experimental data, the movable fluid porosity of core samples was calculated using the T2 spectral weighting coefficient method. A pore structure evaluation standard was established by combining the microscopic characteristics of the core samples. The calculation model for the movable fluid porosity is as follows: In the formula, Porosity of movable fluid For the i-th porosity component, n is the total porosity, W i The weighting coefficients for the porosity-bound portion corresponding to each transverse relaxation T2 component; A movable fluid porosity curve is constructed based on movable fluid porosity and T2 geometric mean. Based on the porosity curve of movable fluid, the Fisher discriminant method is used to establish the well logging classification response relationship of pore structure phase, and to complete the classification evaluation of pore structure.

2. The well logging classification and evaluation method for shale oil pore structure according to claim 1, characterized in that, The nuclear magnetic resonance experimental data include the T2 geometric mean, bound water saturation, and pore distribution of the core samples.

3. The well logging classification and evaluation method for shale oil pore structure according to claim 1, characterized in that, The formula for calculating the weighting coefficient is as follows: In the formula, m and b are parameters obtained from the calculation model of movable fluid porosity in practical applications, obtained through the geometric mean of T2 and the statistical analysis of bound water saturation in nuclear magnetic resonance experimental data, respectively. 2i Let be the i-th component of the transverse relaxation T2.

4. The well logging classification and evaluation method for shale oil pore structure according to claim 1, characterized in that, The model expression for the movable fluid porosity curve is: In the formula, For the porosity curve of the movable fluid, T 2LM denoted as the geometric mean of T2 in the nuclear magnetic resonance experimental data, and a and c are the model constant parameters, respectively.

5. The well logging classification and evaluation method for shale oil pore structure according to claim 1, characterized in that, The establishment of the well logging classification response relationship based on the porosity curve of movable fluid and using Fisher's discriminant method includes: The influencing factors of shale oil pore structure were analyzed, and a well logging identification chart of shale oil pore structure facies was constructed using the Fisher discriminant method. The pore structure facies discrimination function was obtained, and the pore structure facies discrimination function was used for classification and evaluation.

6. The well logging classification and evaluation method for shale oil pore structure according to claim 5, characterized in that, The factors influencing the pore structure of shale oil include compensated neutron (CNL), density (DEN), sand content (QUA), and clay content (CLA) in the well logging curve.

7. A well logging classification and evaluation system for shale oil pore structure, characterized in that, The well logging classification and evaluation method based on the pore structure of shale oil according to any one of claims 1-6 includes: The data acquisition module is used to acquire nuclear magnetic resonance experimental data from core samples. The movable fluid porosity calculation module is used to calculate the movable fluid porosity of core samples based on nuclear magnetic resonance experimental data and the T2 spectral weighting coefficient method, and to establish a pore structure evaluation standard in combination with the microscopic characteristics of the core samples. The movable fluid porosity curve construction module is used to construct movable fluid porosity curves based on movable fluid porosity and T2 geometric mean. The module for constructing the well logging classification response relationship of pore structure phases is used to establish the well logging classification response relationship of pore structure phases based on the porosity curve of movable fluid and the Fisher discriminant method, so as to complete the classification evaluation of pore structure.

8. A well logging classification and evaluation device for shale oil pore structure, characterized in that, include: At least one processor and memory; The memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to execute the well logging classification and evaluation method for shale oil pore structure as claimed in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by the processor, implement the well logging classification and evaluation method for shale oil pore structure as described in any one of claims 1-6.

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