Carbonate rock lithology judgment method based on fuzzy comprehensive evaluation
By using the fuzzy comprehensive evaluation method and well logging data to determine the lithology of carbonate rocks, the problem of lithology identification under the condition of no physical data was solved, and quantitative lithological classification of carbonate reservoirs was realized.
Patent Information
- Application Number
- CN202510988839.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-17
- Publication Date
- 2025-11-28
AI Technical Summary
Existing technologies struggle to accurately identify the lithology of carbonate rocks for which no physical data exists, especially in carbonate reservoir exploration, where quantitative identification is difficult to achieve solely through conventional well logging calculations.
The fuzzy comprehensive evaluation method is adopted. By acquiring drilling and logging data, a factor set, a weight set, and an evaluation set are established. A membership function and a fuzzy relation matrix are constructed, and lithology is judged in combination with the maximum membership principle.
It enables quantitative classification of carbonate rock lithology, applicable to wells with only logging curves and no physical core data, thus improving the accuracy and reliability of lithology identification.
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Figure CN121031806A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the field of oil and gas exploration, and particularly relates to a carbonate rock lithology judgment method based on fuzzy comprehensive evaluation. BACKGROUND
[0002] Lithology identification is the basis for the exploration and research of carbonate rock reservoirs. At present, the lithology identification method for carbonate rocks in production mainly relies on rock physical experiments, microscope analysis, X-ray diffraction and quantitative analysis of logging curve related technologies. According to the real core and cutting data, a relatively accurate lithology identification result can be obtained. However, if there is no real data for the carbonate rock formed in different sedimentary environments, it is difficult to quantitatively identify through conventional logging calculation. Therefore, the carbonate rock lithology identification technology based on fuzzy comprehensive evaluation is proposed for the drilling without real data.
[0003] CONTENT OF THE APPLICATION
[0004] The application aims to provide a carbonate rock lithology judgment method based on fuzzy comprehensive evaluation, and the specific technical solutions are as follows.
[0005] The carbonate rock lithology judgment method based on fuzzy comprehensive evaluation comprises the following steps: S1, obtaining the drilling and logging data of the relevant area and discretizing the logging curve of the well with lithology to be judged; S2, respectively establishing the factor set, obtaining the weight set and establishing the evaluation set according to the drilling and logging data of the relevant area obtained in S1; S3, establishing the membership function and calculating the evaluation parameter membership of each discrete point based on the factor set established in S2; S4, constructing the fuzzy relationship matrix of each discrete point for the evaluation parameter membership obtained in S3, coupling the fuzzy relationship matrix with the weight set obtained in S2 to obtain the fuzzy comprehensive evaluation matrix of each discrete point; and S5, comparing the fuzzy comprehensive evaluation matrix obtained in S4 with the evaluation set established in S2 according to the maximum membership principle, and finally obtaining the lithology judgment of the well with lithology to be judged in S1.
[0006] The logging curve of the well with lithology to be judged in S1 comprises a natural gamma curve, a neutron curve and a uranium curve.
[0007] The analytic hierarchy process is adopted when the weight set is obtained in S2, wherein the target layer is lithology division, the standard layer is the logging curve, and the scheme layer is the possible lithology.
[0008] The establishment of the evaluation set in S2 includes: S2.21, according to the existing carbonate rock lithology classification standard, combined with the use habits in the related regional oil and gas exploration and development, the lithology is divided into four categories by collecting regional cored segment physical data, and the evaluation set V=(I, II, III, IV) is established, wherein I is a sandy bioclastic grain limestone, II is a bioclastic silt grain limestone, III is a bioclastic grain silt limestone, and IV is a siltstone; S2.22, combined with the logging data obtained in S1, an optimal statistical method is used to establish a lithology fuzzy comprehensive evaluation standard.
[0009] The weight set is obtained by using the analytic hierarchy process, and the process includes:
[0010] S2.31, the identification matrix is normalized:
[0011]
[0012] S2.32, each column of the normalized identification matrix is added by row:
[0013]
[0014] S2.33, the vector W is obtained: The characteristic vector W=(W1, W2, …, W n ) T .
[0015] S2.34, the maximum eigenvalue of the matrix is calculated
[0016]
[0017] S2.35, the consistency of the matrix is judged:
[0018]
[0019] In the formula, L is a consistency ratio, N is an average consistency index, M is a consistency index,
[0020]
[0021] When L is less than 0.1, the identification matrix has satisfactory consistency, otherwise the identification matrix needs to be re-established until satisfactory consistency is achieved.
[0022] S2.36, the synthetic weight of each layer factor to the target layer is calculated
[0023]
[0024] The establishment of the membership function in S3 is specifically:
[0025]
[0026] The fuzzy relation matrix constructed in S4 is as follows:
[0027]
[0028] The advantages of this invention are: the method is applicable to wells with only logging curves and no physical core data for carbonate reservoirs; the method can quantitatively classify carbonate lithology.
[0029] Instruction manual illustrations
[0030] Figure 1 This is a schematic diagram of the process of the present invention;
[0031] Figure 2 This is a lithological map of a well in the Maokou Formation of the Sichuan Basin where no physical data was available, as described in an embodiment of this invention. Specific Implementation
[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.
[0033] like Figure 1 As shown, a method for determining the lithology of carbonate rocks based on fuzzy comprehensive evaluation includes: S1, acquiring drilling and logging data from relevant areas, and discretizing the logging curves of the wells whose lithology is to be determined; S2, establishing a factor set, calculating a weight set, and establishing an evaluation set based on the drilling and logging data from relevant areas acquired in S1; S3, establishing a membership function and calculating the membership degree of the evaluation parameters for each discrete point based on the factor set established in S2; S4, constructing a fuzzy relation matrix for each discrete point based on the membership degree of the evaluation parameters obtained in S3, and coupling the fuzzy relation matrix with the weight set obtained in S2 to obtain the fuzzy comprehensive evaluation matrix for each discrete point; S5, comparing the fuzzy comprehensive evaluation matrix obtained in S4 with the evaluation set established in S2 according to the maximum membership principle, and finally obtaining the lithology determination of the wells whose lithology is to be determined in S1.
[0034] The logging curves for the wells whose lithology needs to be determined in S1 include natural gamma curves, neutron curves, and uranium curves.
[0035] The weight set in S2 is obtained using the analytic hierarchy process, where the target layer is the lithological classification, the standard layer is the well logging curve, and the scheme layer is the possible lithology.
[0036] The establishment of the evaluation set in S2 includes: S2.21, according to the existing carbonate rock lithology classification standard, combining with the use habit in the related regional oil and gas exploration and development, the lithology is divided into four categories by collecting regional cored segment physical data, and the evaluation set V=(I, II, III, IV) is established, wherein I is a sandy bioclastic grain limestone, II is a bioclastic silt grain limestone, III is a bioclastic grain silt limestone, and IV is a siltstone; S2.22, combining with the logging data obtained in S1, an optimal statistical method is used to establish a lithology fuzzy comprehensive evaluation standard.
[0037] The weight set is obtained by using the analytic hierarchy process, and the process includes:
[0038] S2.31, the identification matrix is normalized:
[0039]
[0040] S2.32, each column of the normalized identification matrix is added by row:
[0041]
[0042] S2.33, the vector W is obtained: The characteristic vector W=(W1, W2, …, W n ) T .
[0043] S2.34, the maximum eigenvalue of the matrix is calculated
[0044]
[0045] S2.35, the consistency of the matrix is judged:
[0046]
[0047] In the formula, L is a consistency ratio, N is an average consistency index, M is a consistency index,
[0048]
[0049] When L is less than 0.1, the identification matrix has satisfactory consistency, otherwise the identification matrix needs to be re-established until satisfactory consistency is achieved.
[0050] S2.36, the synthetic weight of each layer factor to the target layer is calculated
[0051]
[0052] The establishment of the membership function in S3 is specifically:
[0053]
[0054] The fuzzy relation matrix constructed in S4 is as follows:
[0055]
[0056] To make the present invention easier to understand, the following examples further illustrate it:
[0057] This embodiment conducts a comprehensive study on the lithological classification of the Maokou Formation carbonate rocks in the Sichuan Basin, such as... Figure 1 The process shown:
[0058] Step 1: Collect and organize drilling and logging data with physical lithology in the relevant area, discretize the well logging curves of the lithology to be determined, and set the sampling frequency to 1.
[0059] Step 2: Establish the factor set U as the natural gamma (GR), neutron (CNL), and uranium (U) curve values for wells without physical lithological data, i.e., U = (u1, u2, u3) = (natural gamma, neutron, uranium), n = 3.
[0060] Step 3: Obtain the weight set A = (0.43, 0.33, 0.24) for natural gamma (GR), neutron (CNL) and uranium (U) using the analytic hierarchy process (AHP).
[0061] Step 4: Establish evaluation set and evaluation criteria: Based on the existing lithological classification standards of carbonate rocks and the usage habits in oil and gas exploration and development in relevant areas, the lithology is divided into four categories by collecting physical data from cored sections in the region, and an evaluation set V = (Ⅰ, Ⅱ, Ⅲ, Ⅳ) = (sand-clastic bioclastic grain limestone, bioclastic mudstone, bioclastic grain marl, marl) is established. Combined with well logging data, the optimal statistical method is used to establish fuzzy comprehensive evaluation criteria for lithology (Table 1).
[0062]
[0063] Step 5: Establish the membership function:
[0064]
[0065] The membership degree r of the evaluation parameter for each discrete point is obtained by calculation.
[0066] Step 6: For each discrete point, construct the fuzzy relation matrix R for the membership degree r of the evaluation parameter, and couple it with the weight set A to obtain the fuzzy comprehensive evaluation matrix B for each point.
[0067] Step 7: Based on the principle of maximum membership, select the maximum value in each B and compare it with the lithological fuzzy comprehensive evaluation criteria in Step 4 to finally obtain the lithology of wells without core data. Figure 2 ).
Claims
1. A method for determining the lithology of carbonate rocks based on fuzzy comprehensive evaluation, characterized in that, include: S1. Obtain drilling and logging data for the relevant area, and discretize the logging curves of the wells whose lithology needs to be determined; S2. Based on the relevant regional drilling and logging data obtained in S1, establish the factor set, calculate the weight set, and establish the evaluation set respectively; S3. Establish a membership function and calculate the membership degree of the evaluation parameters for each discrete point based on the factor set established in S2. S4. Construct a fuzzy relation matrix for each discrete point based on the membership degree of the evaluation parameters obtained in S3, and couple the fuzzy relation matrix with the weight set obtained in S2 to obtain the fuzzy comprehensive evaluation matrix for each discrete point. S5. Based on the principle of maximum membership, the fuzzy comprehensive evaluation matrix obtained in S4 is compared with the evaluation set established in S2 to finally obtain the lithology judgment of the well to be judged in S1.
2. The method for determining the lithology of carbonate rocks based on fuzzy comprehensive evaluation as described in claim 1, characterized in that, The logging curves of the well to be judged in S1 include natural gamma curve, neutron curve and uranium curve.
3. The method for determining the lithology of carbonate rocks based on fuzzy comprehensive evaluation as described in claim 1, characterized in that, The weight set in S2 is obtained using the analytic hierarchy process, where the target layer is the lithology classification, the standard layer is the well logging curve, and the scheme layer is the possible lithology.
4. The method for determining the lithology of carbonate rocks based on fuzzy comprehensive evaluation as described in claim 1, characterized in that, The establishment of the evaluation set in S2 includes: S2.
21. Based on existing lithological classification standards for carbonate rocks and combined with the usage habits in oil and gas exploration and development in relevant areas, the lithology is divided into four categories by combining the physical data of the cored sections in the region, and an evaluation set V = (Ⅰ, Ⅱ, Ⅲ, Ⅳ) is established, where Ⅰ is sandy bioclastic grain limestone, Ⅱ is bioclastic mudstone, Ⅲ is bioclastic marl, and Ⅳ is marl. S2.
22. Based on the well logging data obtained in S1, an optimal statistical method is used to establish a fuzzy comprehensive evaluation standard for lithology.
5. The method for determining the lithology of carbonate rocks based on fuzzy comprehensive evaluation as described in claim 3, characterized in that, The analytic hierarchy process used to obtain the weight set includes: S2.
31. Normalize the identification matrix: S2.
32. Add each column of the normalized identification matrix row by row: S2.33, Transform the vector After normalization, the eigenvectors are obtained as follows: W = (W1, W2, ..., W...). n ) T ; S2.34 Calculate the largest eigenvalue of the matrix S2.35, Determine matrix consistency: In the formula, L represents the consistency ratio, N represents the average consistency index, and M represents the consistency index. When L is less than 0.1, the identification matrix has satisfactory consistency; otherwise, the identification matrix needs to be rebuilt until satisfactory consistency is achieved. S2.36 Calculate the composite weight of each layer's factors for the target layer.
6. The method for determining the lithology of carbonate rocks based on fuzzy comprehensive evaluation as described in claim 5, characterized in that, The membership function established in S3 is specifically as follows:
7. The method for determining the lithology of carbonate rocks based on fuzzy comprehensive evaluation as described in claim 6, characterized in that, The fuzzy relation matrix constructed in S4 is as follows: