A method and process for lithological classification of shale oil reservoirs based on RGB fusion of well logging information

Abstract

This invention relates to a method and process for shale oil reservoir lithofacies classification based on RGB fusion of well logging information. The method uses the first derivative of the reflection coefficient curve to divide rock units. For each rock unit, lithological indices are calculated using the root mean square of natural gamma logging, sedimentary structural indices are calculated using the roughness of resistivity logging curves, and oil-bearing indices are calculated using the root mean square of resistivity curves. The RGB color model is used to perform parameter fusion and color representation of the lithological, sedimentary structural, and oil-bearing indices of the shale reservoir. Different types of lithofacies color templates established using core sections are used to identify the lithofacies types of shale oil and gas reservoirs throughout the well section. This method achieves three-parameter fusion identification of shale reservoir lithofacies types, which is more intuitive and convenient.

Description

Technical Field

[0001] This invention is a method and process for lithological classification of shale oil reservoirs based on RGB fusion of well logging information, belonging to the field of oil and gas exploration and development technology. Background Technology

[0002] Shale oil and gas is a global hotspot and emerging field in oil and gas exploration and development, and also one of the main battlegrounds for oil and gas exploration and development in my country. Shale oil and gas reservoir evaluation is the geological foundation for shale oil and gas exploration and development, while lithofacies is a key geological factor controlling the quality of shale oil and gas reservoirs. Utilizing well logging data for lithofacies classification and identification of shale oil and gas reservoirs is an important task in shale reservoir research.

[0003] The commonly used methods can be divided into several categories:

[0004] (1) Core calibration logging method. This method determines the lithofacies type through core analysis, establishes the correspondence between lithofacies and logging responses (such as natural gamma, resistivity, sonic transit time, density, etc.), and classifies lithofacies using logging curve characteristics.

[0005] (2) Cross plot method. This method uses a variety of logging parameters (such as natural gamma and resistivity, sonic transit time and density, etc.) to draw cross plots and classify different lithofacies types according to the distribution area of ​​data points.

[0006] (3) Well logging facies analysis method. This method establishes well logging facies patterns based on the characteristics of well logging curve morphology, amplitude, and contact relationship, and identifies lithofacies by comparing them with core calibration results.

[0007] (4) Cluster analysis method. This method uses clustering algorithms (such as K-means, hierarchical clustering, etc.) to automatically classify well logging data, and combines core data to verify the clustering results and classify lithofacies.

[0008] (5) Machine learning method. This method uses machine learning algorithms such as support vector machine, random forest, and neural network to train a lithofacies identification model based on well logging data to achieve automatic lithofacies identification.

[0009] All of the above methods require core calibration for verification and have some common problems, mainly including:

[0010] (1) Insufficient core calibration: Most methods rely on core verification, but the bottleneck of shale coring leads to a scarcity of calibration data, which is the core factor restricting accuracy. At the same time, the mismatch between the core and logging scales is a difficult problem to solve.

[0011] (2) Polymorphism of logging response: The complexity of shale lithofacies (mineral mixing, organic matter differences) means that the same lithofacies may correspond to multiple logging responses, and vice versa;

[0012] (3) Poor regional mobility: Differences in geological conditions (marine and continental facies, basin types) make it difficult for the methods to be universally applicable;

[0013] (4) Imbalance in interpretability: Empirical methods (such as well logging facies analysis) are highly subjective, while machine learning methods have weak interpretability, making it difficult to balance the two. Moreover, machine learning methods require a large number of samples as support, which is a practical problem.

[0014] To address the challenges of the aforementioned methods, this invention proposes a shale oil reservoir lithofacies classification method and process based on RGB fusion of well logging information. Summary of the Invention

[0015] This invention provides a method and process for shale oil reservoir lithofacies classification based on RGB fusion of well logging information. The specific technical solution includes the following steps:

[0016] S1: Calculate the reflection coefficient sequence of the target layer using the sonic transit time logging curve and density logging curve, obtain the reflection coefficient curve by interpolation, and divide the rock unit using the first derivative of the reflection coefficient curve.

[0017] S2: For each rock unit obtained in step S1, calculate the root mean square of the natural gamma logging curve value of the rock unit as the lithological index value of the rock unit.

[0018] S3: For each rock unit obtained in step S1, calculate the roughness of the resistivity curve, which is used as the sedimentary structure index value of that rock unit.

[0019] S4: For each rock unit obtained in step S1, calculate the root mean square of the resistivity logging curve value of the rock unit as the oil-bearing index value of the rock unit.

[0020] S5: For each rock unit obtained in step S1, the lithological index value, sedimentary structure index value, and oil-bearing index value obtained in steps S2, S3, and S4 are used to form the lithofacies classification evaluation parameters of each rock unit. The RGB color model is used to express the lithofacies classification evaluation parameters of shale oil and gas reservoirs in color, and the color expression results of the whole well section are obtained.

[0021] S6: Use the core sections of the core wells to establish RGB color pattern maps of different types of lithofacies in the study area, which can be used as RGB color templates for lithofacies classification of shale oil and gas reservoirs in the study area.

[0022] S7: Compare the color representation of the target layer rock units obtained in step S5 with the lithofacies color template of the shale oil and gas reservoir in the study area established in step S6 to determine the lithofacies type of the target layer rock units and obtain the lithofacies type of all rock units in the target layer.

[0023] First, this invention uses the change in longitudinal wave impedance of rock strata as the basis for dividing the research units, which integrates rock physical properties and lithological changes, making the division more comprehensive. Second, it uses the roughness of resistivity logging curves to reflect the degree of toothing of high-resolution logging curves within the research unit, reflecting the changes in sedimentary structures, and realizing sedimentary structure analysis at a logging-identifiable scale based on conventional logging curves. Third, it uses an RGB color model to fuse and display the three logging parameters, realizing multi-parameter joint characterization of shale lithofacies.

[0024] Preferably, step S1 in the method includes the following steps:

[0025] (1) For the i-th logging curve sampling point of the target layer at the well point from top to bottom according to depth, the reflection coefficient of the point is calculated using its acoustic transit time (AC) logging curve value and density (DEN) logging curve value. The reflection coefficient sequence of the target layer segment is obtained by calculating the reflection coefficient of each sampling point on the logging curve of the target layer using this method. The reflection coefficient curve is obtained by interpolation according to the sampling interval of 0.125m, where i is a natural number greater than zero. The specific calculation method is as follows:

[0026] ;

[0027] ;

[0028] ;

[0029] Where v is the longitudinal wave velocity of the rock. Here, P-wave velocity is represented by the sampling point of the i-th logging curve from top to bottom in the target layer, and AC is the sonic transit time logging value. Let AI be the sonic transit time logging value of the i-th logging curve sampling point from top to bottom in the target layer according to depth, and let AI be the longitudinal wave impedance of the rock. Let R be the longitudinal wave impedance of the i-th logging curve sampling point in the target layer from top to bottom according to depth, and R be the reflection coefficient of the rock. The reflection coefficient of the i-th logging curve sampling point in the target layer from top to bottom according to depth. This represents the rock density logging value. The density logging value is the sampling point of the i-th logging curve from top to bottom in the target layer according to the depth.

[0030] (2) Calculate the first derivative of the reflection coefficient curve obtained in step (1), and divide the target layer into m rock units with the points where the first derivative is negative as the boundary. Number the rock units L1, L2, L3, L4 according to their depth from top to bottom. 3，…， L m Where m is a natural number;

[0031] Preferably, step S2 in the method includes the following steps:

[0032] (1) Select the j-th rock unit L obtained in step S1 j Where j is a natural number greater than zero but not greater than m, the root mean square of the natural gamma-ray logging (GR) curve samples within the depth range corresponding to the rock element is calculated, and this root mean square is taken as the value of the rock element L. j The lithological index value is denoted as Lith j ;

[0033] (2) Calculate the lithological index values ​​of all m rock units in the entire target section using the method in step (1).

[0034] Preferably, step S3 in the method includes the following steps:

[0035] (1) Select the j-th rock unit L obtained in step S1 j Where j is a natural number greater than zero but not greater than m, the roughness of the resistivity logging (Rt) curve of the rock element is calculated and used as the roughness of the rock element L. j The sedimentary tectonic index value, denoted as SedStr j The specific calculation formula is as follows:

[0036] ；

[0037] Where t is the j-th rock unit L j The corresponding number of sampling points for the resistivity curve, where t is a natural number greater than 1, and p is L. j The corresponding sampling point on the resistivity curve, where p is a natural number greater than zero and not greater than t, x p Rock unit L j The resistivity logging curve value of the p-th sampling point;

[0038] (2) Calculate the sedimentary structural index values ​​of all m rock units in the entire target section using the method in step (1).

[0039] Preferably, step S4 in the method includes the following steps:

[0040] (1) Select the j-th rock unit L obtained in step S1 j Where j is a natural number greater than zero but not greater than m, the root mean square of the resistivity logging curve samples within the depth range corresponding to the rock element is calculated, and this root mean square is taken as the value of the rock element L. j The oil content index value is denoted as OG. j ;

[0041] (2) Calculate the oil-bearing index values ​​of all m rock units in the entire target section using the method in step (1).

[0042] Preferably, step S5 in the method includes the following steps:

[0043] (1) For the rock unit obtained in step S1, the lithological index value, sedimentary structure index value, and oil-bearing index value obtained in steps S2, S3, and S4 are used to form the lithofacies classification evaluation parameters of the rock unit. For the i-th rock unit L i Its lithofacies classification and evaluation parameters are denoted as vectors (Lith i , SedStr i OG i );

[0044] (2) Using the RGB color model to analyze rock unit L i The lithofacies classification and evaluation parameter vector is color-represented, where the three parameters in the vector correspond to the R, G, and B parameter components in the color model, respectively, to obtain the rock unit L. i Color expression;

[0045] (3) Using the methods in steps (1) and (2), the lithofacies classification evaluation parameters of m rock units in the target layer are expressed by color scale, and the color expression results of the whole well section are obtained.

[0046] Preferably, step S6 in the method includes the following steps:

[0047] (1) The core section of the study area was divided into rock units using the method in step S1. A total of q rock units were divided, where q is a natural number greater than zero.

[0048] (2) Using the traditional core observation and analysis method in the industry, the lithofacies types of q rock units were identified and classified, and a total of r lithofacies types were identified, where r is a natural number greater than zero and not greater than q;

[0049] (3) Using the methods in steps S1 to S5, the lithofacies classification evaluation parameters of q rock units in the core section of the study area are expressed in color, and the lithofacies color expression of q rock units is obtained.

[0050] (4) The lithofacies color expression of q rock units is divided into r groups according to lithofacies type. Each lithofacies type of rock unit is used as a group to obtain the color expression of r lithofacies type, which serves as the lithofacies color template for shale oil and gas reservoirs in the study area.

[0051] Compared with traditional methods, this invention considers the differences in petrophysical parameters among different rock units and the vertical variations caused by lithology and sedimentary structures revealed by well logging curves. In particular, it employs an RGB fusion method based on multiple well logging information to achieve intuitive display of multi-information data. Compared with traditional methods, the method of this invention is more intuitive, faster, and integrates more information. This invention provides a fast and effective method for well logging classification and identification of lithological types in shale oil and gas reservoirs, promoting accurate description and precise development of shale oil and gas reservoirs. Attached Figure Description

[0052] Figure 1 This is a flowchart of the method of the present invention.

[0053] Figure 2 These are the logging curve data and calculation results of the target layer data in this embodiment of the invention. Detailed Implementation

[0054] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments. However, the following examples are merely simplified examples of the present invention and do not represent or limit the scope of protection of the present invention. The scope of protection of the present invention is determined by the claims.

[0055] The study area in the following embodiments is located in the Ordos Basin. The target layer is the second sandstone group and the first sub-layer within the seventh section of the Yanchang Formation. In this embodiment, one shale oil development well is selected. There is one core well in the target layer of the study area. The core section is 20m long. The lithofacies color template established in this embodiment is derived from the data of this core section.

[0056] This embodiment provides a method and process for shale oil reservoir lithofacies classification based on RGB fusion of well logging information, according to... Figure 1 The process is as shown, and the data in the example are as follows. Figure 2 The method and process described herein include the following steps:

[0057] S1: Calculate the reflection coefficient sequence of the target layer using sonic transit time logging curves and density logging curves, and divide the rock unit based on the first derivative of the reflection coefficient curves. Specifically, this includes:

[0058] (1) The reflection coefficient sequence of the target layer is calculated using the sonic time-of-flight logging curve and the density logging curve. The reflection coefficient curve is obtained by interpolating the reflection coefficient sequence according to the sampling interval of 0.125m.

[0059] (2) Calculate the first derivative of the reflection coefficient curve to obtain the first derivative curve of the reflection coefficient curve. Using the points where the first derivative is negative as boundaries, divide the study section of the embodiment into 7 rock units, and number the rock units from top to bottom according to depth as L1, L2, L3, L4, L5, L6, L7, L8, L9, L1, L1, L2 ...1, L2, L1, L1, L1, L1, L2, L1 3，…， L7;

[0060] S2: For the 7 rock units obtained in step S1, calculate the root mean square of the natural gamma logging curve value of each rock unit as the lithological index value of that rock unit.

[0061] S3: For the 7 rock units obtained in step S1, calculate the roughness of the resistivity curves for each rock unit, and use it as the sedimentary structure index value of the rock unit.

[0062] S4: For the 7 rock units obtained in step S1, calculate the root mean square of the resistivity logging curve value of each rock unit as the oil-bearing index value of that rock unit.

[0063] S5: For the 7 rock units obtained in step S1, the lithological index values, sedimentary structure index values, and oil-bearing index values ​​obtained in steps S2, S3, and S4 are used to form the lithofacies classification evaluation parameters for each rock unit. The RGB color model is used to express the lithofacies classification evaluation parameters of shale oil and gas reservoirs in color, and the color expression results of the whole well section are obtained.

[0064] S6: Establish an RGB color template for lithofacies classification of shale oil and gas reservoirs in the study area. The specific steps are as follows:

[0065] (1) Using the method in step S1, the 20m core samples from the core wells in the study area were divided into 60 rock units;

[0066] (2) The lithofacies type of each rock unit was identified by observing the core, and a total of 6 lithofacies types were identified;

[0067] (3) Using the methods in steps S1 to S5, the RGB color expression of lithofacies classification evaluation parameters of 60 rock units in the core section of the study area is performed to obtain the lithofacies color expression of the 60 rock units.

[0068] (4) The lithofacies color expression of 60 rock units is divided into 6 groups according to lithofacies type. Each lithofacies type of rock unit is considered as a group, resulting in 6 lithofacies color expressions, which serve as lithofacies color templates for shale oil and gas reservoirs in the study area.

[0069] S7: Compare the color representation results of the 7 rock units obtained in step S5 with the lithofacies color template of the shale oil and gas reservoir in the study area established in step S6 to determine the lithofacies type of the rock unit in the target layer and obtain the lithofacies type of all rock units in the target layer.

[0070] In this embodiment, the data used is as follows: Figure 2 As shown, since this is only a demonstration of the method, the amount of data used is small, with only 7 rock units. In actual large-scale industrial applications, each well typically uses several thousand to tens of thousands, or even more, of typical data sampling points.

[0071] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method and process for lithological classification of shale oil reservoirs based on RGB fusion of well logging information, characterized in that, The identification method includes the following steps: S1: Calculate the reflection coefficient sequence of the target layer using the sonic transit time logging curve and density logging curve, obtain the reflection coefficient curve by interpolation, and divide the rock unit using the first derivative of the reflection coefficient curve. S2: For each rock unit obtained in step S1, calculate the root mean square of the natural gamma logging curve value of the rock unit as the lithological index value of the rock unit. S3: For each rock unit obtained in step S1, calculate the roughness of the resistivity curve, which is used as the sedimentary structure index value of that rock unit. S4: For each rock unit obtained in step S1, calculate the root mean square of the resistivity logging curve value of the rock unit as the oil-bearing index value of the rock unit. S5: For each rock unit obtained in step S1, the lithological index value, sedimentary structure index value, and oil-bearing index value obtained in steps S2, S3, and S4 are used to form the lithofacies classification evaluation parameters of each rock unit. The RGB color model is used to express the lithofacies classification evaluation parameters of shale oil and gas reservoirs in color, and the color expression results of the whole well section are obtained. S6: Use the core sections of the core wells to establish RGB color pattern maps of different types of lithofacies in the study area, which can be used as RGB color templates for lithofacies classification of shale oil and gas reservoirs in the study area. S7: Compare the color representation of the target layer rock units obtained in step S5 with the lithofacies color template of the shale oil and gas reservoir in the study area established in step S6 to determine the lithofacies type of the target layer rock units and obtain the lithofacies type of all rock units in the target layer.

2. The method according to claim 1, characterized in that, Step S1 includes the following steps: (1) For the i-th logging curve sampling point of the target layer at the well point from top to bottom according to depth, the reflection coefficient of the point is calculated using its acoustic transit time (AC) logging curve value and density (DEN) logging curve value. The reflection coefficient sequence of the target layer segment is obtained by calculating the reflection coefficient of each sampling point on the logging curve of the target layer using this method. The reflection coefficient curve is obtained by interpolation according to the sampling interval of 0.125m, where i is a natural number greater than zero. The specific calculation method is as follows: ； ； ； Where v is the longitudinal wave velocity of the rock, v i AC represents the P-wave velocity at the i-th logging point from top to bottom in the target layer, and AC is the sonic transit time logging value. i AI represents the sonic transit time logging value of the i-th logging curve sampling point from top to bottom in the target layer according to depth, where AI is the longitudinal wave impedance of the rock. i Let R be the longitudinal wave impedance of the i-th logging curve sampling point in the target layer from top to bottom according to depth, and R be the reflection coefficient of the rock. i Let ρ be the reflection coefficient of the i-th logging curve sampling point from top to bottom in the target layer according to depth, and ρ be the rock density logging value. i The density logging value is the sampling point of the i-th logging curve from top to bottom in the target layer according to the depth. (2) Calculate the first derivative of the reflection coefficient curve obtained in step (1). Using the points where the first derivative is negative as boundaries, divide the target layer into m rock units, and number the rock units L1, L2, L3, L4 according to depth from top to bottom. 3，…， L m , where m is a natural number.

3. The method according to claim 1 or 2, characterized in that, Step S2 includes the following steps: (1) Select the j-th rock unit L obtained in step S1 j Where j is a natural number greater than zero but not greater than m, the root mean square of the natural gamma-ray logging (GR) curve samples within the depth range corresponding to the rock element is calculated, and this root mean square is taken as the value of the rock element L. j The lithological index value is denoted as Lith j ; (2) Calculate the lithological index values ​​of all m rock units in the entire target section using the method in step (1).

4. The method according to claim 1 or 2, characterized in that, Step S3 includes the following steps: (1) Select the j-th rock unit L obtained in step S1 j Where j is a natural number greater than zero but not greater than m, the roughness of the resistivity logging (Rt) curve of the rock element is calculated and used as the roughness of the rock element L. j The sedimentary tectonic index value, denoted as SedStr j The specific calculation formula is as follows: ； Where t is the j-th rock unit L j The corresponding number of sampling points for the resistivity logging curve, where t is a natural number greater than 1, and p is L. j The corresponding sampling point on the resistivity curve, where p is a natural number greater than zero and not greater than t, x p Rock unit L j The resistivity logging curve value of the p-th sampling point; (2) Calculate the sedimentary structural index values ​​of all m rock units in the entire target section using the method in step (1).

5. The method according to claim 1 or 2, characterized in that, Step S4 includes the following steps: (1) Select the j-th rock unit L obtained in step S1 j Where j is a natural number greater than zero but not greater than m, the root mean square of the resistivity logging curve samples within the depth range corresponding to the rock element is calculated, and this root mean square is taken as the value of the rock element L. j The oil content index value is denoted as OG. j ; (2) Calculate the oil-bearing index values ​​of all m rock units in the entire target section using the method in step (1).

6. The method according to claims 1, 2, 3, 4, and 5, characterized in that, Step S5 includes the following steps: (1) For the rock unit obtained in step S1, the lithological index value, sedimentary structure index value, and oil-bearing index value obtained in steps S2, S3, and S4 are used to form the lithofacies classification evaluation parameters of the rock unit. For the i-th rock unit L i Its lithofacies classification and evaluation parameters are denoted as vectors (Lith i , SedStr i OG i ); (2) Using the RGB color model to analyze rock unit L i The lithofacies classification and evaluation parameter vector is color-represented, where the three parameters in the vector correspond to the R, G, and B parameter components in the color model, respectively, to obtain the rock unit L. i Color expression; (3) Using the methods in steps (1) and (2), the lithofacies classification evaluation parameters of m rock units in the target layer are expressed by color scale, and the color expression results of the whole well section are obtained.

7. The method according to claims 1, 2, 3, 4, 5, and 6, characterized in that, Step S6 includes the following steps: (1) The core section of the study area was divided into rock units using the method in step S1. A total of q rock units were divided, where q is a natural number greater than zero. (2) Using the traditional core observation and analysis method in the industry, the lithofacies types of q rock units were identified and classified, and a total of r lithofacies types were identified, where r is a natural number greater than zero and not greater than q; (3) Using the methods in steps S1 to S5, the lithofacies classification evaluation parameters of q rock units in the core section of the study area are expressed in color, and the lithofacies color expression of q rock units is obtained. (4) The lithofacies color expression of q rock units is divided into r groups according to lithofacies type. Each lithofacies type of rock unit is used as a group to obtain the color expression of r lithofacies type, which serves as the lithofacies color template for shale oil and gas reservoirs in the study area.

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