Compact glutenite reservoir evaluation method based on lithofacies-pore throat structure and application

Abstract

The invention discloses a compact glutenite reservoir evaluation method based on a lithofacies-pore throat structure and application. The method comprises the following steps: step 1, establishing a lithofacies division scheme in a compact glutenite reservoir; 2, determining the reservoir performance of different lithofacies in the compact glutenite reservoir; 3, determining reservoir space types and features of different lithofacies in the compact glutenite reservoir; 4, analyzing pore throat structure characteristics of different lithofacies in the compact glutenite reservoir to obtain multi-type pore throat structure parameters; 5, establishing a reservoir performance and pore throat structure parameter database of different lithofacies in the compact glutenite reservoir; step 6, carrying out compact glutenite reservoir clustering by utilizing a Ward system clustering method; and step 7, in combination with productivity conditions and physical characteristics of different lithofacies in the compact glutenite reservoir, proofreading and perfecting a reservoir clustering result, and finally establishing a compact glutenite reservoir classification evaluation scheme. The invention further discloses application of the method in oil exploitation.

Description

Technical Field

[0001] The invention belongs to the technical field of oilfield geological exploration, and in particular relates to a dense sandy conglomerate reservoir evaluation method based on lithofacies-pore throat structure and application thereof. Background Art

[0002] With the continuous improvement of the exploration level of conventional oil and gas resources around the world, tight oil and gas as unconventional oil and gas resources have become a hot spot for exploration and development.

[0003] Several oil and gas basins in eastern and western China have made good oil and gas discoveries in tight conglomerate. However, such reservoirs are often highly heterogeneous, with large differences in reservoir characteristics, oil content, and the technological measures required for exploitation. They generally have no natural production capacity and require large-scale fracturing to form industrial production capacity. Therefore, in the process of exploration and development, the classification and evaluation results of such reservoirs directly affect the effect and benefits of exploration and development. Therefore, it is urgent to establish an effective, scientific, and highly operational evaluation method for tight conglomerate reservoirs.

[0004] At present, the research on classification and evaluation of tight reservoirs mainly uses pore throat structure parameters combined with physical property parameters for evaluation. Although many scholars have studied the boundaries of tight reservoir accumulation, they have not systematically and fully classified tight sandstone conglomerate reservoirs, and have not formed an effective method to screen out relatively high-quality parts from low-permeability tight sandstone conglomerate oil and gas resources. As a result, in the process of exploration and development of tight sandstone conglomerate oil and gas, especially in the absence of natural production capacity, it is impossible to guide the selection of favorable layers in a single well and carry out targeted engineering process measures, which affects the exploration and development of oil and gas in tight sandstone reservoirs. For example, Wang Weiming et al. published a paper titled "Method for Determining Key Parameters of Tight Sandstone Gas Resource Potential: Taking the Tuha Basin as an Example" in Petroleum and Natural Gas Geology in 2014, which constructed energy storage evaluation parameters and divided tight sandstone gas reservoirs into three categories based on the tripartite relationship between energy storage evaluation parameters and porosity. Although the reservoir evaluation parameters took into account permeability and oil saturation, the final reservoir classification scheme was only attributed to the porosity factor, without considering the influence of other control factors. In 2018, Li Yingyan et al. published a paper titled "Classification Standard and Application of Geological Sweet Spots in Conglomerate Tight Oil Reservoirs of Baikouquan Formation in Mahu Sag, Junggar Basin" in the Journal of Northeast Petroleum University. The paper divided the conglomerate oil layers into sweet spots, inefficient oil layers and non-oil layers based on the step-by-step change of oil production intensity with oil-bearing porosity. Although the basis of the classification scheme is clear, it does not explain the microscopic characteristics and influencing factors of the reservoirs in different types of oil layers, and its method is not scientific and reasonable. In 2020, Huang Jianbo et al. published a paper titled "Classification of Low-Permeability Conglomerate Reservoirs in the Baikouquan Formation in the Mahu Sag, Junggar Basin" in Well Logging Technology. The paper divided conglomerates into four types based on reservoir physical properties and production capacity information, but did not provide a basis for dividing the boundaries of physical property parameters of different types of reservoirs, making it difficult to apply in actual exploration practice. In 2022, Gao Yang published a paper titled "Physical Property Classification and Controlling Factors of Conglomerate Reservoirs in the Mahu Sag, Junggar Basin" in the Journal of Chengdu University of Technology. The paper established a classification scheme for the physical properties of conglomerate reservoirs based on the microstructural characteristics of the mercury injection curve morphology, starting pressure, and throat radius main peak distribution of conglomerate samples. Although the classification scheme is clear, the reservoir control factors are not included in the establishment of the reservoir classification evaluation scheme, resulting in a reduction in the credibility of the classification scheme.

[0005] Chinese invention patent application CN106021788A discloses a method for classifying evaluation standards for tight reservoirs based on pore throat structure characteristics, which includes the following steps: 1) determining the upper threshold of tight reservoirs based on the principle of mechanical equilibrium; 2) analyzing the pore throat structure of tight reservoirs based on mercury injection experiments; 3) determining the lower limit of oil content of tight reservoirs based on the pore throat structure characteristics of oil-bearing tight reservoirs; 4) determining the lower limit of physical properties of enriched tight oil reservoirs based on the relationship between tight reservoir physical properties and production capacity; 5) establishing the relationship between tight reservoir pore throat structure and reservoir physical properties, and dividing the evaluation standards for tight reservoir physical properties. However, the above method only uses pore throat structure parameters to classify reservoirs of different levels, which is a rather one-sided method. However, under actual geological conditions, the pore throat structure parameter values ​​of different types of reservoirs often overlap to a certain extent. However, even if the pore throat structure parameters are the same, if the lithofacies are different, the reservoir quality will still be different. Therefore, if we only start from the pore structure and physical property parameters without considering the impact of other parameters on reservoir classification, the prediction results may have certain deviations. Therefore, this method lacks scientific rigor and is not very applicable.

[0006] Chinese invention patent application CN105334149A discloses a method for evaluating the microscopic pore structure of a tight reservoir and classifying the reservoir, the method comprising the following steps: 1) analyzing the genesis and petrological characteristics of the reservoir, determining the phase type, composition, etc.; 2) analyzing the physical properties of the reservoir; 3) observing the reservoir storage space characteristics; 4) measuring the reservoir pore structure parameters by experimental methods; 5) analyzing the control factors of the reservoir physical properties; 6) classifying the reservoir. However, the method ultimately determined the lithology, storage space type, and pore size of the three types of reservoirs based on microscopic identification and experimental testing, but its applicability was not reflected, nor was its distribution of high-quality reservoir types and how favorable targets were determined. Therefore, the classification method cannot be well applied to the determination of exploration targets.

[0007] Chinese invention patent application CN113820754A discloses a method for evaluating favorable tight sandstone reservoirs by using artificial intelligence to identify reservoir diagenetic phases. This method clarifies the logging response identification characteristics of different reservoir diagenetic phases, calculates the comprehensive evaluation index of different reservoir diagenetic phases at a single well point, draws a plane map of the reservoir comprehensive evaluation index, and predicts favorable areas of tight sandstone reservoirs. However, the identification and appraisal of diagenetic phases in this method is not very operational, and it is difficult to apply it to exploration practice. Therefore, it is relatively difficult to apply it to exploration practice. Summary of the invention

[0008] Purpose of the invention: In order to solve the technical problem of low prediction accuracy of tight conglomerate reservoirs in oil and gas basins, the present invention provides a tight conglomerate reservoir evaluation method based on lithofacies-pore throat structure and its application.

[0009] The present invention is aimed at tight conglomerate reservoirs. The core of the present invention is to establish a classification and evaluation scheme for this type of reservoir based on two dimensions: "lithophanes", the fundamental factor controlling the quality of the reservoir, and "pore throat structure", which controls the difference in reservoir production capacity.

[0010] The storage performance and permeability of tight conglomerate reservoirs are mainly affected by the pore-throat structure characteristics of different lithofacies reservoirs. The tight conglomerate reservoirs are classified based on the lithofacies-pore-throat structure characteristics, which explains the differences in reservoir performance at different levels from a mechanism perspective. Combined with the production capacity and oil content analysis, it is possible to classify and grade the tight conglomerate reservoirs from the perspective of geological genesis.

[0011] The present invention classifies tight sandy conglomerate reservoirs, establishes reservoir classification and evaluation standards, clarifies the oil and gas production standards of reservoirs of different levels to a certain extent, and has important guiding significance for the exploration, development and strategic deployment of tight sandy conglomerate reservoirs at this stage. The present invention is closely related to exploration and deployment, and the method is used to predict favorable reservoirs. The prediction results are more based on geological genesis and rationality, and fit the actual exploration and development of oil fields.

[0012] Technical solution: A method for evaluating tight conglomerate reservoirs based on lithofacies-pore-throat structure. The specific steps are as follows:

[0013] Step 1: Conduct petrological characteristic analysis of tight conglomerate reservoirs, count and compare the differences in petrological characteristics of tight conglomerate reservoirs, and establish a lithofacies division scheme in tight conglomerate reservoirs;

[0014] Step 2, determining the reservoir properties of different lithofacies in the tight conglomerate reservoir;

[0015] Step 3, determining the reservoir space types and characteristics of different lithofacies in the tight conglomerate reservoir;

[0016] Step 4, analyzing the pore throat structure characteristics of different lithofacies in the tight sandy conglomerate reservoir to obtain multiple types of pore throat structure parameters;

[0017] Step 5, establishing a database of reservoir performance and pore throat structure parameters of different lithofacies in tight conglomerate reservoirs;

[0018] Step 6: Clustering tight conglomerate reservoirs using the Ward system clustering method;

[0019] Step 7: Based on the production capacity and physical property characteristics of different lithofacies in the tight conglomerate reservoir, the reservoir clustering result of step (6) is checked and improved, and finally a classification and evaluation scheme for the tight conglomerate reservoir is established.

[0020] Furthermore, the technical means for conducting petrological characteristic analysis of the sandstone reservoir in step (1) include at least one of core observation, microscopic thin section identification, and whole-rock mineral X-ray diffraction.

[0021] Furthermore, in step (1), the lithofacies of the tight conglomerate reservoir are divided into six categories: medium-coarse conglomerate phase, fine conglomerate phase, gravelly sandstone phase, gravelly sandstone phase, medium-coarse sandstone phase, and silt-fine sandstone phase.

[0022] Furthermore, the specific division scheme of the tight conglomerate reservoir lithofacies in step (1) is as follows:

[0023]

[0024] Furthermore, the specific steps of step (2) are: respectively counting the categories of all tight conglomerate reservoir facies divided in step (1), the distribution ranges and mean levels of porosity and permeability of different facies reservoirs, and comparing and analyzing that there are differences in the physical property conditions and porosity-permeability relationship changes of different facies reservoirs.

[0025] Furthermore, the specific steps of step (3) are as follows: by means of core observation and thin section identification observation, the reservoir space types of different facies in the tight conglomerate reservoir are quantitatively analyzed by using the point statistics method, wherein:

[0026] Primary pores, secondary pores and fractures are the main reservoir space types in tight conglomerate reservoirs.

[0027] Furthermore, in step (4), conventional mercury injection test, constant rate mercury injection test and micron-level CT scanning are used to analyze the pore throat structure characteristics of different lithofacies of the tight conglomerate reservoir, wherein:

[0028] Based on conventional mercury injection testing, the maximum distribution range and mean of pore throat radius, the distribution range and mean of pore throat radius, the median distribution range and mean of pore throat radius, the homogeneity coefficient, the coefficient of variation, the structural coefficient, the maximum mercury injection saturation, and the mercury withdrawal efficiency of different lithofacies of tight conglomerate reservoirs are obtained;

[0029] Based on the constant rate mercury injection test, the distribution range and mean of pore radius, throat radius, and main peak value of throat radius in different lithofacies of tight conglomerate reservoir are obtained.

[0030] Based on micron-scale CT scanning experiments, the pore-throat ratios and coordination numbers of different lithofacies in tight conglomerate reservoirs were obtained.

[0031] Furthermore, the database parameters in step (5) include: mean pore throat radius, maximum pore throat radius, median pore throat radius, homogeneity coefficient, coefficient of variation, structural coefficient, maximum mercury injection saturation, mercury withdrawal efficiency, main peak value of throat radius, and lithofacies type.

[0032] Furthermore, the specific steps of step (6) are: taking the mean value of pore throat radius, the maximum value of pore throat radius, the median value of pore throat radius, the homogeneity coefficient, the coefficient of variation, the structural coefficient, the maximum mercury injection saturation, the mercury withdrawal efficiency, the main peak value of throat radius, and the lithofacies type as variables, performing cluster analysis on the parameter data of the above variables, and dividing the tight conglomerate reservoir into three major categories and five minor categories from the perspective of mathematical analysis, among which:

[0033] The three major categories are Category I, Category II and Category III, among which:

[0034] Category I consists of Category I1 and Category I2;

[0035] Class III consists of Class II1 and Class II2.

[0036] Furthermore, sub-category I1 is ultra-low permeability-low permeability reservoirs, whose lithofacies are gravelly sandstone facies and gravelly sandstone facies;

[0037] The second subcategory is tight and ultra-low permeability reservoirs, whose lithofacies are gravelly sandstone, gravelly sandstone and fine conglomerate.

[0038] Furthermore, sub-category II1 is tight-ultra-low permeability reservoir, whose lithofacies are fine conglomerate facies and gravelly sandstone facies;

[0039] The II2 sub-category is tight reservoir, whose lithofacies are fine conglomerate facies and gravelly sandstone facies.

[0040] The third category is tight reservoirs, with the lithofacies being medium-coarse conglomerate, medium-coarse sandstone, silt-fine sandstone, and a small amount of gravel-bearing sandstone.

[0041] The above-mentioned tight sandstone reservoir evaluation method based on lithofacies-pore throat structure is applied in oil production.

[0042] Furthermore, the reservoirs of Category I can produce oil or condensate oil and gas after measures are taken, the reservoirs of Category II can produce condensate oil and gas or pure gas after measures are taken, and the reservoirs of Category III can produce low-yield oil and gas after measures are taken.

[0043] The main pore-throat structural parameters that control reservoir classification are determined as the maximum pore-throat radius and its distribution range, the mean pore-throat radius and its distribution range, and the main peak value of the throat radius and its distribution range.

[0044] Beneficial effects: The dense sandy conglomerate reservoir evaluation method based on lithofacies-pore throat structure and its application disclosed in the present invention have the following beneficial effects:

[0045] It effectively solves the problem of low accuracy in predicting favorable tight conglomerate reservoirs in oil and gas basins in the existing technology, improves the prediction effect of reservoir distribution, and has important theoretical significance and application value for accurately predicting favorable tight conglomerate reservoirs and oil reservoir exploration and development in oil and gas basins. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The present invention discloses a flow chart of a method for evaluating a tight sandstone reservoir based on lithofacies-pore-throat structure.

[0047] Figure 2 This is a scatter plot of porosity and permeability of different lithofacies in tight conglomerate reservoirs.

[0048] Figure 3 Figure 2 is the relationship diagram between porosity and lithofacies of different lithofacies in tight sandstone conglomerate reservoirs.

[0049] Figure 4 This is the relationship diagram between permeability and lithofacies of different lithofacies in tight sandstone conglomerate reservoirs.

[0050] Figure 5 Schematic diagram of the maximum distribution of pore throat radius contributions of different rock phases to physical properties in tight sandy conglomerate reservoirs (based on conventional mercury injection testing).

[0051] Figure 6 This is a distribution bar chart of the mean and maximum pore throat radius of different lithofacies conglomerates in tight conglomerate reservoirs (based on high-pressure mercury injection testing).

[0052] Figure 7 This is the pore radius distribution frequency diagram of different lithologies in tight conglomerate reservoirs (based on constant rate mercury injection testing).

[0053] Figure 8 This is the throat radius distribution frequency diagram of different lithologies of tight conglomerate reservoirs (based on constant-rate mercury injection testing).

[0054] Fig. 9 This is a cladogram of Ward system cluster analysis. Specific implementation method:

[0055] In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, preferred implementation examples are given below and described in detail with reference to the accompanying drawings.

[0056] Example 1

[0057] Taking the tight sandy conglomerate reservoir in the Yanjia area of ​​the Dongying Depression in the Jiyang Depression exploration area of ​​Sinopec Shengli Oilfield Branch as an example, the technical solution of the present invention is verified.

[0058] The evaluation method of tight conglomerate reservoir based on lithofacies-pore throat structure has the following specific steps:

[0059] Step 1: Conduct petrological characteristic analysis of tight conglomerate reservoirs, count and compare the differences in petrological characteristics of tight conglomerate reservoirs, and establish a lithofacies division scheme for tight conglomerate reservoirs;

[0060] Focusing on the tight conglomerate reservoirs of different fan groups in the Yanjia area of ​​the Dongying Sag, the rock composition, component characteristics, rock type classification and rock structure characteristics of the conglomerate were analyzed by core observation, thin section identification, whole-rock mineral X-ray diffraction and other means to clarify the petrological characteristics of the conglomerate.

[0061] Based on gravel content, particle size, sedimentary structure, lithology, particle contact relationship, support type, dissolution degree, and roundness degree, the division standard and scheme of tight sandy conglomerate lithofacies were established, and the tight sandy conglomerate reservoir was divided into 6 categories, including medium-coarse conglomerate phase, fine conglomerate phase, gravelly sandstone phase, gravelly sandstone phase, medium-coarse sandstone phase, and silt-fine sandstone phase. The specific division scheme is shown in the following table:

[0062]

[0063] Step 2: Determine the reservoir properties of different facies in the tight conglomerate reservoir:

[0064] The distribution range and mean level of porosity and permeability of the six lithofacies were statistically analyzed and compared. The physical property conditions and porosity-permeability relationship of different lithofacies were different (e.g. Figure 2 ), where:

[0065] The pore conditions of gravelly sandstone, gravelly sandstone and fine conglomerate facies are relatively good (e.g. Figure 3 However, the permeability of the gravel sandstone phase is significantly better than that of the gravel sandstone and fine gravel phase (as shown in Figure 4 shown).

[0066] Step 3: Determine the reservoir space types and characteristics of different lithofacies in tight conglomerate reservoirs:

[0067] Through core observation and microscopic thin section identification observation, quantitative analysis was performed using the "point statistics method". It was found that primary pores, microcracks and their associated secondary dissolution pores are the main reservoir space types of tight conglomerate. There are differences in the reservoir space types between different lithofacies reservoirs. The specific differences are shown in the following table:

[0068]

[0069] Step 4: Analyze the pore throat structure characteristics of different lithofacies in tight conglomerate reservoirs and obtain multi-type pore throat structure parameters:

[0070] By combining conventional mercury injection, constant rate mercury injection and micron-scale CT, the maximum distribution range of pore throat radius and the maximum mean of pore throat radius of different lithofacies were obtained based on conventional mercury injection experiments (e.g. Figure 6 ), pore throat radius distribution range and mean (as shown in Figure 5 As shown), the median distribution range and mean of pore throat radius, mean coefficient, coefficient of variation, structural coefficient, maximum mercury injection saturation, and mercury removal efficiency;

[0071] Based on the constant rate mercury injection test, the distribution range and mean of pore radius of different lithofacies reservoirs were obtained (e.g. Figure 7 As shown in the figure), throat radius distribution range and mean value (as shown in the figure Figure 8 As shown), the main peak distribution range and mean of throat radius;

[0072] Based on the micron CT scanning experiment, the pore-throat ratio and coordination number of different lithofacies reservoirs are obtained. The micron-level CT scanning pore-throat structure parameter table of different lithofacies sandstone is as follows:

[0073]

[0074] Step 5: Establish a database of reservoir properties and pore throat structure parameters of different lithofacies in tight conglomerate reservoirs: The database parameters include: mean pore throat radius (Rd), maximum pore throat radius (R0), median pore throat radius (R50), homogeneity coefficient, coefficient of variation, structure coefficient, maximum mercury injection saturation, mercury withdrawal efficiency, main peak value of throat radius, and lithofacies type.

[0075] Step 6: Clustering of tight conglomerate reservoirs using the Ward system clustering method:

[0076] A total of 10 parameters, including the mean pore throat radius (Rd), the maximum pore throat radius (R0), the median pore throat radius (R50), the homogeneity coefficient, the coefficient of variation, the structural coefficient, the maximum mercury injection saturation, the mercury withdrawal efficiency, the main peak value of the throat radius, and the lithofacies type, were used as variables to perform cluster analysis on the parameter data. The analysis matrix of the Ward system clustering is as follows:

[0077] Approximate Matrix

[0078]

[0079] From the perspective of mathematical analysis, the tight sandstone conglomerate reservoirs in the study area are divided into three categories and five subcategories, such as Fig. 9 As shown in the dendrogram, the data in the database is clustered according to the cluster analysis method, and similar data are grouped into one category. Fig. 9 Here ①, ②, and ③ represent three major categories, among which:

[0080] ① can be divided into two small categories, ② can be divided into two small categories, and ③ cannot be further divided into small categories due to relatively less data and more lithofacies types, so there are 5 categories in total.

[0081] The main pore-throat structural parameters that control reservoir classification are determined as the maximum pore-throat radius and its distribution range, the mean throat radius and its distribution range, and the main peak of throat radius and its distribution range.

[0082] Ward system clustering method, also known as the deviation square sum method, is a mature clustering method suitable for multi-factor, multi-index classification and feature identification. It can be comprehensively classified according to the multi-faceted characteristics of the research object, highlighting the homogeneity and the difference between the regions, and reflecting the comprehensive differences between the research objects. This method is based on the idea of ​​variance analysis and takes the Euclidean distance as the standard. Each sample in the set is first classified into a class. When merging categories, the variance between the class centroids is calculated, and the two categories with the smallest increase in the deviation square sum are merged, and then all categories are merged step by step.

[0083] Among them, the principle and algorithm of Ward system clustering method are as follows:

[0084] Assume that N samples have been divided into k categories G1, G2, ..., Gk, and Xit represents G t The variable indicator value vector of the i-th sample in n t Represents class G t The number of samples in X t Represents class G t The center of gravity of G t The formula for the sum of squared deviations of the samples in is S t :

[0085]

[0086] If classes Ga and Gb are merged into a new class Gr, the sum of squared deviations of each class can be expressed as

[0087]

[0088]

[0089]

[0090] The above formula can be used to define the square of the inter-class distance between Ga and Gb as

[0091] D 2 ab =S r -S a -S b

[0092] Where Gr = Ga ∪ Gb, then any class distance is

[0093]

[0094] When judging whether two samples are similar, the distance between two points representing different samples can be used to show their similarity.

[0095] Let dij represent the Euclidean distance between sample Xi and sample Xj

[0096]

[0097] If the distance between two categories is close, the smaller the increase in the sum of squared deviations, the higher the similarity between the samples, and they can be classified into one category; the larger the sum of squared deviations between different categories, the lower the similarity between the samples.

[0098] Step 7: Based on the productivity characteristics and physical property characteristics of different lithofacies of the tight conglomerate reservoir, the reservoir classification results of step (6) are checked and improved to establish a classification and evaluation scheme for the tight conglomerate reservoir, wherein:

[0099] The statistical production capacity of single wells in tight conglomerate in the study area is shown in the following table:

[0100]

[0101] Analysis shows that the discovered oil and gas are mainly concentrated in three types of lithofacies: gravel sandstone, gravel-bearing sandstone and fine conglomerate.

[0102] The cluster analysis results were combined with the actual production capacity and reservoir properties of individual wells, and it was found that the production capacity of reservoirs corresponded well to the classification results. Reservoirs with high production capacity were concentrated in Class I and Class II reservoirs, while reservoirs with low production capacity were concentrated in Class III reservoirs.

[0103] The details are shown in the following table:

[0104]

[0105] When applying the operation in other regions, if it is found that the correspondence between the production capacity and the classification results is unreasonable at this step, return to step 6, reselect the pore throat structure parameters and perform cluster analysis until it is reasonable.

[0106] In the "lithofacies-pore throat structure" reservoir classification and evaluation scheme, eight parameters, namely lithofacies type, pore throat combination type, reservoir space type, maximum pore throat radius, mean pore throat radius, main peak value of throat radius, porosity, and permeability, are used to divide tight conglomerate reservoirs into three major categories and five minor categories. The specific classifications are as follows:

[0107] The three major categories are Category I, Category II and Category III, among which:

[0108] Category I consists of Category I1 and Category I2;

[0109] Class III consists of Class II1 and Class II2.

[0110] Furthermore, sub-category I1 is ultra-low permeability-low permeability reservoirs, whose lithofacies are gravelly sandstone facies and gravelly sandstone facies;

[0111] The second subcategory is tight and ultra-low permeability reservoirs, whose lithofacies are gravelly sandstone, gravelly sandstone and fine conglomerate.

[0112] Furthermore, sub-category II1 is tight-ultra-low permeability reservoir, whose lithofacies are fine conglomerate facies and gravelly sandstone facies;

[0113] The II2 sub-category is tight reservoir, whose lithofacies are fine conglomerate facies and gravelly sandstone facies.

[0114] The third category is tight reservoirs, whose lithofacies are medium-coarse conglomerate facies, medium-coarse sandstone facies, fine-silt sandstone facies, and a small amount of gravel-bearing sandstone facies.

[0115] The above-mentioned tight sandstone reservoir evaluation method based on lithofacies-pore throat structure is applied in oil production.

[0116] Furthermore, the reservoirs of Category I can produce oil or condensate oil and gas after measures are taken, the reservoirs of Category II can produce condensate oil and gas or pure gas after measures are taken, and the reservoirs of Category III can produce low-yield oil and gas after measures are taken.

[0117] The specific results are shown in the following table:

[0118]

[0119] Example 2

[0120] In the application practice, the pore throat structure (Rd) parameter logging prediction model was used to evaluate the reservoir at a depth of 4415m in the Fsx11 well. The specific results are shown in the following table:

[0121]

[0122] Calculation shows that the average pore throat radius (Rd) is 0.908μm. Combined with the lithofacies type (gravel-bearing sandstone) at this depth, it is known that the reservoir category is I2. According to the classification evaluation plan, this type of reservoir produces oil or condensate oil and gas, which is consistent with the logging interpretation results and oil testing results.

[0123] Among them, the well logging evaluation model of pore throat parameters of sandstone is:

[0124] log2Rd=20.65549+0.657165DEN+1.04825CNL-0.58023AC

[0125] (R 2 =0.738)

[0126] In the formula, Rd refers to the average pore throat radius at a certain depth of a single well; DEN is the density logging value at a certain depth of a single well; CNL is the neutron logging value at a certain depth of a single well; AC is the sonic time difference logging value at a certain depth of a single well.

[0127] Example 3

[0128] Under the guidance of the method of the present invention, during 2021-2022, the tight sandy conglomerate reservoir targets in the Yanjia area of ​​the Dongying Depression were predicted, which guided the reservoir evaluation and production layer optimization of wells such as Fsx11, Fsx12, and Yx941. After fracturing and oil testing, high-yield oil and gas flows were obtained, guiding the discovery of more than 6 million tons of new reserves of tight sandy conglomerate in the Dongying Depression, and achieved good exploration results.

[0129] The above describes the embodiments of the present invention in detail. However, the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of ordinary technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A method for evaluating tight conglomerate reservoirs based on lithofacies-pore-throat structure, characterized in that: The specific steps are as follows: Step 1: Conduct petrological characteristic analysis of tight conglomerate reservoirs, count and compare the differences in petrological characteristics of tight conglomerate reservoirs, and establish a lithofacies division scheme in tight conglomerate reservoirs; Step 2, determining the reservoir properties of different lithofacies in the tight conglomerate reservoir; Step 3, determining the reservoir space types and characteristics of different lithofacies in the tight conglomerate reservoir; Step 4, analyzing the pore throat structure characteristics of different lithofacies in the tight sandy conglomerate reservoir to obtain multiple types of pore throat structure parameters; Step 5, establishing a database of reservoir performance and pore throat structure parameters of different lithofacies in tight conglomerate reservoirs; Step 6: Clustering tight conglomerate reservoirs using the Ward system clustering method; Step 7: Based on the production capacity and physical property characteristics of different lithofacies in the tight conglomerate reservoir, the reservoir clustering result of step (6) is checked and improved, and finally a classification and evaluation scheme for the tight conglomerate reservoir is established.

2. The method for evaluating a tight conglomerate reservoir based on lithofacies-pore-throat structure according to claim 1, characterized in that: The technical means for conducting petrological characteristic analysis of the sandstone reservoir in step (1) include at least one of core observation, microscopic thin section identification, and whole-rock mineral X-ray diffraction.

3. The method for evaluating a tight conglomerate reservoir based on lithofacies-pore-throat structure according to claim 1, characterized in that: In step (1), the lithofacies of the tight sandy conglomerate reservoir are divided into six categories: medium-coarse conglomerate phase, fine conglomerate phase, gravelly sandstone phase, gravelly sandstone phase, medium-coarse sandstone phase, and silt-fine sandstone phase.

4. The method for evaluating a tight conglomerate reservoir based on lithofacies-pore-throat structure according to claim 3, characterized in that: The specific division scheme of the tight conglomerate reservoir lithofacies in step (1) is as follows:

5. The method for evaluating a tight conglomerate reservoir based on lithofacies-pore-throat structure according to claim 1, characterized in that: The specific steps of step (2) are: respectively counting the categories of all tight conglomerate reservoir facies divided in step (1), the distribution range and mean level of porosity and permeability of different facies reservoirs, and comparing and analyzing that there are differences in the physical property conditions and porosity-permeability relationship changes of different facies reservoirs.

6. The method for evaluating a tight conglomerate reservoir based on lithofacies-pore-throat structure according to claim 1, characterized in that: The specific steps of step (3) are as follows: by means of core observation and thin section identification observation, the reservoir space types of different facies in the tight conglomerate reservoir are quantitatively analyzed by using the point statistics method, wherein: Primary pores, secondary pores and fractures are the main reservoir space types in tight conglomerate reservoirs.

7. The method for evaluating a tight conglomerate reservoir based on lithofacies-pore-throat structure according to claim 1, characterized in that: In step (4), conventional mercury injection test, constant rate mercury injection test and micron-level CT scanning are used to analyze the pore throat structure characteristics of different lithofacies of the tight conglomerate reservoir, wherein: Based on conventional mercury injection testing, the maximum distribution range and mean of pore throat radius, the distribution range and mean of pore throat radius, the median distribution range and mean of pore throat radius, the homogeneity coefficient, the coefficient of variation, the structural coefficient, the maximum mercury injection saturation, and the mercury withdrawal efficiency of different lithofacies of tight conglomerate reservoirs are obtained; Based on the constant rate mercury injection test, the distribution range and mean of pore radius, throat radius, and main peak value of throat radius in different lithofacies of tight conglomerate reservoirs are obtained. Based on micron-scale CT scanning experiments, the pore-throat ratios and coordination numbers of different lithofacies in tight conglomerate reservoirs were obtained.

8. The method for evaluating a tight conglomerate reservoir based on lithofacies-pore-throat structure according to claim 1, characterized in that: The database parameters in step (5) include: mean pore throat radius, maximum pore throat radius, median pore throat radius, homogeneity coefficient, coefficient of variation, structural coefficient, maximum mercury injection saturation, mercury withdrawal efficiency, main peak value of throat radius, and lithofacies type.

9. The method for evaluating a tight conglomerate reservoir based on lithofacies-pore-throat structure according to claim 1, characterized in that: The specific steps of step (6) are: taking the mean value of pore throat radius, the maximum value of pore throat radius, the median value of pore throat radius, the homogeneity coefficient, the coefficient of variation, the structural coefficient, the maximum mercury injection saturation, the mercury withdrawal efficiency, the main peak value of throat radius, and the lithofacies type as variables, clustering analysis is performed on the parameter data of the above variables, and the tight conglomerate reservoir is divided into three major categories and five minor categories from the perspective of mathematical analysis, among which: The three major categories are Category I, Category II and Category III, among which: Category I consists of Category I1 and Category I2; Class III consists of Class II1 and Class II2.

10. The method for evaluating a tight conglomerate reservoir based on lithofacies-pore-throat structure according to claim 9, characterized in that: The first sub-category is ultra-low permeability-low permeability reservoirs, whose lithofacies are gravelly sandstone facies and gravelly sandstone facies; The second subcategory is tight and ultra-low permeability reservoirs, whose lithofacies are gravelly sandstone, gravelly sandstone and fine conglomerate.

11. The method for evaluating a tight conglomerate reservoir based on lithofacies-pore-throat structure according to claim 9, characterized in that: Sub-category II1 is tight-ultra-low permeability reservoir, whose lithofacies are fine conglomerate facies and gravelly sandstone facies; The II2 sub-category is tight reservoir, whose lithofacies are fine conglomerate facies and gravelly sandstone facies.

12. The method for evaluating a tight conglomerate reservoir based on lithofacies-pore-throat structure according to claim 9, characterized in that: The third category is tight reservoirs, with the lithofacies being medium-coarse conglomerate, medium-coarse sandstone, silt-fine sandstone, and a small amount of gravel-bearing sandstone.

13. Application of the tight conglomerate reservoir evaluation method based on lithofacies-pore-throat structure as claimed in any one of claims 1 to 12 in oil production.

14. The use according to claim 13, characterized in that The reservoirs of Category I can produce oil or condensate oil and gas after treatment, the reservoirs of Category II can produce condensate oil and gas or pure gas after treatment, and the reservoirs of Category III can produce low-yield oil and gas after treatment.

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