Quantitative classification evaluation method for tight sandstone reservoir and related equipment

Abstract

The invention discloses a tight sandstone reservoir quantitative classification evaluation method and related equipment, and belongs to the field of petroleum exploration. According to the method, six key parameters including an oil layer structure index, an oil layer thickness, permeability and a variable coefficient thereof, and oil saturation and a variable coefficient thereof are optimized, and a comprehensive oil enrichment index is innovatively constructed; therefore, the tight sandstone reservoir type can be rapidly and accurately evaluated, and important guidance is provided for tight sandstone reservoir exploration and development. The method is simple and practical to operate, rapid and quantitative tight sandstone reservoir classification evaluation can be achieved, and the evaluation result can effectively guide tight sandstone reservoir exploration and development target optimization and targeted technical policy making.

Description

Technical Field

[0001] The invention belongs to the field of petroleum exploration, and in particular relates to a quantitative classification evaluation method for tight sandstone reservoirs and related equipment. Background Art

[0002] The classification and evaluation of oil reservoirs is the key to comprehensive research on oil reservoirs and the ultimate goal of comprehensive geological research on oil reservoirs. It mainly considers factors such as oil layer thickness, reservoir physical properties and oil content to evaluate the enrichment degree and quality of oil reservoirs, and provides an important basis for decision-making on oil reservoir exploration and development. For tight sandstone reservoirs, scientific and precise classification and evaluation of oil reservoirs is more important, because tight sandstone reservoirs are often more difficult and costly to explore and develop, and more scientific and precise classification and evaluation of oil reservoirs are needed to guide target selection and the formulation of targeted exploration and development technical countermeasures.

[0003] Technical research shows that many researchers at home and abroad have used different methods to classify and evaluate oil reservoirs from different perspectives, such as macroscopic physical parameter method, macro-micro parameter combination method, fuzzy optimization method, grey cluster analysis method, use of mercury injection data and physical property analysis data method, rock physical phase method, neural network method, Q-type cluster analysis and Bayesian discriminant combination method, Q-type cluster analysis and multi-level discriminant combination method, etc.

[0004] The above methods can basically be divided into two categories. One is a relatively qualitative comprehensive method, such as the macroscopic physical property parameter method and the macro-micro parameter combination method. It mainly selects macroscopic or microscopic parameters reflecting reservoir properties, permeability and oil content to establish a comprehensive classification evaluation table for reservoirs, and evaluates reservoir types based on the comprehensive evaluation table. This type of method is not quantitative enough, and the sizes of different parameters of the same reservoir are often not synchronized. For example, a reservoir can be judged as a certain category based on one parameter, and another category based on another parameter. The final classification requires comprehensive judgment, and the influence of human experience factors is large, and the classification is not scientific and quantitative enough. The Chinese patent publication number CN107366537A, "Method for evaluating undeveloped reserve blocks by classification of reservoirs", provides a method for evaluating undeveloped reserve blocks by classification of reservoirs, collects basic information of the blocks to be evaluated, establishes a basic information data table for reservoir evaluation, including basic information of the blocks, reserve information and change history, block geological information, block production information, reservoir physical properties and existing problems, evaluates and classifies each specific block one by one according to the classification principles, classification basis and classification standards for undeveloped reserve evaluation at different levels, and divides the undeveloped reserve block reservoirs into three categories. The specific parameters and the quantitative value range of the parameters are not mentioned, which is a qualitative comprehensive evaluation method, and the evaluation lacks quantification and operability.

[0005] The other type is quantitative mathematical methods, such as multiple linear regression, fuzzy identification, neural network, cluster analysis and Bayesian discriminant combination, Q-type cluster analysis and multi-level discriminant combination, etc., which are classified as pure mathematical methods. The advantage of this type of method is quantification, but it also has many disadvantages. It often uses mathematical methods to select evaluation parameters, geological factors are less considered, and the geological significance of the method cannot be clear; or sometimes only pays attention to multiple factors, and does not pay attention to the repetition and mutual interference between parameters, which affects the accuracy of identification; at the same time, this type of mathematical method often requires a large number of samples to establish a mathematical model. Only when the sample size is larger and more complete, the established mathematical model will be more accurate. However, in actual operation, it is often limited by the number of samples and cannot establish the optimal mathematical model. Moreover, the mathematical model is often different with different sample selections, so the accuracy of classification and identification cannot be guaranteed to be scientific and reasonable. For example, Cao Yingchang, Yang Tian, ​​Wang Yanzhong, etc., "Geological Multi-factor Comprehensive Quantitative Classification Evaluation of Ultra-low Permeability Reservoirs in Jiyang Depression" (Modern Geology, Issue 1, 2015), first defined the cumulative oil production difference to characterize the development effect to reflect the comprehensive characteristics of ultra-low permeability reservoirs, and then selected the oil layer thickness layer ratio, sandstone thickness layer ratio, average particle size, medium depth of oil production section, permeability, porosity, and specific reserve abundance as the geological multi-factor comprehensive quantitative classification evaluation parameters of ultra-low permeability reservoirs through geological law constraints, correlation analysis between geological factors, operability analysis, and the correlation between each parameter and cumulative oil production difference. With the help of gray correlation analysis, the weight coefficient of each evaluation parameter was determined, and the comprehensive evaluation score was calculated. Combined with the size of the cumulative oil production difference, a standard multi-factor comprehensive quantitative classification evaluation was formed, and the ultra-low permeability reservoirs in Jiyang Depression were divided into three categories. This method is a quantitative multi-factor quantitative comprehensive evaluation method, but too many parameters were selected, and there were duplications and interferences between the parameters, which affected the evaluation results. For example, there is duplication between the oil layer thickness ratio and the sandstone thickness ratio. The quality of the reservoir is related to the oil layer thickness. The thicker the sand layer is, the less likely it is to contain oil. Therefore, if the oil layer thickness ratio is selected, it is meaningless to select the sandstone thickness ratio. There is duplication and interference between the average particle size and the permeability and porosity. The average particle size affects the porosity and permeability. If the permeability and porosity are selected, there is no need to select the average particle size. In addition, this method does not take into account the heterogeneity of reservoir physical properties and oil content, and the heterogeneity is crucial to the impact of the reservoir.

[0006] Different oil reservoirs have different reservoir characteristics, and researchers consider problems from different starting points, so the evaluation methods adopted are also different. Tight sandstone reservoirs have strong reservoir and oil heterogeneity, and the vertical structure of the oil layer is complex. The vertical structure of the oil layer, oil layer thickness, permeability, oil content, and the heterogeneity of permeability and oil content have a vital impact on the reservoir. Most evaluation methods do not consider the key factors such as the vertical structural characteristics of the oil layer, the heterogeneity of physical properties, and the heterogeneity of oil content. They often select too many parameters to participate in the evaluation through mathematical methods, and do not consider the duplication and interference between parameters, which makes the evaluation results biased. Therefore, the key to solving the classification evaluation of tight sandstone reservoirs is to select indicator parameters with important geological representative significance and formulate a classification evaluation method that is highly targeted, simple, feasible, quantitative, and has promotion value. Summary of the invention

[0007] In order to overcome the shortcomings of the above-mentioned technology, the present invention provides a quantitative classification evaluation method and related equipment for tight sandstone reservoirs, which can solve the technical problem that the existing evaluation method needs to select a large amount of evaluation parameter data and does not consider the duplication and interference between parameters, resulting in low accuracy of the evaluation results.

[0008] In order to achieve the above object, the present invention adopts the following technical solution:

[0009] A quantitative classification and evaluation method for tight sandstone reservoirs, comprising:

[0010] Obtain the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient of the target oil reservoir;

[0011] Based on the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient, the comprehensive oil enrichment index is obtained;

[0012] Based on the comprehensive oil enrichment index and combined with the classification threshold of the comprehensive oil enrichment index, the evaluation type of the target oil reservoir is obtained.

[0013] Furthermore, the step of obtaining the oil layer structure index includes:

[0014] Obtaining the maximum single-layer oil layer thickness and the oil layer thickness, and obtaining the oil layer structure index based on the maximum single-layer oil layer thickness and the oil layer thickness;

[0015] Among them, oil layer structure index = maximum single oil layer thickness ÷ oil layer thickness.

[0016] Furthermore, the specific formula of the permeability variation coefficient is:

[0017]

[0018] Among them, VK represents the coefficient of variation of permeability; K i represents the i-th permeability value, i = 1, 2, 3, ... n; represents the average permeability.

[0019] Furthermore, the specific formula of the oil saturation variation coefficient is:

[0020]

[0021] Among them, V O represents the coefficient of variation of oil saturation; S Oj represents the jth oil saturation value, j = 1, 2, 3, ... m; Represents the average oil saturation.

[0022] Furthermore, the permeability and oil saturation are obtained through core physical property analysis experiments or logging interpretation.

[0023] Furthermore, the calculation formula of the comprehensive oil enrichment index is:

[0024] Comprehensive oil enrichment index = oil layer structure index * oil layer thickness * permeability * (1-permeability variation coefficient) * oil saturation * (1-oil saturation variation coefficient).

[0025] Furthermore, the comprehensive oil-richness index is compared with the comprehensive oil-richness index classification threshold to obtain the evaluation type of the target reservoir, where:

[0026] If the comprehensive oil-bearing enrichment index is ≥1, the target reservoir belongs to Class I reservoir;

[0027] If 0.1≤comprehensive oil-bearing enrichment index<1, the target reservoir belongs to Class II reservoir;

[0028] If the comprehensive oil enrichment index is less than 0.1, the target reservoir belongs to Class III reservoir.

[0029] A system for quantitative classification and evaluation of tight sandstone reservoirs, used to implement the steps of the above-mentioned method for quantitative classification and evaluation of tight sandstone reservoirs, comprising:

[0030] A parameter acquisition module is used to obtain the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient of the target oil reservoir;

[0031] An index calculation module, used to obtain a comprehensive oil-rich index based on the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient;

[0032] The oil reservoir evaluation module is used to obtain the evaluation type of the target oil reservoir based on the comprehensive oil enrichment index combined with the comprehensive oil enrichment index classification threshold.

[0033] A device comprising:

[0034] Memory for storing computer programs;

[0035] A processor is used to implement the steps of the above-mentioned method for quantitative classification and evaluation of tight sandstone reservoirs when executing the computer program.

[0036] A computer-readable storage medium stores a computer program, which, when executed by a processor, is used to implement the steps of the above-mentioned method for quantitative classification and evaluation of tight sandstone reservoirs.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention also provides a method for quantitative classification and evaluation of tight sandstone reservoirs. The method selects six key parameters, namely, oil layer structure index, oil layer thickness, permeability and its coefficient of variation, oil saturation and its coefficient of variation, and innovatively constructs a comprehensive oil enrichment index, thereby realizing rapid and accurate evaluation of tight sandstone reservoir types and providing important guidance for the exploration and development of tight sandstone reservoirs. The specific advantages are as follows:

[0039] 1. Through a lot of research and exploration attempts, six key parameters including reservoir structure index, reservoir thickness, permeability and its coefficient of variation, oil saturation and its coefficient of variation were selected for reservoir classification evaluation. Few parameters were selected but with obvious geological significance. Reservoir thickness, permeability and oil saturation are the key to reservoir quality. The vertical structural characteristics of the reservoir, permeability heterogeneity and oil heterogeneity were taken into consideration, and the parameter selection was highly targeted.

[0040] 2. By constructing a comprehensive oil-richness index to classify and evaluate reservoirs, it not only solves the problem of quantitative classification and evaluation, but also solves the problem that the evaluation results are difficult to determine when different parameters are inconsistent. In the index, oil layer thickness, permeability, and oil saturation are in a multiplicative relationship. When the three are poor, the index value is small, when the three are good, the index value is large, and when some are poor and some are good, the index value is medium. The product of the three can complement each other, avoiding the error or difficulty in determining the type of reservoir simply based on the quality of the three.

[0041] 3. This method is simple and practical to operate, and can realize rapid quantitative classification and evaluation of tight sandstone reservoirs. The evaluation results can effectively guide the optimization of exploration and development targets and the formulation of targeted technical policies for tight sandstone reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1A flowchart of a method for quantitative classification and evaluation of tight sandstone reservoirs provided by an embodiment of the present invention;

[0043] Figure 2 The CH6 in JY region of the Ordos Basin provided by the embodiment of the present invention 1 1 Classification result of the comprehensive oil content index method for small-layer tight sandstone reservoirs;

[0044] Figure 3 A flowchart of a quantitative classification and evaluation method for tight sandstone reservoirs provided by the present invention;

[0045] Figure 4 A schematic structural diagram of a quantitative classification and evaluation system for tight sandstone reservoirs provided by the present invention. DETAILED DESCRIPTION

[0046] The present invention provides a method for quantitative classification and evaluation of tight sandstone reservoirs. Figure 3 As shown, the following steps are included:

[0047] S1: Obtain the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient of the target reservoir.

[0048] Specifically, the steps of obtaining the oil layer structure index include:

[0049] Obtaining the maximum single-layer oil layer thickness and the oil layer thickness, and obtaining the oil layer structure index based on the maximum single-layer oil layer thickness and the oil layer thickness;

[0050] Among them, oil layer structure index = maximum single oil layer thickness ÷ oil layer thickness.

[0051] Among them, the specific formula of permeability variation coefficient is:

[0052]

[0053] Among them, V K represents the coefficient of variation of permeability; K i represents the i-th permeability value, i = 1, 2, 3, ... n; represents the average permeability.

[0054] Among them, the specific formula of oil saturation variation coefficient is:

[0055]

[0056] Among them, V O represents the coefficient of variation of oil saturation; S Oj represents the jth oil saturation value, j = 1, 2, 3, ... m; Represents the average oil saturation.

[0057] Among them, permeability and oil saturation are obtained through core physical property analysis experiments or well logging interpretation.

[0058] S2: Based on the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient, a comprehensive oil enrichment index is obtained.

[0059] Among them, the calculation formula of comprehensive oil enrichment index is:

[0060] Comprehensive oil enrichment index = oil layer structure index * oil layer thickness * permeability * (1-permeability variation coefficient) * oil saturation * (1-oil saturation variation coefficient);

[0061] Among them, when the permeability variation coefficient is greater than or equal to 1, the permeability variation coefficient is 0.99; when the oil saturation variation coefficient is greater than or equal to 1, the oil saturation variation coefficient is 0.99.

[0062] S3: Based on the comprehensive oil enrichment index and the classification threshold of the comprehensive oil enrichment index, the evaluation type of the target reservoir is obtained.

[0063] Specifically, the comprehensive oil-richness index is compared with the comprehensive oil-richness index classification threshold to obtain the evaluation type of the target reservoir, where:

[0064] If the comprehensive oil-bearing enrichment index is ≥1, the target reservoir belongs to Class I reservoir;

[0065] If 0.1≤comprehensive oil-bearing enrichment index<1, the target reservoir belongs to Class II reservoir;

[0066] If the comprehensive oil enrichment index is less than 0.1, the target reservoir belongs to Class III reservoir.

[0067] like Figure 4 As shown, the present invention also provides a quantitative classification and evaluation system for tight sandstone reservoirs, including: a parameter acquisition module, used to obtain the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient of the target oil reservoir; an index calculation module, used to obtain a comprehensive oil enrichment index based on the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient; a reservoir evaluation module, used to obtain the evaluation type of the target oil reservoir based on the comprehensive oil enrichment index combined with the comprehensive oil enrichment index classification threshold.

[0068] The present invention also provides a device, comprising: a memory for storing a computer program; and a processor for implementing the steps of the method for quantitative classification and evaluation of tight sandstone reservoirs when executing the computer program.

[0069] When the processor executes the computer program, the above-mentioned steps of quantitative classification and evaluation of tight sandstone reservoirs are implemented, for example: obtaining the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient of the target oil reservoir; obtaining a comprehensive oil enrichment index based on the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient; based on the comprehensive oil enrichment index, combined with the comprehensive oil enrichment index classification threshold, obtaining the evaluation type of the target oil reservoir.

[0070] Alternatively, the processor implements the functions of each module in the above system when executing the computer program, for example: a parameter acquisition module, used to obtain the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient of the target oil reservoir; an index calculation module, used to obtain a comprehensive oil enrichment index based on the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient; a reservoir evaluation module, used to obtain the evaluation type of the target oil reservoir based on the comprehensive oil enrichment index combined with the comprehensive oil enrichment index classification threshold.

[0071] Exemplarily, the computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments that can complete the preset functions, and the instruction segments are used to describe the execution process of the computer program in the tight sandstone reservoir quantitative classification evaluation device. For example, the computer program can be divided into a parameter acquisition module, an index calculation module and a reservoir evaluation module; the specific functions of each module are as follows: a parameter acquisition module, which is used to obtain the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient of the target oil reservoir; an index calculation module, which is used to obtain a comprehensive oil enrichment index based on the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient; a reservoir evaluation module, which is used to obtain the evaluation type of the target oil reservoir based on the comprehensive oil enrichment index and the classification threshold of the comprehensive oil enrichment index.

[0072] The quantitative classification and evaluation device for tight sandstone reservoirs can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The quantitative classification and evaluation device for tight sandstone reservoirs may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above is an example of a quantitative classification and evaluation device for tight sandstone reservoirs, and does not constitute a limitation on the quantitative classification and evaluation device for tight sandstone reservoirs. It may include more components than the above, or a combination of certain components, or different components. For example, the quantitative classification and evaluation device for tight sandstone reservoirs may also include input and output devices, network access devices, buses, etc.

[0073] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the quantitative classification and evaluation of tight sandstone reservoirs, and uses various interfaces and lines to connect various parts of the entire quantitative classification and evaluation equipment of tight sandstone reservoirs.

[0074] The memory can be used to store the computer program and / or module, and the processor implements various functions of the tight sandstone reservoir quantitative classification and evaluation device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0075] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0076] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for quantitative classification and evaluation of tight sandstone reservoirs are implemented.

[0077] If the modules / units integrated in the tight sandstone reservoir quantitative classification and evaluation system are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0078] Based on such understanding, the present invention implements all or part of the processes in the above-mentioned method for quantitative classification and evaluation of tight sandstone reservoirs, and can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned method for quantitative classification and evaluation of tight sandstone reservoirs can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or preset intermediate form, etc.

[0079] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0080] It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0081] The present invention will be further described below in conjunction with the embodiments and drawings:

[0082] Example

[0083] In order to solve the problem mentioned in the background technology: the existing evaluation method is a quantitative multi-factor quantitative comprehensive evaluation method. Due to the excessive selection of parameters, there are duplications and interferences between the parameters, which affect the evaluation results. For example, there are duplications in the two parameters of oil layer thickness ratio and sandstone thickness ratio. The quality of the oil reservoir is related to the oil layer thickness. No matter how thick the sand layer is, it may not contain oil. Therefore, if the oil layer thickness ratio is selected, it is meaningless to select the sandstone thickness ratio; there are duplications and interferences between the average particle size and the permeability and porosity. The average particle size affects the porosity and permeability. If the permeability and porosity are selected, there is no need to select the average particle size. In addition, the existing evaluation method does not take into account the heterogeneity of reservoir physical properties and oil content, and the heterogeneity has a crucial impact on the reservoir.

[0084] In order to solve the above problems, this embodiment provides a quantitative classification and evaluation method for tight sandstone reservoirs. Figure 1 As shown, specifically:

[0085] Step 1: Select evaluation parameters, count and calculate parameter values

[0086] Through extensive research and exploratory attempts, six key parameters, including oil layer structure index, oil layer thickness, permeability and its coefficient of variation, oil saturation and its coefficient of variation, were selected to innovatively construct a comprehensive oil enrichment index.

[0087] Among them, the oil layer structure index Ha = the maximum single oil layer thickness in the layer ÷ the cumulative oil layer thickness in the layer, dimensionless;

[0088] Oil layer thickness Ho: cumulative oil layer thickness in the layer, in meters (m);

[0089] Permeability K: average permeability of the oil layer section in the layer, data comes from core physical property analysis experiments or well logging interpretation, unit is millidarce (mD);

[0090] Permeability variation coefficient V K : Coefficient of variation of permeability of oil layer segment within the layer, calculated according to the coefficient of variation formula, dimensionless;

[0091] Oil saturation So: oil saturation of the oil layer in the layer, data comes from core physical property analysis experiment or well logging interpretation, unit %;

[0092] Oil saturation variation coefficient Vo: The permeability variation coefficient of the oil layer section within the layer, calculated according to the variation coefficient formula, dimensionless.

[0093] Among them, the specific formula of permeability variation coefficient is:

[0094]

[0095] Among them, V K represents the coefficient of variation of permeability; K irepresents the i-th permeability value, i = 1, 2, 3, ... n; represents the average permeability.

[0096] The specific formula for the oil saturation variation coefficient is:

[0097]

[0098] Among them, V O represents the coefficient of variation of oil saturation; S Oj represents the jth oil saturation value, j = 1, 2, 3, ... m; Represents the average oil saturation.

[0099] Step 2: Construct a comprehensive oil-rich index and calculate the value of the comprehensive oil-rich index.

[0100] Comprehensive oil enrichment index Q = Ha·H o •K.A. K ·S o ·A o , unit: m·mD·%;

[0101] When 1>V K >0:A K =1-V K ; When V K ≥1:A K =0.99;

[0102] When 1>V o >0:A o =1-V o ; When V o ≥1:A o =0.99;

[0103] Among them, A K and V K Related calculation intermediate quantity; A o and V o Related calculation intermediate quantities.

[0104] Step 3: Determine the classification threshold of the comprehensive oil enrichment index:

[0105] Type I oil reservoir: Q≥1; Type II oil reservoir: 0.1≤Q<1; Type III oil reservoir: Q<0.1.

[0106] Step 4: Classification and evaluation of tight sandstone reservoirs:

[0107] Calculate the comprehensive oil-richness index Q value of the target reservoir, and determine the reservoir type based on the Q value of the target reservoir.

[0108] In order to further explain the technical solution of the present invention, the following description is given in combination with practical applications:

[0109] In the first step, based on the comprehensive interpretation results of single well logging and the results of core physical property analysis experiments, CH6 1 1 The maximum single oil layer thickness, cumulative oil layer thickness (i.e. oil layer thickness), permeability and oil saturation of the small layer are statistically analyzed. According to the above calculation formula, the oil layer structure index, permeability variation coefficient and oil saturation variation coefficient are calculated. The specific values ​​are shown in Table 1.

[0110] In the second step, the comprehensive oil content index of the 30 wells was calculated according to the calculation formula. The specific values ​​are shown in Table 1.

[0111] The third step is to determine the CH6 content of a single well according to the range of oil enrichment index values ​​of different types of reservoirs (Type I reservoir: Q ≥ 1, Type II reservoir: 0.1 ≤ Q < 1, Type III reservoir: Q < 0.1). 1 1 The reservoir types of the small layers are evaluated and the results are shown in Table 1.

[0112] Table 1 shows the CH6 1 1 Statistical table of productivity characteristics of different reservoir classifications in small layers

[0113]

[0114] like Figure 2 As shown, for 30 wells CH6 1 1 The daily oil production of the small layer test and the comprehensive oil content index are scattered and statistically analyzed. There is a good positive correlation between the two. Figure 2 As shown in Table 2, the daily oil production of Class I reservoirs is greater than 15t, the daily oil production of Class II reservoirs is between 10 and 20t, and the daily oil production of Class III reservoirs is generally less than 8t. Statistics on the initial daily oil production of a single well after production show that the initial daily oil production of Class I reservoirs after production is greater than 2.5t, the initial daily oil production of Class II reservoirs after production is between 1.2 and 2.5t, and the initial daily oil production of Class III reservoirs after production is generally less than 1.2t (Appendix Table 2). The above statistical analysis shows that the application of the comprehensive index method for reservoir classification can not only achieve quantitative classification, but also be easy to operate, and the results are consistent with the geological characteristics of the reservoir and production practice.

[0115] Table 2 shows the CH6 in JY area of ​​Ordos Basin 1 1 Statistical table of productivity characteristics of different reservoir classifications in small layers

[0116]

[0117] In summary, the present invention provides a quantitative classification evaluation method for tight sandstone reservoirs, which can solve the problem that the existing evaluation methods need to select a large amount of evaluation parameter data, and the evaluation results are not accurate due to the failure to consider the duplication and interference between parameters. The method selects six key parameters, namely, the oil layer structure index, oil layer thickness, permeability and its coefficient of variation, oil saturation and its coefficient of variation, and innovatively constructs a comprehensive oil enrichment index, thereby realizing rapid and accurate evaluation of tight sandstone reservoir types, providing important guidance for the exploration and development of tight sandstone reservoirs, and having the following advantages over the existing technology:

[0118] 1. Through a lot of research and exploration attempts, six key parameters including reservoir structure index, reservoir thickness, permeability and its coefficient of variation, oil saturation and its coefficient of variation were selected for reservoir classification evaluation. Few parameters were selected but with obvious geological significance. Reservoir thickness, permeability and oil saturation are the key to reservoir quality. The vertical structural characteristics of the reservoir, permeability heterogeneity and oil heterogeneity were taken into consideration, and the parameter selection was highly targeted.

[0119] 2. By constructing a comprehensive oil-richness index to classify and evaluate reservoirs, it not only solves the problem of quantitative classification evaluation, but also solves the problem that the evaluation results are difficult to determine when different parameters are inconsistent. In the index, oil layer thickness, permeability, and oil saturation are in a multiplicative relationship. When the three are poor, the index value is small, and when the three are good, the index value is large. When some are poor and some are good, the index value is medium. The product of the three can complement each other, avoiding the error or difficulty in determining the type of reservoir simply based on the quality of the three.

[0120] 3. This method is simple and practical to operate, and can realize rapid quantitative classification and evaluation of tight sandstone reservoirs. The evaluation results can effectively guide the optimization of exploration and development targets and the formulation of targeted technical policies for tight sandstone reservoirs.

[0121] The above embodiment is only one of the implementation methods that can realize the technical solution of the present invention. The scope of protection claimed by the present invention is not limited only to this embodiment, but also includes changes, replacements and other implementation methods that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention.

Claims

1. A quantitative classification and evaluation method for tight sandstone reservoirs, characterized in that: include: Obtain the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient of the target oil reservoir; Based on the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient, the comprehensive oil enrichment index is obtained; Based on the comprehensive oil enrichment index and combined with the classification threshold of the comprehensive oil enrichment index, the evaluation type of the target oil reservoir is obtained.

2. A method for quantitative classification and evaluation of tight sandstone reservoirs according to claim 1, characterized in that: in, The step of obtaining the oil layer structure index comprises: Obtaining the maximum single-layer oil layer thickness and the oil layer thickness, and obtaining the oil layer structure index based on the maximum single-layer oil layer thickness and the oil layer thickness; Among them, oil layer structure index = maximum single oil layer thickness ÷ oil layer thickness.

3. A method for quantitative classification and evaluation of tight sandstone reservoirs according to claim 1, characterized in that: in, The specific formula of permeability variation coefficient is: Among them, V K represents the coefficient of variation of permeability; K i represents the i-th permeability value, i = 1, 2, 3, ... n; represents the average permeability.

4. A method for quantitative classification and evaluation of tight sandstone reservoirs according to claim 1, characterized in that: in, The specific formula for the oil saturation variation coefficient is: Among them, V O represents the coefficient of variation of oil saturation; S Oj represents the jth oil saturation value, j = 1, 2, 3, ... m; Represents the average oil saturation.

5. The method for quantitative classification and evaluation of tight sandstone reservoirs according to claim 1, characterized in that: in, Permeability and oil saturation are obtained through core physical property analysis experiments or well logging interpretation.

6. A method for quantitative classification and evaluation of tight sandstone reservoirs according to claim 1, characterized in that: The calculation formula of comprehensive oil enrichment index is: Comprehensive oil enrichment index = oil layer structure index * oil layer thickness * permeability * (1-permeability variation coefficient) * oil saturation * (1-oil saturation variation coefficient).

7. A method for quantitative classification and evaluation of tight sandstone reservoirs according to claim 1, characterized in that: The comprehensive oil-richness index is compared with the comprehensive oil-richness index classification threshold to obtain the evaluation type of the target reservoir, where: If the comprehensive oil-bearing enrichment index is ≥1, the target reservoir belongs to Class I reservoir; If 0.1≤comprehensive oil-bearing enrichment index<1, the target reservoir belongs to Class II reservoir; If the comprehensive oil enrichment index is less than 0.1, the target reservoir belongs to Class III reservoir.

8. A system for quantitative classification and evaluation of tight sandstone reservoirs, used to implement the steps of the method for quantitative classification and evaluation of tight sandstone reservoirs according to any one of claims 1 to 7, characterized in that: include: A parameter acquisition module is used to obtain the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient of the target oil reservoir; An index calculation module, used to obtain a comprehensive oil-rich index based on the oil layer structure index, oil layer thickness, permeability, permeability variation coefficient, oil saturation and oil saturation variation coefficient; The oil reservoir evaluation module is used to obtain the evaluation type of the target oil reservoir based on the comprehensive oil enrichment index combined with the comprehensive oil enrichment index classification threshold.

9. A device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the method for quantitative classification and evaluation of tight sandstone reservoirs according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it is used to implement the steps of the method for quantitative classification and evaluation of tight sandstone reservoirs according to any one of claims 1 to 7.

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