A reservoir comprehensive interpretation and evaluation method based on dimension reduction fitting of logging sensitivity parameters

Abstract

The application discloses a reservoir comprehensive interpretation and evaluation method based on logging sensitive parameter dimension reduction fitting, data acquisition and preprocessing; sensitive parameter optimization and calculation; factor analysis is carried out on each sensitive parameter after standardization processing to extract common factors, and an original factor loading matrix is obtained and is subjected to orthogonal rotation to obtain a rotated factor loading matrix; a factor score coefficient matrix is obtained according to the rotated factor loading matrix, and a first comprehensive index and a second comprehensive index after dimension reduction are constructed for the reservoir of each target well section; according to the gathering characteristics of the fluid property data of each target well section reservoir on a two-dimensional crossplot board established by the first comprehensive index and the second comprehensive index, the distribution areas of different fluid types are demarcated, the discrimination standard is determined, and the fluid property of an unknown reservoir is interpreted and evaluated. The application can solve the problems of low interpretation coincidence rate of the existing single interpretation method and unclear identification of the fluid property of a complex reservoir.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas geological exploration technology, specifically relating to a comprehensive reservoir interpretation and evaluation method based on dimension reduction fitting of logging sensitive parameters. Background Technology

[0002] Accurate interpretation and evaluation of logging data is a key basis for the efficient development of unconventional oil and gas reservoirs. In actual exploration, when faced with complex reservoir genesis, low porosity and permeability, and strong heterogeneity, a single logging interpretation method often faces enormous challenges.

[0003] Specifically, while existing gas logging interpretation methods such as the Pixler chart and triangular chart, and geochemical logging interpretation methods such as rock pyrolysis charts each have their advantages, they also have the following drawbacks:

[0004] (1) Single parameters and poor anti-interference ability: Gas logging data is easily affected by drilling engineering parameters such as drilling time, displacement and drill bit diameter, and is also easily affected by drilling fluid properties. Drilling time is the time required to drill one meter of formation during the drilling process; while rock pyrolysis data has limitations in determining reservoir water content.

[0005] (2) Low interpretation accuracy: For complex geological conditions, the interpretation accuracy of a single logging interpretation method is usually around 75%, which is difficult to meet the needs of high-precision exploration.

[0006] (3) Difficulty in multi-parameter fusion: Although logging has obtained massive amounts of data, there is collinearity or correlation between different parameters. Directly performing multi-parameter intersection will result in excessive dimensionality, making it difficult to evaluate intuitively, and data redundancy will interfere with judgment. Summary of the Invention

[0007] In view of the shortcomings of the prior art, the present invention provides a comprehensive reservoir interpretation and evaluation method based on dimension reduction fitting of logging sensitive parameters, which can solve the problems of low interpretation accuracy and unclear identification of complex reservoir fluid properties of existing single interpretation methods.

[0008] The technical solution adopted by this invention to solve its technical problem is:

[0009] A comprehensive reservoir interpretation and evaluation method based on dimensionality reduction fitting of logging sensitive parameters includes the following steps:

[0010] S1. Data Acquisition and Preprocessing: For the target block, gas logging data and rock pyrolysis logging data of each target well section are collected and used as raw data. The quality control of the collected raw data is carried out to remove abnormal data.

[0011] S2. Sensitive parameter selection and calculation: Based on the data preprocessed in step S1, a set of sensitive parameters for characterizing reservoir fluid properties is selected and calculated.

[0012] S3, Factor Analysis and Dimensionality Reduction:

[0013] Standardize each sensitive parameter in the sensitive parameter set;

[0014] Principal component analysis was used to perform factor analysis on the standardized sensitive parameters to extract the common factors of each sensitive parameter and obtain the original factor loading matrix.

[0015] The original factor loading matrix is ​​orthogonally rotated using the maximum variance orthogonal rotation method to obtain the rotated factor loading matrix, giving each common factor a clear physical interpretation:

[0016] S4. Construct a comprehensive interpretation model: Obtain the factor score coefficient matrix based on the rotation factor loading matrix, and based on the factor score coefficient matrix, construct a dimension-reduced first comprehensive index F1 for characterizing oil and gas properties and a second comprehensive index F2 for characterizing water-bearing properties for the reservoirs of each target well section.

[0017] S5. Comprehensive evaluation of fluid properties: Establish a two-dimensional cross plot with the first comprehensive index F1 as the abscissa and the second comprehensive index F2 as the ordinate. Project the data points of the first comprehensive index F1 and the second comprehensive index F2 of the known reservoirs in each target well section onto the two-dimensional cross plot. Based on the aggregation characteristics of the fluid property data of the known reservoirs in each target well section on the two-dimensional cross plot, delineate the distribution areas of different fluid types, determine the discrimination criteria, and interpret and evaluate the fluid properties of unknown reservoirs.

[0018] Furthermore,

[0019] In step S1: Gas logging data is acquired using a gas logging instrument and includes total hydrocarbon value (TG) and the relative contents of each hydrocarbon component (C1, C2, C3, iC4, nC4), where C1 is the relative content of methane, C2 is the relative content of ethane, C3 is the relative content of propane, iC4 is the relative content of isobutane branched structure in butane, and nC4 is the relative content of n-butane straight-chain structure in butane; rock pyrolysis logging data is acquired using a rock pyrolysis analyzer and includes gaseous hydrocarbon content (S0), liquid hydrocarbon content (S1), and cracked hydrocarbon content (S2).

[0020] In step S2: the sensitive parameters in the sensitive parameter set include total hydrocarbon value (TG), relative gas mass (Wg), relative oil mass (Wo), total hydrocarbon content (PG), total hydrocarbon yield index (TPI), rock pyrolysis peak ratio (S2 / S1), and derived parameter (PG×TPI). Specifically,

[0021] relative mass of gas Where C4 represents the relative content of butane, and ;

[0022] relative mass of oil , where d is a dimensionless constant, and the value of d ranges from 0.5 to 2.0, determined according to the geological characteristics and crude oil properties of the target block;

[0023] Total oil and gas content ;

[0024] Oil and gas total production index ;

[0025] Derived parameters .

[0026] Furthermore, in step S1, the process of acquiring rock pyrolysis logging data is as follows:

[0027] First, a quantitative amount of rock sample was placed in a heating furnace and heated at 90°C for 2 minutes. The gaseous hydrocarbon content S0 in the rock sample was then measured, with the unit being mg / g.

[0028] The rock sample was then heated at 200℃ for 3 minutes, and the liquid hydrocarbon content S1 in the rock sample was measured in mg / g.

[0029] The rock sample was then heated from 300℃ to 600℃ at a heating rate of 50℃ / min, and held at 600℃ for 1 minute. The content of cracked hydrocarbons S2 of heavy hydrocarbons, colloids and asphaltene in the rock sample was then measured, with the unit being mg / g.

[0030] Further, in step S3: before standardization, a factor analysis suitability test is performed on the sensitive parameter set. The factor analysis suitability test includes the KMO test and the Bartlett test of sphericity. When the KMO sampling suitability measure of the KMO test is greater than 0.5 and the significance probability of the Bartlett test of sphericity is less than 0.05, the sensitive parameter set is determined to be suitable for factor analysis.

[0031] Furthermore, in step S3, the sensitive parameters in the sensitive parameter set are standardized. Specifically, for each target well section, the sensitive parameters in the sensitive parameter set are standardized using the Z-Score standardization method, and the calculation formula is as follows: ,in Let Xi be the standardized value of the i-th sensitive parameter for a certain target well section, and let Xi be the original value of the i-th sensitive parameter for a certain target well section. This represents the average of the corresponding sensitive parameters for all target well sections. The standard deviation of the corresponding sensitive parameters for all target well sections.

[0032] Further, in step S3, principal component analysis is used to perform factor analysis on each of the standardized sensitive parameters to extract the common factors of each sensitive parameter. The top few common factors with a cumulative variance contribution rate greater than 85% are selected as the original common factors, and the original factor loading matrix of each original common factor is obtained. The variance maximum orthogonal rotation method is used to orthogonally rotate the original factor loading matrix of each original common factor to obtain the rotated factor loading matrix of each rotated common factor, so that each original common factor has a clear physical interpretation.

[0033] Further, in step S3, there are three original common factors, which are respectively called the first original common factor, the second original common factor, and the third original common factor. By analyzing the rotation factor loading matrix of each rotation common factor, it is found that the first rotation common factor mainly distributes the load on the total oil and gas content PG, the total oil and gas yield index TPI, and the derived parameter PG×TPI. It is also found that the second rotation common factor mainly distributes the load on the rock pyrolysis peak ratio S2 / S1, the total hydrocarbon value TG, and the relative gas mass Wg. Therefore, the first rotation common factor is defined as the oil and gas abundance factor, and the second rotation common factor is defined as the fluid property factor.

[0034] Further, step S4 specifically involves: obtaining the factor score coefficient matrix of the first original common factor based on the rotation factor loading matrix of the first rotation common factor, and obtaining the factor score coefficient matrix of the second original common factor based on the rotation factor loading matrix of the second rotation common factor. Based on the factor score coefficient matrix of the first original common factor, a dimension-reduced first comprehensive index F1 for characterizing hydrocarbon properties is constructed for each target well section's reservoir. Based on the factor score coefficient matrix of the second original common factor, a dimension-reduced second comprehensive index F2 for characterizing water-bearing properties is constructed for each target well section's reservoir. The first comprehensive index F1 is mainly contributed by hydrocarbon abundance factors, and the second comprehensive index F2 is mainly contributed by fluid property factors. Specifically,

[0035] , ,

[0036] Where n is the number of sensitive parameters in the sensitive parameter set. This is the standardized value of the i-th sensitive parameter for a specific target well section. The factor score coefficient of the first comprehensive index for the i-th sensitive parameter is... is the factor score coefficient of the second comprehensive index for the i-th sensitive parameter.

[0037] Furthermore, in step S4: The values ​​are extracted from the factor score coefficient matrix of the first original common factor. The value is extracted from the factor score coefficient matrix of the second original common factor.

[0038] Furthermore, in step S5: the fluid type includes oil and gas reservoirs, gas-bearing water reservoirs or water reservoirs, dry reservoirs or differentially ventilated reservoirs.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] The reservoir comprehensive interpretation and evaluation method based on dimensionality reduction fitting of logging sensitive parameters in this invention includes the following steps: S1, Data acquisition and preprocessing: For the target block, gas logging data and rock pyrolysis logging data of each target well section are collected as raw data. The quality control of the collected raw data is performed to remove abnormal engineering data; S2, Sensitive parameter selection and calculation: Based on the data preprocessed in step S1, a set of sensitive parameters for characterizing reservoir fluid properties is selected and calculated; S3, Factor analysis and dimensionality reduction: Each sensitive parameter in the sensitive parameter set is standardized; Principal component analysis is used to perform factor analysis on each standardized sensitive parameter to extract the common factors of each sensitive parameter and obtain the original factor loading matrix; The variance maximum orthogonal rotation method is used to adjust the original factor loading matrix. The matrix is ​​orthogonally rotated to obtain the rotation factor loading matrix, giving each common factor a clear physical interpretation: S4. Constructing a comprehensive interpretation model: Based on the rotation factor loading matrix, the factor score coefficient matrix is ​​obtained. Based on this matrix, for each target well section's reservoir, a dimension-reduced first comprehensive index F1 for characterizing oil and gas properties and a second comprehensive index F2 for characterizing water-bearing properties are constructed; S5. Comprehensive evaluation of fluid properties: A two-dimensional cross plot is established with the first comprehensive index F1 of each target well section's reservoir as the abscissa and the second comprehensive index F2 as the ordinate. Based on the aggregation characteristics of known fluid property data of each target well section's reservoir on the two-dimensional cross plot, the distribution areas of different fluid types are delineated, and discrimination criteria are determined to interpret and evaluate the fluid properties of unknown reservoirs. In this way, the present invention can comprehensively utilize gas logging data and rock pyrolysis logging data, and through factor analysis technology, reduce the dimensionality and fit the selected logging sensitive parameters to construct a comprehensive interpretation model. This solves the problems of low interpretation accuracy and unclear identification of fluid properties in complex reservoirs caused by existing single interpretation methods, thereby improving the accuracy of identifying oil, gas, and water layers in reservoirs. Attached Figure Description

[0041] Figure 1 This is a flowchart of the reservoir comprehensive interpretation and evaluation method based on dimension reduction fitting of logging sensitive parameters according to the present invention;

[0042] Figure 2 The F1-F2 comprehensive interpretation and evaluation two-dimensional intersection diagram is shown in the specific embodiment. Detailed Implementation

[0043] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. These embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0044] like Figure 1 As shown, a comprehensive reservoir interpretation and evaluation method based on dimensionality reduction fitting of logging sensitive parameters includes the following steps:

[0045] S1. Data Acquisition and Preprocessing: For the target block, gas logging data and rock pyrolysis logging data of each target well section are collected and used as raw data. The quality control of the collected raw data is carried out to remove abnormal data.

[0046] S2. Sensitive parameter selection and calculation: Based on the data preprocessed in step S1, a set of sensitive parameters for characterizing reservoir fluid properties is selected and calculated.

[0047] S3, Factor Analysis and Dimensionality Reduction:

[0048] The suitability of factor analysis is tested on the sensitive parameter set to determine that there is a significant correlation among the sensitive parameters in the set, making it suitable for factor analysis.

[0049] Standardize each sensitive parameter in the sensitive parameter set;

[0050] Principal component analysis was used to perform factor analysis on the standardized sensitive parameters to extract the common factors of each sensitive parameter and obtain the original factor loading matrix.

[0051] The original factor loading matrix is ​​orthogonally rotated using the maximum variance orthogonal rotation method to obtain the rotated factor loading matrix, giving each common factor a clear physical interpretation:

[0052] S4. Construct a comprehensive interpretation model: Obtain the factor score coefficient matrix based on the rotation factor loading matrix, and based on the factor score coefficient matrix, construct a dimension-reduced first comprehensive index F1 for characterizing oil and gas properties and a second comprehensive index F2 for characterizing water-bearing properties for the reservoirs of each target well section.

[0053] S5. Comprehensive evaluation of fluid properties: Establish a two-dimensional cross plot with the first comprehensive index F1 as the abscissa and the second comprehensive index F2 as the ordinate. Project the data points of the first comprehensive index F1 and the second comprehensive index F2 of the known reservoirs in each target well section onto the two-dimensional cross plot. Based on the aggregation characteristics of the fluid property data of the known reservoirs in each target well section on the two-dimensional cross plot, delineate the distribution areas of different fluid types, determine the discrimination criteria, and interpret and evaluate the fluid properties of unknown reservoirs.

[0054] in,

[0055] In step S1, gas logging data is acquired using a gas logging instrument and includes the total hydrocarbon value (TG) and the relative contents of each hydrocarbon component (C1, C2, C3, iC4, nC4), where C1 is the relative content of methane, C2 is the relative content of ethane, C3 is the relative content of propane, iC4 is the relative content of isobutane branched structure in butane, and nC4 is the relative content of n-butane straight-chain structure in butane. Rock pyrolysis logging data is acquired using a rock pyrolysis analyzer and includes the gaseous hydrocarbon content (S0), liquid hydrocarbon content (S1), and cracked hydrocarbon content (S2).

[0056] In step S2, the sensitive parameters in the sensitive parameter set include total hydrocarbon value (TG), relative gas mass (Wg), relative oil mass (Wo), total hydrocarbon content (PG), total hydrocarbon yield index (TPI), rock pyrolysis peak ratio (S2 / S1), and derived parameter (PG×TPI). Specifically,

[0057] relative mass of gas Where C4 represents the relative content of butane, and ;

[0058] relative mass of oil , where d is a dimensionless constant, and the value of d ranges from 0.5 to 2.0, determined according to the geological characteristics and crude oil properties of the target block;

[0059] Total oil and gas content ;

[0060] Oil and gas total production index ;

[0061] Derived parameters .

[0062] In step S1, the process of acquiring rock pyrolysis logging data is as follows:

[0063] First, a quantitative amount of rock sample is placed in a heating furnace and heated at 90°C for 2 minutes. The gaseous hydrocarbon content S0 in the rock sample is measured in mg / g, where the rock sample is typically 0.1g.

[0064] The rock sample was then heated at 200℃ for 3 minutes, and the liquid hydrocarbon content S1 in the rock sample was measured in mg / g. The distillation fraction of the liquid hydrocarbon is gasoline. The liquid hydrocarbon exhibits strong fluidity and can reflect the intensity of water washing. Its value gradually decreases with the increase of water content. Water washing refers to the dissolution, dilution and displacement of crude oil components during the long-term contact between formation water and crude oil. This leads to a decrease in light hydrocarbons and a relative enrichment of heavy hydrocarbons. Liquid hydrocarbons represent the components in crude oil that are most easily affected by water washing, so its content can reflect the intensity of water washing.

[0065] The rock sample was then heated from 300℃ to 600℃ at a heating rate of 50℃ / min and held at 600℃ for 1 minute. The content of cracked hydrocarbons S2 in the rock sample, including heavy hydrocarbons, colloids and asphaltene, was measured in mg / g. The cracked hydrocarbons were distilled into kerosene and diesel oil, and the values ​​of cracked hydrocarbons were relatively stable.

[0066] In step S3, the suitability test for factor analysis includes the KMO test and the Bartlett test of sphericity. When the KMO sampling suitability measure of the KMO test is greater than 0.5 and the significance probability of the Bartlett test of sphericity is less than 0.05, the sensitive parameter set is deemed suitable for factor analysis.

[0067] In step S3, the sensitive parameters in the sensitive parameter set are standardized. Specifically, for each target well section, the sensitive parameters in the sensitive parameter set are standardized using the Z-Score standardization method, and the calculation formula is as follows: ,in Let Xi be the standardized value of the i-th sensitive parameter for a certain target well section, and let Xi be the original value of the i-th sensitive parameter for a certain target well section. This represents the average of the corresponding sensitive parameters for all target well sections. The standard deviation of the corresponding sensitive parameters for all target well sections.

[0068] In step S3, principal component analysis is used to perform factor analysis on each sensitive parameter after standardization, extract the common factors of each sensitive parameter, select the top few common factors with a cumulative variance contribution rate greater than 85% as the original common factors, and obtain the original factor loading matrix of each original common factor; the variance maximum orthogonal rotation method is used to orthogonally rotate the original factor loading matrix of each original common factor to obtain the rotated factor loading matrix of each rotated common factor, so that each original common factor has a clear physical interpretation.

[0069] In step S3, there are three original common factors, which are respectively called the first original common factor, the second original common factor, and the third original common factor. By analyzing the rotation factor loading matrix of each rotation common factor, it is found that the first rotation common factor mainly distributes the load on the total oil and gas content PG, the total oil and gas yield index TPI, and the derived parameter PG×TPI. It is also found that the second rotation common factor mainly distributes the load on the rock pyrolysis peak ratio S2 / S1, the total hydrocarbon value TG, and the relative gas mass Wg. Therefore, the first rotation common factor is defined as the oil and gas abundance factor, and the second rotation common factor is defined as the fluid property factor.

[0070] Specifically, step S4 involves: obtaining the factor score coefficient matrix of the first original common factor based on the rotation factor loading matrix of the first rotation common factor, and obtaining the factor score coefficient matrix of the second original common factor based on the rotation factor loading matrix of the second rotation common factor. Based on the factor score coefficient matrix of the first original common factor, a dimension-reduced first comprehensive index F1 for characterizing hydrocarbon properties is constructed for each target well section's reservoir. Based on the factor score coefficient matrix of the second original common factor, a dimension-reduced second comprehensive index F2 for characterizing water-bearing properties is constructed for each target well section's reservoir. The first comprehensive index F1 is mainly contributed by hydrocarbon abundance factors, and the second comprehensive index F2 is mainly contributed by fluid property factors. Specifically...

[0071] , ,

[0072] Where n is the number of sensitive parameters in the sensitive parameter set. This is the standardized value of the i-th sensitive parameter for a specific target well section. The factor score coefficient of the first comprehensive index for the i-th sensitive parameter is... is the factor score coefficient of the second comprehensive index for the i-th sensitive parameter.

[0073] In step S4: The values ​​are extracted from the factor score coefficient matrix of the first original common factor. The value is extracted from the factor score coefficient matrix of the second original common factor.

[0074] In step S5, the fluid type includes oil and gas reservoirs, gas-bearing water reservoirs or water reservoirs, dry reservoirs or poor-gas reservoirs.

[0075] The gas logging tool is used to detect the relative content of various hydrocarbon components in drilling fluid. It consists of a degasser, a gas delivery line, a total hydrocarbon detector, a chromatographic analysis unit, and a data acquisition system. The degasser, using vacuum degassing, is installed at the drilling fluid outlet tank to separate hydrocarbon components from the drilling fluid, achieving a degassing efficiency of over 95%. The gas delivery line is typically 30-50 meters long, with a delivery time of 2-3 minutes, and is made of a material that does not adsorb hydrocarbon components. The total hydrocarbon detector in the gas logging tool uses a flame ionization detector (FID), with hydrogen as the fuel gas and air as the oxidant, to measure the total hydrocarbon value (TG), with a detection limit of up to 1 ppm. The chromatographic analysis unit uses a capillary column with helium or nitrogen as the carrier gas, and can complete the separation and quantitative analysis of various hydrocarbon components within 5-10 minutes. The data acquisition system in the gas logging tool collects, processes, and stores gas logging data in real time, automatically calculating the total hydrocarbon value (TG) and the relative contents of each hydrocarbon component (C1, C2, C3, iC4, nC4).

[0076] The rock pyrolysis analyzer is used to detect the hydrocarbon content and pyrolysis characteristics of rock samples from reservoirs. It consists of a sample introduction system, a pyrolysis furnace, a full hydrocarbon detector (FID), and a data processing system. The sample introduction system uses an autosampler, with a sample volume typically of 50-100 mg. The sample needs to be pulverized to a 60-100 mesh sieve. The pyrolysis furnace is a programmable temperature-controlled electric furnace with a temperature control accuracy of ±1℃ and a maximum temperature exceeding 600℃. The FID in the rock pyrolysis analyzer detects the hydrocarbon gases released during pyrolysis and records the pyrolysis curve. The data processing system automatically calculates the gaseous hydrocarbon content (S0), liquid hydrocarbon content (S1), cracked hydrocarbon content (S2), total oil and gas content (PG), total oil and gas yield index (TPI), and derived parameters PG×TPI. Specific Implementation

[0077] This embodiment selects logging data from 10 exploration wells with a total of 20 target well sections in a certain sea area depression as samples to establish a comprehensive interpretation model. The specific parameter values ​​given below, such as factor score coefficients and discrimination criteria, are based on the data of this specific block. When applying to other blocks, it is necessary to recalculate them based on local sample data. The detailed implementation steps are as follows:

[0078] Step S1, Data Acquisition and Preprocessing:

[0079] (1) Gas logging data acquisition: During the drilling process, gas logging data corresponding to the reservoir of each target well section is acquired, including the total hydrocarbon value TG and the relative contents of each hydrocarbon component C1, C2, C3, iC4, nC4, which are used as the raw data of gas logging data.

[0080] (2) Rock pyrolysis logging data acquisition: During the drilling process, rock samples are taken from the reservoir of each target well section, and after cleaning and drying, pyrolysis analysis is performed to collect the rock pyrolysis logging data corresponding to the reservoir of each target well section, including gaseous hydrocarbon content S0, liquid hydrocarbon content S1 and cracked hydrocarbon content S2, which are used as the raw data of rock pyrolysis logging data.

[0081] (3) Data quality control: The raw data collected is subject to quality control, and the following four types of abnormal engineering data are removed: 1) Abnormalities caused by the effects after tripping in and out of the well, 2) Abnormalities caused by connecting a single joint, 3) Delayed gas display caused by circulating drilling fluid, and 4) Changes in degassing efficiency caused by changes in drilling fluid properties.

[0082] Step S2, Sensitive Parameter Optimization and Calculation:

[0083] Based on the data preprocessed in step S1, a set of sensitive parameters for characterizing reservoir fluid properties is screened and calculated. Specifically, seven parameters that are most sensitive to reservoir fluid properties are selected:

[0084] (1) Total hydrocarbon value (TG): Directly output by the gas logging instrument, reflecting the total intensity of oil and gas indication.

[0085] (2) Relative mass of gas Wg: The calculation formula is as follows Where C4 represents the relative content of butane, and This sensitive parameter reflects the average molecular weight of the mixed hydrocarbon gas, where 16 is the molecular weight of methane, 30 is the molecular weight of ethane, 44 is the molecular weight of propane, and 58 is the molecular weight of butane.

[0086] (3) Relative mass of oil Wo: The calculation formula is as follows Where d is a dimensionless constant, and in this embodiment, d = 1.0. The physical meaning of this sensitive parameter is: C4² reflects the enrichment degree of heavy hydrocarbon components. Reflecting the content of intermediate hydrocarbon components, C4² and The ratio between them characterizes the relative quality features of crude oil. It is calculated based on the relative content of components, eliminating interference from engineering parameters.

[0087] (4) Total oil and gas content PG: The calculation formula is as follows The unit is mg / g. Since the value of PG can directly reflect the change in hydrocarbon content in the reservoir, the larger the PG value, the better the oil and gas abundance of the reservoir. As the water content of the reservoir increases, the PG value tends to decrease.

[0088] (5) Total Oil and Gas Production Index (TPI): The calculation formula is as follows The values ​​range from 0 to 1. When the water or oil content in the reservoir changes, the content of liquid hydrocarbons shows a more obvious pattern, while the value of cracked hydrocarbons is relatively stable. Therefore, the properties of fluids can be determined by using the values ​​of PG and TPI.

[0089] (6) Rock pyrolysis peak ratio S2 / S1: S1 represents the liquid hydrocarbon content, in which liquid hydrocarbons have strong fluidity, and S2 represents the cracked hydrocarbon content. Studies have found that this ratio can be used as a sensitive parameter to distinguish between oil layers and water layers. Specifically, the water washing effect of oil layers is weak, and the value of S1 is larger, while the water washing effect of water layers is strong, and the value of S1 is smaller, while the value of S2 is basically the same in oil layers and water layers.

[0090] (7) Derived parameter PG×TPI: This sensitive parameter combines the characteristics of oil and gas abundance and total oil and gas production rate. The calculation result is as follows: It reflects the content of mobile hydrocarbons and can effectively distinguish between oil and gas reservoirs and ineffective reservoirs.

[0091] Step S3, Factor Analysis and Dimensionality Reduction:

[0092] (1) Applicability test: KMO test and Bartlett's test of sphericity were performed on the above 7 sensitive parameters. In this embodiment, the value of the KMO sampling appropriateness measure was 0.692 (greater than 0.5), the value of the approximate chi-square in the Bartlett's test of sphericity was 193.972, and the significance probability of the Bartlett's test of sphericity was 0 (less than 0.05). See Table 1 for details. This shows that there is a significant correlation between the sensitive parameters and they are suitable for factor analysis. The correlation coefficient matrix of the sensitive parameters is detailed in Table 2.

[0093] Table 1. Results of KMO test and Bartlett's test of sphericity

[0094]

[0095] Table 2. Correlation coefficients and significance matrices of sensitive parameters

[0096]

[0097] In Table 2, the correlation coefficient matrix of the sensitive parameters shows that the correlation coefficients among multiple sensitive parameters are relatively large, and their corresponding significance (one-tailed) values ​​are generally small, indicating that there is a significant correlation among these sensitive parameters, thus demonstrating the necessity of factor analysis.

[0098] (2) Standardization processing: For each target well section, the Z-Score standardization method is used to standardize each sensitive parameter in the sensitive parameter set. The calculation formula is as follows: ,in Let Xi be the standardized value of the i-th sensitive parameter for a certain target well section, and let Xi be the original value of the i-th sensitive parameter for a certain target well section. This represents the average of the corresponding sensitive parameters for all target well sections. The standard deviation of the corresponding sensitive parameters for all target well sections is used. Standardization processing ensures that each sensitive parameter has the same dimension, which facilitates factor analysis.

[0099] (3) Extraction of common factors: Principal component analysis was used to perform factor analysis on each sensitive parameter after standardization to extract the common factors of each sensitive parameter. The analysis results in this embodiment show that the cumulative variance contribution rate of the first three common factors reached 87.995% (>85%), as detailed in Table 3. This indicates that these three common factors can explain most of the original information. Therefore, the first three common factors were selected as the original common factors and were respectively called the first original common factor, the second original common factor and the third original common factor. The extracted feature values ​​of the first three original common factors are detailed in Table 4, and the original factor loading matrix of each original common factor was obtained, as detailed in Table 5.

[0100] Table 3 Initial eigenvalues ​​of the 7 original common factors

[0101]

[0102] Table 4. Extracted feature values ​​of the first three original common factors

[0103]

[0104] Table 4 shows the variance contribution information of the three extracted original common factors. The content of Table 4 is exactly the same as that of the first three common factors in Table 3, indicating that the first three original common factors can explain 87.995% of the total variance. That is, more than 85% of the information of the logging sensitive parameters can be explained by these three atomic common factors.

[0105] Table 5. Original factor loading matrix of each original common factor

[0106]

[0107] (4) Factor Rotation: The original factor loading matrices of each original common factor are orthogonally rotated using the Varimax rotation method. After rotation, each original common factor is transformed into a rotated common factor, and each original factor loading matrix is ​​transformed into a rotated factor loading matrix. Thus, the rotated factor loading matrices of each rotated common factor are obtained, as detailed in Table 6. By analyzing the rotated factor loading matrix of the first rotated common factor, it is found that the first rotated common factor mainly distributes its loadings on the total oil and gas content (PG), the total oil and gas production index (TPI), and the derived parameter (PG×TPI). Therefore, the first rotated common factor is defined as the oil and gas abundance factor. By analyzing the rotated factor loading matrix of the second rotated common factor... The second rotating common factor is found to have its load mainly distributed on the rock pyrolysis peak ratio S2 / S1, total hydrocarbon value TG, and relative gas mass Wg. Therefore, the second rotating common factor is defined as a fluid property factor. By analyzing the rotating factor loading matrix of the third rotating common factor, it is found that the third rotating common factor has a high loading on the relative oil mass Wo. Its information is reflected in the calculation of the first comprehensive index for the corresponding factor score coefficient of the first comprehensive index for the relative oil mass Wo. Its information is also reflected in the calculation of the second comprehensive index for the corresponding factor score coefficient of the second comprehensive index for the relative oil mass Wo. The rotating characteristic values ​​of the first three rotating common factors are detailed in Table 7.

[0108] Table 6. Rotation factor loading matrix for each rotation common factor

[0109]

[0110] Table 7. Rotation eigenvalues ​​of the first three rotation common factors

[0111]

[0112] Table 7 shows the variance contribution information of the first three rotated common factors after rotation. Compared with the unrotated ones in Table 4, the variance contribution value of each common factor has changed, but the final cumulative variance contribution rate remains unchanged.

[0113] Step S4: Construct a comprehensive explanation model:

[0114] The factor score coefficient matrix of the first original common factor is obtained based on the rotation factor loading matrix of the first rotation common factor, and the factor score coefficient matrix of the second original common factor is obtained based on the rotation factor loading matrix of the second rotation common factor. Based on the factor score coefficient matrix of the first original common factor, a dimension-reduced first comprehensive index F1 for characterizing hydrocarbon properties is constructed using a regression algorithm for each target well interval's reservoir. Based on the factor score coefficient matrix of the second original common factor, a dimension-reduced second comprehensive index F2 for characterizing water-bearing properties is constructed using a regression algorithm for each target well interval's reservoir. The first comprehensive index F1 is mainly contributed by hydrocarbon abundance factors, and the second comprehensive index F2 is mainly contributed by fluid property factors. Specifically...

[0115] , ,

[0116] Where n is the number of sensitive parameters in the sensitive parameter set. This is the standardized value of the i-th sensitive parameter for a specific target well section. The factor score coefficient of the first comprehensive index for the i-th sensitive parameter is... The factor score coefficient of the second composite index for the i-th sensitive parameter is given by , where The values ​​are extracted from the factor score coefficient matrix of the first original common factor. The value is extracted from the factor score coefficient matrix of the second original common factor.

[0117] In this embodiment, factor analysis was performed using SPSS software, and the factor score coefficient matrices of the first and second original common factors are shown in Table 8.

[0118] Table 8 Factor Score Coefficient Matrix

[0119]

[0120] Based on Table 1, the calculation formulas for F1 and F2 in this embodiment are as follows:

[0121] ,

[0122] ,

[0123] In this embodiment, all the above-mentioned sensitive parameters are Z-Score standardized values.

[0124] It should be emphasized that the above factor score coefficient values ​​are calculated for a specific block sample in this embodiment. When applying the method of this invention to other blocks, factor analysis should be performed again based on the known oil test data of the block to obtain the factor score coefficients applicable to the block.

[0125] Step S5, Comprehensive Evaluation of Fluid Properties:

[0126] A two-dimensional cross plot is established with the first comprehensive index F1 as the horizontal axis and the second comprehensive index F2 as the vertical axis. The data points of the first comprehensive index F1 and the second comprehensive index F2 of the known reservoirs in each target well section are projected onto the two-dimensional cross plot. Based on the aggregation characteristics of the fluid property data of the known reservoirs in each target well section on the two-dimensional cross plot, the distribution areas of different fluid types are delineated, and the discrimination criteria are determined to interpret and evaluate the fluid properties of unknown reservoirs.

[0127] In this embodiment, based on the distribution characteristics of the data points of the first comprehensive index F1 and the second comprehensive index F2 of 20 known target well interval reservoirs, see details below. Figure 2 The established criteria for judgment are as follows:

[0128] Oil and gas reservoir regions: F1≥10 and F2≤11;

[0129] Regions with dry or poorly ventilated layers: F1 < 10 and -25 <F2<100;

[0130] Regions containing gas-bearing or water layers: F1≤5 and F2≥100.

[0131] It should be noted that the boundary values ​​of the above discrimination criteria are also determined for a specific block in this embodiment. When applied to other blocks, the discrimination criteria applicable to the block should be redefined based on the distribution of known fluid property sample points on the two-dimensional intersection chart.

[0132] The implementation results are as follows:

[0133] The model was applied to 20 target well sections in 10 exploratory wells in the target block for verification. Compared with the oil testing results, the logging interpretation accuracy increased from 75% for a single technology to 90%, as detailed below:

[0134] (1) Oil and gas reservoirs: 8 target well sections, all of which were correctly identified, with a 100% accuracy rate;

[0135] (2) Gas-bearing or water-bearing layers: 6 out of 7 well sections were correctly identified, with a compliance rate of 85.7%;

[0136] (3) Dry or poor gas layer: 4 out of 5 well sections were correctly identified, with a compliance rate of 80%;

[0137] The overall compliance rate was 90%, which is 15% higher than that of traditional single-item techniques, proving the effectiveness and advancement of the method.

[0138] The method of the present invention has the following advantages:

[0139] (1) Multi-source information fusion: It combines the advantages of gas logging and rock pyrolysis logging, and reflects the true situation of the reservoir more comprehensively than a single method;

[0140] (2) Mathematical dimensionality reduction and noise reduction: Factor analysis is used to condense multiple sensitive parameters with correlation into a few uncorrelated common factors, which effectively removes data redundancy;

[0141] (3) Improve interpretation accuracy: Through practical application verification, the integrated model can significantly improve the accuracy of reservoir fluid interpretation.

[0142] (4) Strong universality: The core methodology of this invention is applicable to different regions and different types of oil and gas reservoirs. Factor analysis can be performed again based on sample data of a specific block to obtain model parameters applicable to that block.

[0143] The method of the present invention has the following universality:

[0144] (1) Universality of parameter combination: The seven sensitive parameters (TG, Wg, Wo, PG, TPI, S2 / S1, PG×TPI) preferred in this invention cover the key information of two types of logging data, gas logging and rock pyrolysis, and are applicable to various conventional and unconventional oil and gas reservoirs;

[0145] (2) Universality of dimensionality reduction methods: Factor analysis is a mature multivariate statistical method that does not depend on specific geological conditions and can be applied to any well logging data analysis scenario with multi-parameter correlation;

[0146] (3) Customizability of model parameters: Although this embodiment provides factor score coefficients and discrimination criteria for specific blocks, the method of this invention allows and encourages users to recalculate these parameters based on the actual sample data of the target block in order to obtain the best interpretation effect;

[0147] (4) Scalability of the evaluation system: The F1-F2 two-dimensional intersection chart established by this invention can be further subdivided into fluid types according to actual needs, such as distinguishing between oil layers, gas layers, oil-water co-layers, and gas-water co-layers.

[0148] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. A comprehensive reservoir interpretation and evaluation method based on dimensionality reduction fitting of logging sensitive parameters, characterized in that: Includes the following steps: S1. Data Acquisition and Preprocessing: For the target block, gas logging data and rock pyrolysis logging data of each target well section are collected and used as raw data. The quality control of the collected raw data is carried out to remove abnormal data. S2. Sensitive parameter selection and calculation: Based on the data preprocessed in step S1, a set of sensitive parameters for characterizing reservoir fluid properties is selected and calculated. S3, Factor Analysis and Dimensionality Reduction: Standardize each sensitive parameter in the sensitive parameter set; Principal component analysis was used to perform factor analysis on the standardized sensitive parameters to extract the common factors of each sensitive parameter and obtain the original factor loading matrix. The original factor loading matrix is ​​orthogonally rotated using the maximum variance orthogonal rotation method to obtain the rotated factor loading matrix, giving each common factor a clear physical interpretation: S4. Construct a comprehensive interpretation model: Obtain the factor score coefficient matrix based on the rotation factor loading matrix, and based on the factor score coefficient matrix, construct a dimension-reduced first comprehensive index F1 for characterizing oil and gas properties and a second comprehensive index F2 for characterizing water-bearing properties for the reservoirs of each target well section. S5. Comprehensive evaluation of fluid properties: Establish a two-dimensional cross plot with the first comprehensive index F1 as the abscissa and the second comprehensive index F2 as the ordinate. Project the data points of the first comprehensive index F1 and the second comprehensive index F2 of the known reservoirs in each target well section onto the two-dimensional cross plot. Based on the aggregation characteristics of the fluid property data of the known reservoirs in each target well section on the two-dimensional cross plot, delineate the distribution areas of different fluid types, determine the discrimination criteria, and interpret and evaluate the fluid properties of unknown reservoirs.

2. The reservoir comprehensive interpretation and evaluation method based on dimensionality reduction fitting of logging sensitive parameters according to claim 1, characterized in that: In step S1: Gas logging data is acquired using a gas logging instrument and includes total hydrocarbon value (TG) and the relative contents of each hydrocarbon component (C1, C2, C3, iC4, nC4), where C1 is the relative content of methane, C2 is the relative content of ethane, C3 is the relative content of propane, iC4 is the relative content of isobutane branched structure in butane, and nC4 is the relative content of n-butane straight-chain structure in butane; rock pyrolysis logging data is acquired using a rock pyrolysis analyzer and includes gaseous hydrocarbon content (S0), liquid hydrocarbon content (S1), and cracked hydrocarbon content (S2). In step S2: the sensitive parameters in the sensitive parameter set include total hydrocarbon value (TG), relative gas mass (Wg), relative oil mass (Wo), total hydrocarbon content (PG), total hydrocarbon yield index (TPI), rock pyrolysis peak ratio (S2 / S1), and derived parameter (PG×TPI). Specifically, relative mass of gas Where C4 represents the relative content of butane, and ; relative mass of oil , where d is a dimensionless constant, and the value of d ranges from 0.5 to 2.0, determined according to the geological characteristics and crude oil properties of the target block; Total oil and gas content ; Oil and gas total production index ; Derived parameters .

3. The comprehensive reservoir interpretation and evaluation method based on dimensionality reduction fitting of logging sensitive parameters according to claim 2, characterized in that, In step S1, the process of acquiring rock pyrolysis logging data is as follows: First, a quantitative amount of rock sample was placed in a heating furnace and heated at 90°C for 2 minutes. The gaseous hydrocarbon content S0 in the rock sample was then measured, with the unit being mg / g. The rock sample was then heated at 200℃ for 3 minutes, and the liquid hydrocarbon content S1 in the rock sample was measured in mg / g. The rock sample was then heated from 300℃ to 600℃ at a heating rate of 50℃ / min, and held at 600℃ for 1 minute. The content of cracked hydrocarbons S2 of heavy hydrocarbons, colloids and asphaltene in the rock sample was then measured, with the unit being mg / g.

4. The comprehensive reservoir interpretation and evaluation method based on dimensionality reduction fitting of logging sensitive parameters according to claim 2, characterized in that, In step S3: Before standardization, the sensitivity parameter set is subjected to a factor analysis suitability test, which includes the KMO test and the Bartlett test of sphericity. When the KMO sample fit measure of the KMO test is greater than 0.5 and the significance probability of the Bartlett test of sphericity is less than 0.05, the sensitivity parameter set is deemed suitable for factor analysis.

5. The reservoir comprehensive interpretation and evaluation method based on dimensionality reduction fitting of logging sensitive parameters according to claim 2, characterized in that, In step S3, the sensitive parameters in the sensitive parameter set are standardized. Specifically, for each target well section, the sensitive parameters in the sensitive parameter set are standardized using the Z-Score standardization method, and the calculation formula is as follows: ,in Let Xi be the standardized value of the i-th sensitive parameter for a certain target well section, and let Xi be the original value of the i-th sensitive parameter for a certain target well section. This represents the average of the corresponding sensitive parameters for all target well sections. The standard deviation of the corresponding sensitive parameters for all target well sections.

6. The comprehensive reservoir interpretation and evaluation method based on dimensionality reduction fitting of logging sensitive parameters according to claim 5, characterized in that, In step S3, principal component analysis is used to perform factor analysis on each sensitive parameter after standardization, extract the common factors of each sensitive parameter, select the top few common factors with a cumulative variance contribution rate greater than 85% as the original common factors, and obtain the original factor loading matrix of each original common factor. The original factor loading matrices of each original common factor are orthogonally rotated using the maximum variance orthogonal rotation method to obtain the rotated factor loading matrices of each rotated common factor, thus giving each original common factor a clear physical interpretation.

7. The comprehensive reservoir interpretation and evaluation method based on dimensionality reduction fitting of logging sensitive parameters according to claim 6, characterized in that, In step S3, there are three original common factors, which are respectively called the first original common factor, the second original common factor, and the third original common factor. By analyzing the rotation factor loading matrix of each rotation common factor, it is found that the first rotation common factor mainly distributes the load on the total oil and gas content PG, the total oil and gas yield index TPI, and the derived parameter PG×TPI. It is also found that the second rotation common factor mainly distributes the load on the rock pyrolysis peak ratio S2 / S1, the total hydrocarbon value TG, and the relative gas mass Wg. Therefore, the first rotation common factor is defined as the oil and gas abundance factor, and the second rotation common factor is defined as the fluid property factor.

8. The comprehensive reservoir interpretation and evaluation method based on dimensionality reduction fitting of logging sensitive parameters according to claim 7, characterized in that, Step S4 specifically involves: obtaining the factor score coefficient matrix of the first original common factor based on the rotation factor loading matrix of the first rotation common factor, and obtaining the factor score coefficient matrix of the second original common factor based on the rotation factor loading matrix of the second rotation common factor. Based on the factor score coefficient matrix of the first original common factor, a dimension-reduced first comprehensive index F1 for characterizing hydrocarbon properties is constructed for each target well section's reservoir. Based on the factor score coefficient matrix of the second original common factor, a dimension-reduced second comprehensive index F2 for characterizing water-bearing properties is constructed for each target well section's reservoir. The first comprehensive index F1 is mainly contributed by hydrocarbon abundance factors, and the second comprehensive index F2 is mainly contributed by fluid property factors. Specifically... ， ， Where n is the number of sensitive parameters in the sensitive parameter set. This is the standardized value of the i-th sensitive parameter for a specific target well section. The factor score coefficient of the first comprehensive index for the i-th sensitive parameter is... is the factor score coefficient of the second comprehensive index for the i-th sensitive parameter.

9. The comprehensive reservoir interpretation and evaluation method based on dimensionality reduction fitting of logging sensitive parameters according to claim 8, characterized in that, In step S4: The values ​​are extracted from the factor score coefficient matrix of the first original common factor. The value is extracted from the factor score coefficient matrix of the second original common factor.

10. The comprehensive reservoir interpretation and evaluation method based on dimensionality reduction fitting of logging sensitive parameters according to claim 2, characterized in that, In step S5: the fluid type includes oil and gas reservoirs, gas-bearing water reservoirs or water reservoirs, dry reservoirs or poor-gas reservoirs.

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