Fracture-vug type reservoir rock facies fine division method

Abstract

The invention discloses a fractured-vuggy reservoir rock facies fine division method. The method comprises the steps that single well data of a to-be-measured area is acquired and preprocessed; carrying out depth homing on the required core physical property data; performing principal component analysis on the single well data to obtain dimension reduction and noise reduction fusion representation data; the data set is divided into a clustering data set and an inspection data set; carrying out optimal classification cluster selection on the clustering data set by adopting an elbow rule; classifying the data by adopting a fuzzy C-means improved clustering algorithm, and establishing an identification model of the fractured-vuggy reservoir rock facies; evaluating the identification model of the fractured-vuggy reservoir rock facies by adopting the contour coefficient; the identification model of the fractured-vuggy reservoir rock facies is inspected in combination with the inspection data set; outputting a test clustering result by using the test identification model of the fractured-vuggy reservoir rock facies, and performing sensitivity analysis by using a rank correlation matrix; and intersection is carried out by adopting the sensitive curves to obtain an identification chart of the fracture-vuggy reservoir rock facies.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas reservoir exploration and development, in particular to a method for finely dividing rock phases of fracture-cavity reservoirs. Background Art

[0002] In recent years, with the continuous deepening of exploration, great progress has been made in the study and understanding of microbial mounds and beaches. However, the distribution of high-quality reservoir phases and the mechanism of reservoir formation still need to be further explored, which seriously restricts the further efficient development of oil fields. As the basis of mound and beach structure, rock phase is a concentrated reflection of the macroscopic physical property changes and microscopic pore structure characteristics of rocks. The configuration relationship of pores and fractures in different rock phases is very different, resulting in large differences in the storage and permeability of the internal structure of mounds and beaches and strong heterogeneity. The accurate identification of rock phases is of great significance to the subsequent research on mound and beach gas reservoirs. Therefore, it is necessary to carry out detailed identification and division of the rock phases in the block, implement the vertical and horizontal distribution laws of the rock phases, and provide technical support for the next step of accurate well layout, oil well productivity evaluation, prediction of favorable zones for remaining oil, and evaluation of water injection development potential.

[0003] Among them, rock phase is a comprehensive concept, which refers to the reservoir genetic unit with certain rock physical properties and can reflect sedimentation, diagenesis and later tectonic transformation. It manifests itself as changes in reservoir rock physical properties at the macro level and as heterogeneous characteristics of reservoir pore structure at the micro level. Its related research has broad application prospects in the process of oil and gas exploration and development, improving the success rate of exploration wells, and can also evaluate and predict the fluidity of fluids, providing geological basis for exploration and development goals. Since the concept of rock phase was proposed, geologists have proposed many rock phase identification methods based on basic data and research practices. They can be roughly summarized into the following categories:

[0004] First, the rock phase identification method based on geological theory: according to the principles of sedimentation and diagenesis, it roughly divides the sedimentary phase environment according to the sedimentary interface and diagenetic environment by surveying the outcrops in the field, and then subdivides the rock phase type according to the rock fragments and core thin sections of each sedimentary phase. Based on the microscopic analysis of rock thin sections in the target reservoir area, the relationship between the logging identification template and the seismic data body is established to identify and predict the dolomite reservoir. For example, the public technology of "publication number CN106908856A, named a method for seismic prediction of lacustrine thin-layer dolomite reservoirs", and the public technology of "publication number CN109577962A, named a method for analyzing the rock phase of terrestrial fine-grained sedimentary rocks", which classifies rock phases according to lithology or lithology combination, organic carbon content, silica content and calcium content, and establishes rock phase classification rules. Some geologists identify rock phases based on core descriptions and combined with the characteristics of changes in lithology and physical properties, establish rock phase classification and naming rules, and divide rock phase types, thereby clarifying favorable rock phases and distribution ranges, such as the public technology "Publication No. CN105201490A, Named A Shale Layer Rock Phase Analysis Method". The advantage of this type of method is that the theoretical basis is relatively sufficient, and the disadvantage is that it can only roughly divide rock phases on a macro scale. The division accuracy is not high for strata with strong heterogeneity and many rock phase types, and it is impossible to accurately characterize the distribution characteristics of reservoir intervals and interlayers, making it difficult to meet the needs of detailed reservoir description.

[0005] Second, the classification method based on cores and related data: for example, the public technology of "publication number CN115078434A, named "Identification method of phase-controlled karst in early diagenesis of carbonate rocks based on coupling of petrology and geochemistry", this method analyzes the relationship between rock fabric, karstification and geochemical characteristics, and classifies the karst intensity to establish an early diagenesis phase-controlled karst identification model. Another example is the public technology of "publication number CN111814879A, named "a classification method and device, electronic equipment and storage medium for reservoir rock physical phase", this method divides unimodal reservoirs or / and bimodal reservoirs according to the size of the pore throat radius distribution peak value of multiple capillary pressure curves and the preset interval, determines multiple J function curves of the unimodal reservoir or / and the bimodal reservoir; establishes the relationship between the multiple J function curves and water saturation, and determines the classification of reservoir rock physical phases. It should be pointed out that the advantage of this type of method is that it uses reliable core analysis and testing data, and the basis for classification is also relatively sufficient. However, the disadvantage of this type of technology is that if you want to finely characterize the flow unit of the entire reservoir, you need to conduct continuous coring experiments on a large number of wells. Due to the constraints of drilling engineering technology, coring conditions, high economic costs and other factors, most oil fields do not have such conditions, so this method is not convenient for widespread application research in the region.

[0006] Third, the rock phase classification method using mathematical methods: In recent years, the advantages of machine learning in solving complex classification problems are gradually being recognized by people. This method is currently widely used in major oil fields. The overall implementation idea is to first select macroscopic and microscopic features that reflect rock structure and structure, rock physical properties and fluid properties through cores, thin sections and various analytical tests, establish rock phase classification standards, standard rock phase logging response characteristics, and select suitable artificial intelligence algorithms to establish rock phase identification models to divide rock phase types. For example, the public technology of "publication number CN201910889252.7, named a reservoir classification and identification method based on support vector machine algorithm" solves the nonlinear problem between reservoir rock phase types and multiple factors to a certain extent. Another example is the public technology of "Deep Tight Sandstone Reservoir Evaluation Method with Publication Number CN113820754A and Named "Deep Tight Sandstone Reservoir Evaluation Method for Identifying Reservoir Diagenetic Phases Based on Artificial Intelligence". For deep tight sandstone reservoirs, the diagenetic phases are first distinguished based on the lithology classification results, and the logging data of the determined rock phases are used to train the BP neural network to identify the diagenetic phase type of each well in the study area. This type of method is a sublimation of the second type of method (based on the division method of core and related data). Its advantage is that it uses more characteristic parameters. Based on the idea of ​​geological-geophysical integration, it makes full use of the advantages of artificial intelligence to divide rock phases. The division method is developing from qualitative to quantitative, from linear to nonlinear, and from low-dimensional space to high-dimensional space. Its accuracy is higher. However, this type of method also has shortcomings. For reservoirs with complex configurations of fractures, holes, and caves and strong heterogeneity, the rock phase types are complex and diverse, and the identification of their rock phase types far exceeds the identification accuracy of logging data. How to ensure that the division results are of geological significance and not be interfered by human factors and reasonably merge the rock phases to control them within the scope of logging identification capabilities is an urgent problem to be solved.

[0007] Therefore, it is urgent to propose a method for fine division of rock phases in fracture-vuggy reservoirs that is simple in logic, accurate and reliable. Summary of the invention

[0008] In view of the above problems, the purpose of the present invention is to provide a method for fine division of rock phases of fracture-cavity reservoirs. The technical solution adopted by the present invention is as follows:

[0009] A method for fine division of rock phases of fracture-cavity reservoirs, comprising the following steps:

[0010] Obtain single well data of the area to be tested and preprocess the single well data;

[0011] Combined with the pre-processed single well data, the required core physical property data is depth-relocated;

[0012] The pre-processed single well data is subjected to principal component analysis to obtain dimension reduction, noise reduction and fusion characterization data for rock phase identification;

[0013] The clustering data set and test data set of the rock phase identification model are established by using dimensionality reduction, noise reduction and fusion characterization data;

[0014] The elbow rule is used to select the optimal classification cluster for the clustering data set to obtain the most classification cluster number;

[0015] The clustering algorithm improved by fuzzy C-means is used to classify the clustering data set of the rock phase identification model, so as to establish the identification model of the rock phase of the fracture-cavity reservoir.

[0016] The silhouette coefficient is used to evaluate the identification model of fracture-vuggy reservoir rock phases;

[0017] The rock phase identification model of fracture-cavity reservoir is tested in combination with the test data set;

[0018] The clustering results were tested using the output of the identification model of fracture-cavity reservoir rock phases, and the rank correlation matrix was used to perform sensitivity analysis to obtain the sensitivity analysis results.

[0019] The identification plate of fracture-vuggy reservoir rock phase is obtained by using sensitive curve intersection.

[0020] Furthermore, the single well data include natural gamma curve, compensated neutron curve, compensated acoustic wave curve, density curve, deep lateral resistivity and shallow lateral resistivity curve, mass photoelectric absorption cross section index curve, potassium spectrum curve, uranium spectrum curve, and thorium spectrum curve.

[0021] Furthermore, the expression of the dimension reduction and noise reduction fusion characterization data var(X) is:

[0022]

[0023] Among them, a i represents the value of feature X of the preprocessed single well data; μ x Represents the mean of the preprocessed single well data.

[0024] Furthermore, the expression of the identification model of the fracture-cavity reservoir rock phase is:

[0025]

[0026] stp=log e 4n(e-1)-1

[0027] Among them, e represents the number of classifications; p represents the parameter that makes up for the defects of the partition coefficient and clustering function; b ij Represents the sample point x iFor cluster center k j The degree of membership; Represents the sample point x i With cluster center k j The density sensitive distance; ρ represents the scaling factor; ω represents the adjustment factor of fuzzy entropy; G represents the identification model of fracture-cavity reservoir rock phase; s.t. p represents the calculation formula for making up for the defective parameters of the partition coefficient and clustering function.

[0028] Furthermore, the initial cluster center is no longer randomly selected, but is calculated by the average density-sensitive distance, which can effectively solve the noise interference problem and reduce the number of iterations. The function is:

[0029]

[0030] In the formula, is the average density sensitive distance; is the number of combinations of selecting any two sample points from n samples.

[0031] Furthermore, the expression of the rank correlation matrix is:

[0032]

[0033] Among them, ρ represents the correlation, x i ,y i represents any two sets of clustered data, Represents clustered data x i The average value of Represents clustered data y i The average value of .

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] (1) The present invention uses natural gamma curves, compensated neutron curves, compensated acoustic wave curves, density curves, deep lateral resistivity and shallow lateral resistivity curves, mass photoelectric absorption cross-section index curves, potassium spectrum curves, uranium spectrum curves, thorium spectrum curves, etc. as well logging data for the work area of ​​the standard well, and can extract useful information through multi-source complex data as much as possible.

[0036] (2) The present invention performs principal component analysis on the logging curve to obtain dimension reduction, noise reduction and fusion characterization data for rock phase identification. The advantage is that it reduces the noise in multi-source data, provides the proportion of useful information, and reduces the dimension, thereby greatly shortening the amount of calculation.

[0037] (3) The present invention adopts the elbow rule to select the optimal classification cluster for the data. The advantage is that the similarity of the data can be judged by the characteristics of the data itself, which can greatly reduce the influence of human factors and make the clustering characteristics more objective and reasonable.

[0038] (4) The present invention adopts a fuzzy C-means improved clustering algorithm to classify data and establishes an identification model for the rock phase of fracture-cavity reservoirs. In addition, an identification model for the rock phase of fracture-cavity reservoirs is established based on the fuzzy C-means improved clustering algorithm, and a test data set is used to test the effect, which makes the clustering results more reasonable and credible.

[0039] (5) The present invention uses a rank correlation matrix to perform sensitivity analysis, and then selects sensitive curves for intersection based on the sensitivity analysis results to obtain a fracture-vuggy reservoir rock phase identification map. The advantage of this method is that after screening sensitive parameters, the identification map established can be quickly applied to the same type of oil fields, greatly improving the applicability and interpretability of the method.

[0040] In summary, the present invention has the advantages of simple logic, accuracy and reliability, and has high practical value and promotion value in the technical field of oil and gas reservoir exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope of protection. For those skilled in the art, other related drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 It is a logic flow chart of the present invention.

[0043] Figure 2 This is the AC-DEN rock phase identification intersection plate of the Deng 4th Member in the present invention.

[0044] Figure 3 This is the AC-DEN rock phase identification intersection plate of the Deng 2 section in the present invention.

[0045] Figure 4 This is a diagram of the rock phase classification prediction and evaluation results of a certain well in the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical scheme and advantages of this application clearer, the present invention is further described below in conjunction with the accompanying drawings and embodiments, and the embodiments of the present invention include but are not limited to the following embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0047] In this embodiment, the term "and / or" is merely a term used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone.

[0048] The terms "first" and "second" in the description and claims of this embodiment are used to distinguish different objects rather than to describe a specific order of objects. For example, a first target object and a second target object are used to distinguish different target objects rather than to describe a specific order of target objects.

[0049] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0050] In the description of the embodiments of the present application, unless otherwise specified, the meaning of "multiple" refers to two or more than two. For example, multiple processing units refer to two or more processing units; multiple systems refer to two or more systems.

[0051] like Figures 1 to 4 As shown, this embodiment provides a method for fine division of rock phases of fracture-cavity reservoirs. This embodiment takes the fracture-cavity reservoirs of Dengying Formation (Deng II and Deng IV) in Sichuan Basin as an example, which includes the following steps:

[0052] The first step is to select a single well with the most complete work area data in the area to be tested as the standard well.

[0053] The second step is to pre-process the logging data of the work area according to the standard well. The logging data of the work area include natural gamma curve, compensated neutron curve, compensated acoustic wave curve, density curve, deep lateral resistivity and shallow lateral resistivity curve, mass photoelectric absorption cross-section index curve, potassium spectrum curve, uranium spectrum curve, thorium spectrum curve, etc.

[0054] The third step is to perform depth homing on the core physical property data to be used later, that is, homing to the logging depth. Here, the core data generally has a certain depth error with the wellbore logging test data, and the error is eliminated by depth homing.

[0055] The fourth step is to perform principal component analysis on the preprocessed single well data to obtain the dimension reduction, noise reduction and fusion characterization data for rock phase identification, which is expressed as:

[0056]

[0057] Among them, a i represents the value of feature X of the preprocessed single well data; μ x Represents the mean of the preprocessed single well data.

[0058] Here, the statistics of the fusion method characterization data based on PCA dimension reduction and denoising are shown in Table 1:

[0059] Table 1 Data statistics of fusion method characterization based on PCA dimensionality reduction and denoising

[0060]

[0061]

[0062] The fifth step is to use the dimensionality reduction, noise reduction and fusion characterization data to establish the clustering data set and test data set of the rock phase identification model. Among them, 80% of the data is used as the model clustering data, and the remaining 20% ​​of the data is used as the model test data.

[0063] In the sixth step, for the clustering data set, the elbow rule is used to select the optimal classification clusters for the data and obtain the most efficient number of classification clusters.

[0064] In the seventh step, according to the number of the most classified clusters, the fuzzy C-means improved intelligent clustering method (FCM-DSDFP) which has more advantages in complex mapping relationships is selected to intelligently classify the data and establish a fine identification model of fracture-cavity reservoir rock phase. Its expression is:

[0065]

[0066] stp=log e 4n(e-1)-1

[0067] Among them, e represents the number of classifications; p represents the parameter that makes up for the defects of the partition coefficient and clustering function; b ij Represents the sample point x i For cluster center k j The degree of membership; Represents the sample point x i With cluster center k jThe density sensitive distance; ρ represents the scaling factor; ω represents the adjustment factor of fuzzy entropy; G represents the identification model of fracture-cavity reservoir rock phase; stp represents the calculation formula for compensating the partition coefficient and clustering function defect parameters. Here, FCM-DSDFP (i.e., FCM Algorithm Based on DensitySensitive Distance and Fuzzy Partrition) is an improvement of the FCM unsupervised clustering method. Among them, FCM adopts mechanisms such as membership degree and cluster center, and uses the alternating optimization algorithm for intelligent solution and clustering. It has fewer parameters and is more advantageous in complex mapping relationships. However, due to the random selection characteristics of the initial cluster center of this method and the local optimality of the Euclidean distance algorithm, FCM is easily disturbed by noise points, the clustering effect is unstable, and the global consistency is ignored, resulting in a decrease in clustering accuracy.

[0068] Compared with the traditional fuzzy C-means (FCM) algorithm, the initial cluster center of the improved intelligent clustering method (FCM-DSDFP) is no longer randomly selected, but is calculated by the average density-sensitive distance, which can effectively solve the noise interference problem and reduce the number of iterations. Its function is:

[0069]

[0070] In the formula, is the average density sensitive distance; is the number of combinations of selecting any two sample points from n samples.

[0071] FCM-DSDFP overcomes the defects of FCM and achieves global optimal clustering by introducing density-sensitive distance and fuzzy entropy. It can be used to solve the problem of complex reservoir rock phase prediction.

[0072] In the eighth step, the silhouette coefficient is used to evaluate the identification model of the fracture-cavity reservoir rock phase, and an evaluation threshold is preset. When the evaluation result is less than the evaluation threshold, return to the seventh step for classification.

[0073] The ninth step is to test the identification model of fracture-cavity reservoir rock phase with the test data set. Similarly, the identification model is evaluated and analyzed until it is greater than the evaluation threshold.

[0074] The tenth step is to use the identification model output of fracture-cavity reservoir rock phase to test the clustering results, and use the rank correlation matrix to perform sensitivity analysis to obtain the sensitivity analysis results, as shown in Table 2:

[0075] Table 2 Sensitivity analysis based on rank matrix

[0076]

[0077] Step 11: According to the sensitivity analysis results, select the sensitivity curves for intersection to obtain the rock phase identification plate of the fracture-cavity reservoir, such as Figure 2 and Figure 3 shown. Figure 2 and Figure 3 The results show that the improved intelligent clustering method (FCM-DSDFP) has obvious effect on the classification of rock phases in the 4th and 2nd members of the Dengying Formation. Each type of rock phase has a high consistency with the core. The improved intelligent clustering method (FCM-DSDFP) can divide the rock phases in the 4th and 2nd members of the Dengying Formation into 7 categories respectively.

[0078] in, Figure 4 (a) and Figure 4 (b) in the figure shows the rock phase classification test results of the 4th and 2nd sections of the Dengying Formation. Figure 4 It can be seen that Figure 4 (a) shows a core well of the 4th member of the Dengying Formation. The core is 16 meters long. The rock phase classification results obtained by the improved intelligent clustering method (FCM-DSDFP) model are consistent with the rock phases divided by the core at a rate of 88.26%, which is consistent with 14.12 meters; Figure 4 (b) shows a core well of the 4th section of the Dengying Formation, with a core length of 13 meters. The rock phase classification result obtained by the improved intelligent clustering method (FCM-DSDFP) model is 87.12% consistent with the rock phase classified by the core, which is consistent with 11.32 meters. The rock phase classification prediction results evaluated by the method of this embodiment all achieved good evaluation results.

[0079] The above embodiments are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any changes that adopt the design principles of the present invention and are made through non-creative labor on this basis should fall within the protection scope of the present invention.

Claims

1. A method for fine division of rock phases of fracture-cavity reservoirs, characterized in that: The following steps are involved: Obtain single well data of the area to be tested and preprocess the single well data; Combined with the pre-processed single well data, the required core physical property data is depth-relocated; The pre-processed single well data is subjected to principal component analysis to obtain dimension reduction, noise reduction and fusion characterization data for rock phase identification; The clustering data set and test data set of the rock phase identification model are established by using dimensionality reduction, noise reduction and fusion characterization data; The elbow rule is used to select the optimal classification cluster for the clustering data set to obtain the most classification cluster number; The clustering algorithm improved by fuzzy C-means is used to classify the clustering data set of the rock phase identification model, so as to establish the identification model of the rock phase of the fracture-cavity reservoir. The silhouette coefficient is used to evaluate the identification model of fracture-vuggy reservoir rock phases; The rock phase identification model of fracture-cavity reservoir is tested in combination with the test data set; The clustering results were tested using the output of the identification model of fracture-cavity reservoir rock phases, and the rank correlation matrix was used to perform sensitivity analysis to obtain the sensitivity analysis results. The identification plate of fracture-vuggy reservoir rock phase is obtained by using sensitive curve intersection.

2. A method for fine division of rock phases of fracture-cavity reservoirs according to claim 1, characterized in that: The single well data include natural gamma curve, compensated neutron curve, compensated acoustic wave curve, density curve, deep lateral resistivity and shallow lateral resistivity curve, mass photoelectric absorption cross-section index curve, potassium spectrum curve, uranium spectrum curve, and thorium spectrum curve.

3. The method for fine division of rock phases of fracture-cavity reservoir according to claim 1, characterized in that: The expression of the dimension reduction, noise reduction and fusion characterization data var(X) is: Among them, a i represents the value of feature X of the preprocessed single well data; μ x Represents the mean of the preprocessed single well data.

4. A method for fine division of rock phases of fracture-cavity reservoirs according to claim 1, 2 or 3, characterized in that: The expression of the identification model of the fracture-cavity reservoir rock phase is: s.t.p=log e 4n(e-1)-1 Among them, e represents the number of classifications; p represents the parameter that makes up for the defects of the partition coefficient and clustering function; b ij Represents the sample point x i For cluster center k j The degree of membership; Represents the sample point x i With cluster center k j The density sensitive distance; ρ represents the scaling factor; ω represents the adjustment factor of fuzzy entropy; G represents the identification model of fracture-cavity reservoir rock phase; s.t. p represents the calculation formula for making up for the defective parameters of the partition coefficient and clustering function.

5. A fracture-cavity reservoir rock phase fine division method according to claim 4, characterized in that: The expression of the rank correlation matrix is: Among them, ρ represents the correlation, x i ,y i represents any two sets of clustered data, Represents clustered data x i The average value of Represents clustered data y i The average value of .

6. A method for fine division of rock phases of fracture-cavity reservoirs according to claim 4, characterized in that: The sample point x i With cluster center k j Density sensitive distance Average density sensitive distance The expression is obtained as follows: in, is the average density sensitive distance; is the number of combinations of selecting any two sample points from n samples.

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