A resource evaluation method based on prior solution of geological Pareto parameters

Abstract

The present invention provides a resource evaluation method based on a priori solution of geological Pareto parameters. The method adopts a geological Pareto distribution model to fit the scale distribution of oil and gas reservoirs discovered before each time node in the evaluation unit to obtain the a priori distribution parameters of the geological Pareto distribution model. According to the a priori distribution parameters of the geological Pareto distribution model, the variation relationship between the distribution parameters of the geological Pareto distribution model and the number of oil and gas reservoirs is obtained, and then the oil and gas reservoir scale distribution model based on the final total number of oil and gas reservoirs in the evaluation unit is obtained. According to the oil and gas reservoir scale distribution model based on the final total number of oil and gas reservoirs in the evaluation unit, the scale distribution of oil and gas reservoirs in the evaluation unit is predicted, which reflects the distribution of oil and gas reservoirs in different oil and gas reservoir scale intervals and is convenient for researchers to further carry out economic benefit evaluation.

Description

Technical Field

[0001] The invention belongs to the field of oil and gas exploration, and in particular relates to a resource evaluation method based on a priori solution of geological Pareto parameters. Background Art

[0002] Oil and gas resource evaluation is a part of the decision-making science of oil and gas exploration and development. Its purpose is to use appropriate resource evaluation methods to evaluate different evaluation units at different exploration stages, and to study the existence characteristics, distribution status, scale probability and sequence of oil and gas resources in the evaluation units according to the needs of different exploration purposes. How to scientifically predict the resource scale and oil and gas reservoir distribution characteristics of the evaluation unit, the oil and gas resource evaluation method is its core and focus. With the progress of exploration theory, oil and gas resource evaluation methods have also developed rapidly, and a series of oil and gas resource evaluation methods have emerged. Some commonly used evaluation methods include: analogy method, volume method, oil and gas reservoir scale series method, exploration efficiency trend method, USGS seventh approximation method, etc. In general, these evaluation methods have promoted the widespread development of oil and gas resource evaluation work and enriched the diversity of oil and gas resource evaluation methods.

[0003] Analyzing the above-mentioned evaluation methods, their common feature is to answer the amount of resources (resource potential) of an evaluation unit, and their different features are that the principles and applicable exploration stages of these methods are different. For example, the analogy method is mainly used in the early stage of exploration, and its results are only used as a reference for exploration decisions by comparing with similar high-exploration areas; the volume method is mainly used for closure evaluation, and for zones (or oil and gas accumulation systems), it is only an estimate of the amount of resources; the oil and gas reservoir scale series method and the USGS seventh approximation method are both used in the early stage of exploration, when a small number of oil and gas reservoirs have been discovered.

[0004] The common research purpose of these methods is to answer the question of oil and gas resource potential, that is, how much oil and gas there is, in the early stage of oil and gas exploration, so as to provide reference information for early exploration decisions.

[0005] The patent with publication number CN103278866A also discloses a method for evaluating resource potential, which does not reflect the distribution of oil and gas reservoirs in different scale ranges.

[0006] However, as exploration progresses, the potential of oil and gas resources in a region is increasingly recognized, and the amount of resources is gradually becoming clearer. In particular, for the evaluation of oil and gas resources in highly mature exploration areas (zones or oil and gas reservoir systems), researchers are not limited to predicting the amount of resources in the evaluation unit, but also need to understand how many oil and gas reservoirs there are in the evaluation area and the distribution of oil and gas reservoirs in different scale intervals, so as to further carry out economic benefit evaluation.

[0007] At this point, decision makers are no longer limited to the specific amount of resources, but are concerned about how many oil and gas reservoirs are yet to be discovered? What are the scales of these oil and gas reservoirs? How much work is needed to discover these oil and gas reservoirs? How much is the exploration cost? Is it economically worthwhile to further explore? Therefore, on the basis of answering the resource potential, it is necessary to further effectively predict the scale distribution of oil and gas reservoirs in the evaluation unit. Summary of the invention

[0008] In order to solve the technical problems in the prior art, the present invention provides a resource evaluation method based on a priori solution of geological Pareto parameters, and the specific scheme is as follows:

[0009] A resource evaluation method based on a priori solution of geological Pareto parameters includes the following steps:

[0010] S1: Statistics on the oil and gas reservoirs discovered in the evaluation unit of the study area up to the current time node;

[0011] S2: using a geological Pareto distribution model to fit the scale distribution of oil and gas reservoirs discovered before each time point in the evaluation unit to obtain a priori distribution parameters of the geological Pareto distribution model;

[0012] S3: obtaining distribution parameters of a geological Pareto distribution model based on the final total number of oil and gas reservoirs in the evaluation unit according to the prior distribution parameters, thereby obtaining an oil and gas reservoir scale distribution model based on the final total number of oil and gas reservoirs in the evaluation unit;

[0013] S4: predicting the distribution of oil and gas reservoir sizes within the evaluation unit according to an oil and gas reservoir size distribution model based on the final total number of oil and gas reservoirs within the evaluation unit.

[0014] Furthermore, in step S3, fitting sample points are obtained and fitted according to the prior distribution parameters and the number of oil and gas reservoirs discovered before the corresponding time node, so as to obtain the changing relationship between the distribution parameters of the geological Pareto distribution model and the number of oil and gas reservoirs, and then obtain the distribution parameters of the geological Pareto distribution model based on the final total number of oil and gas reservoirs in the evaluation unit.

[0015] Furthermore, in step S3, the final total number of oil and gas reservoirs in the evaluation unit is obtained according to the following method:

[0016] Step S31: establishing a total number distribution model of oil and gas reservoirs in the evaluation unit according to the number of oil and gas reservoirs discovered in the evaluation unit up to the current time node;

[0017] Step S32: estimating the final total number of oil and gas reservoirs in the evaluation unit according to the total number distribution model of oil and gas reservoirs in the evaluation unit.

[0018] Furthermore, in step S32, a group of total numbers of oil and gas reservoirs is randomly sampled according to the total number distribution model of oil and gas reservoirs, and the average value of the total number of oil and gas reservoirs in this group is used as the final total number of oil and gas reservoirs in the evaluation unit.

[0019] Furthermore, in step S31, the total number distribution model of oil and gas reservoirs is obtained according to the following method:

[0020] Obtaining the minimum total number of oil and gas reservoirs, the most likely total number of oil and gas reservoirs, and the maximum total number of oil and gas reservoirs in the evaluation unit according to the number of oil and gas reservoirs discovered in the evaluation unit up to the current time node;

[0021] According to the minimum total number of oil and gas reservoirs, the most likely total number of oil and gas reservoirs and the maximum total number of oil and gas reservoirs in the evaluation unit, a distribution model of the total number of oil and gas reservoirs in the evaluation unit is established by using triangular distribution.

[0022] Furthermore, the minimum total number of oil and gas reservoirs in the evaluation unit is the number of oil and gas reservoirs discovered in the evaluation unit up to the current time node;

[0023] The maximum total number of oil and gas reservoirs in the evaluation unit is the product of the most likely total number of oil and gas reservoirs and a coefficient greater than 1;

[0024] The total number of the most likely oil and gas reservoirs in the evaluation unit is the product of the number of trap clues obtained by seismic interpretation and the current exploration success rate; or

[0025] The product of the density of discovered oil and gas reservoirs in a highly explored area with similar geological conditions and the area of ​​the evaluation unit.

[0026] Furthermore, in step S1, the oil and gas reservoirs discovered in the evaluation unit up to the current time node are counted according to the oil and gas reservoir reserve unit merging process;

[0027] The steps of the oil and gas reservoir reserve unit merging process include:

[0028] S11: Based on the range of the evaluation unit, determine the oil and gas fields within the range of the evaluation unit;

[0029] S12: According to the oil and gas fields to which they belong, count the oil and gas reservoir reserve units discovered up to the current time node in the vertical direction according to different layers;

[0030] S13: further merging oil and gas reservoir reserve units according to the types of oil and gas reservoirs;

[0031] S14: Based on the merging result of the oil and gas reservoir reserve units and in combination with the scope of the evaluation unit, the oil and gas reservoirs discovered in the merged evaluation unit up to the current time node are formed.

[0032] Furthermore, in step S13, the types of oil and gas reservoirs include: structural oil and gas reservoirs, lithological oil and gas reservoirs, and structural-lithological oil and gas reservoirs.

[0033] Furthermore, the distribution parameters of the geological Pare distribution model include morphological parameters and position parameters.

[0034] Furthermore, the resource evaluation method also includes a verification step between step S1 and step S2, wherein the verification step verifies whether the size distribution of the oil and gas reservoirs discovered in the evaluation unit up to the current time node conforms to the geological Pareto distribution.

[0035] Compared with the prior art, the present invention provides a resource evaluation method based on a priori solution of geological Pareto parameters. The method adopts a geological Pareto distribution model to fit the scale distribution of oil and gas reservoirs discovered before each time node in the evaluation unit to obtain the a priori distribution parameters of the geological Pareto distribution model. According to the a priori distribution parameters of the geological Pareto distribution model, the changing relationship between the distribution parameters of the geological Pareto distribution model and the number of oil and gas reservoirs is obtained, and then the oil and gas reservoir scale distribution model based on the final total number of oil and gas reservoirs in the evaluation unit is obtained. According to the oil and gas reservoir scale distribution model based on the final total number of oil and gas reservoirs in the evaluation unit, the scale distribution of oil and gas reservoirs in the evaluation unit is predicted, which reflects the distribution of oil and gas reservoirs in different oil and gas reservoir scale ranges, and is convenient for researchers to further carry out economic benefit evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be described in more detail below based on embodiments and with reference to the accompanying drawings, wherein:

[0037] Figure 1 A resource evaluation flow chart based on a priori solution of geological Pareto parameters in an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of using a probit diagram to examine the size distribution of oil and gas reservoirs in an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of a distribution model of the total number of oil and gas reservoirs in an evaluation unit in an embodiment of the present invention;

[0040] Figure 4 This is a schematic diagram of the variation trend of the morphological parameter lamda of the geological Pareto distribution model as the number of oil and gas reservoirs increases in an embodiment of the present invention;

[0041] Figure 5 This is a schematic diagram of the variation trend of the location parameter gamma of the geological Pareto distribution model as the number of oil and gas reservoirs increases in an embodiment of the present invention;

[0042] Figure 6 The diagram is a schematic diagram of the scale structure of oil and gas reservoirs predicted based on the geological Pareto distribution model in a certain evaluation unit in an embodiment of the present invention.

[0043] In the drawings, identical objects are denoted by identical reference numerals, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0044] The present invention will be further described below in conjunction with the accompanying drawings.

[0045] The present invention is applicable to mature areas with medium or high exploration levels, and the classification of high and low exploration levels is common knowledge in the art.

[0046] The "oil and gas reservoirs discovered up to the current time point" in the present invention refers to the oil and gas reservoirs discovered up to the start of the evaluation of the evaluation unit.

[0047] In the present invention, within the evaluation unit, the single value probability of “the most likely total number of oil and gas reservoirs” is the largest, such as Figure 3 The total number of oil and gas reservoirs corresponding to the maximum vertical coordinate value shown in the figure. The single-value probability is different from the discovery probability. The discovery probability is the accumulation of the single-value probability. The discovery probability of the minimum total number of oil and gas reservoirs in the evaluation unit is 100%, that is, the probability that the total number of oil and gas reservoirs in the evaluation unit is greater than the minimum total number of oil and gas reservoirs is 100%. Specifically, Figure 3 As shown, the ordinate corresponding to the horizontal axis 92 indicates the probability (single-value probability) that the total number of oil and gas reservoirs in the evaluation unit may be 92, the cumulative probability corresponding to the horizontal axis 92 is the probability that the total number of oil and gas reservoirs in the evaluation unit is less than or equal to 92, and the probability of discovery corresponding to the horizontal axis 92 is the probability obtained by subtracting the probability that the total number of oil and gas reservoirs in the evaluation unit is less than or equal to 92 from 1. For the minimum total number of oil and gas reservoirs in the evaluation unit, its cumulative probability is the probability of being less than or equal to the minimum total number of oil and gas reservoirs, that is, 0%, and the probability of discovery is 1-0%=100%; for the maximum total number of oil and gas reservoirs in the evaluation unit, its cumulative probability is the probability of being less than or equal to the maximum total number of oil and gas reservoirs, that is, 100%, and the probability of discovery is 1-100%=0%; for the most likely total number of oil and gas reservoirs in the evaluation unit, a value is assigned according to the actual situation, usually 50%.

[0048] 1. Evaluation unit division and sample data arrangement

[0049] A zone is between a basin and a trap, and refers to a unified combination of a series of traps and discovered oil and gas reservoirs (fields) in the same structural belt of a basin, which have the same genetic connection and the laws of oil and gas generation, migration and accumulation and are geographically adjacent. The division of zones usually adopts the method of "division in plane and stratification in vertical direction", and the key geological elements considered include reservoirs, regional caprocks, oil and gas filling conditions, trajectories and the effective configuration relationship of the above four elements. The divided zones are evaluation units.

[0050] Based on the divided evaluation units (i.e. zones), the discovered oil and gas reservoirs and the reserves of each oil and gas reservoir in the evaluation unit up to the current time node are counted according to the merging principle of the oil and gas reservoir reserve unit, and finally the discovered oil and gas reservoirs in the evaluation unit up to the current time node, i.e., the discovered oil and gas reservoir samples in the evaluation unit up to the current time node, are formed. The discovered oil and gas reservoir samples up to the current time node are reflected in the form of a data table, as shown in Table 1. The scale and discovery time of each oil and gas reservoir need to be given in the statistical data table, or they can be arranged in chronological order according to the sequence number.

[0051] When merging oil and gas reservoir reserve units, the following process needs to be followed:

[0052] 1) Based on the scope of the evaluation unit, determine the oil and gas fields within the scope of the evaluation unit;

[0053] 2) According to the oil and gas fields to which they belong, count the oil and gas reservoir reserve units discovered up to the current time point in the vertical direction according to different layers;

[0054] 3) For the oil and gas reservoir reserve units discovered in different strata up to the current time node, further merge the oil and gas reservoir reserve units according to the types of structural oil and gas reservoirs, lithological oil and gas reservoirs, structural-lithological oil and gas reservoirs, etc.;

[0055] 4) Based on the merging results of the oil and gas reservoir reserve units and in combination with the scope of the evaluation unit (plane partitioning, vertical stratification), the oil and gas reservoirs discovered in the merged evaluation unit up to the current time node are formed.

[0056] The small reserve units of oil and gas reservoirs are usually obtained by statistics in step 2) of the oil and gas reservoir merging process. These oil and gas reservoir reserve units are not real oil and gas reservoirs and need to be further merged into oil and gas reservoirs.

[0057] Table 1 Statistics of oil and gas reservoir data of evaluation unit

[0058]

[0059] II. Analysis of samples of discovered oil and gas reservoirs in the evaluation unit (testing steps)

[0060] The oil and gas reservoirs discovered in the evaluation unit up to the current time node are samples of the oil and gas reservoirs discovered in the evaluation unit up to the current time node. The oil and gas reservoirs discovered in the evaluation unit up to the current time node are the basic research data for predicting the scale structure of the oil and gas reservoirs in the evaluation unit. The purpose of statistics on the oil and gas reservoirs discovered in the evaluation unit up to the current time node is to study the distribution law of the scale of the discovered oil and gas reservoirs, and to further analyze the overall distribution of the scale of the oil and gas reservoirs in the evaluation unit through the distribution law of the scale of the discovered oil and gas reservoirs. Usually, the distribution model of the scale of oil and gas reservoirs conforms to the geological Pareto distribution model.

[0061] Based on the discovered oil and gas reservoir samples in the evaluation unit up to the current time node, the Probit chart can be used to test when selecting a matching probability distribution model. The Probit chart can be used to intuitively determine whether the distribution is suitable for the discovered oil and gas reservoir samples in the evaluation unit.

[0062] Probit chart: The Probit chart is a graph drawn based on the relationship between the cumulative proportion of the variable and the cumulative proportion of the specified distribution (geological Pareto distribution). The Probit chart can be used to test whether the data conforms to the specified distribution. Test method: The fitted dotted line can be used to test whether the data (the scale of the discovered oil and gas reservoirs) conforms to the specified distribution. When the data (the scale of the discovered oil and gas reservoirs) conforms to the specified distribution, the distribution curve (the dotted line in the figure) fits the sample points better, such as Figure 2 shown. Figure 2 The X-axis (abscissa) is the logarithmic coordinate value of the scale of discovered oil and gas reservoirs; the Y-axis (ordinate) is the cumulative proportion of the scale of discovered oil and gas reservoirs. Among the cumulative proportions on both sides of the figure, the cumulative proportion on one side is the cumulative proportion greater than or equal to the scale of a certain oil and gas reservoir, and the cumulative proportion on the other side is the cumulative proportion less than or equal to the scale of a certain oil and gas reservoir. The dotted line is a graph drawn from the relationship between the cumulative proportion of the scale of discovered oil and gas reservoirs in a certain evaluation unit and the cumulative proportion of the specified distribution (geological Pareto distribution), and the solid line is a graph drawn from the relationship between the cumulative proportion of the scale of oil and gas discovered in a certain evaluation unit and the cumulative proportion of the log-normal distribution.

[0063] The chi-square test can also be used to test whether the size of the discovered oil and gas reservoirs conforms to the specified distribution.

[0064] 3. Establishment of the distribution model of oil and gas reservoirs in the evaluation unit

[0065] The total number of oil and gas reservoirs in the evaluation unit includes two parts: one part is the number of oil and gas reservoirs that have been discovered through drilled wells based on oil and gas exploration; the other part is the exploration targets that objectively exist in the evaluation unit but have not been found or confirmed due to various reasons such as exploration technology, exploration knowledge or exploration workload, and need to continue to be implemented in subsequent exploration.

[0066] Among them, the number of oil and gas reservoirs discovered in the evaluation unit up to the current time node should be the minimum total number of oil and gas reservoirs in the evaluation unit, and it has been confirmed, with a discovery probability of 100%. To estimate the final total number of oil and gas reservoirs in the evaluation unit, it is necessary to establish a distribution model for the total number of oil and gas reservoirs in the evaluation unit. The following methods can be used to establish a distribution model for the total number of oil and gas reservoirs in the evaluation unit:

[0067] 1) Minimum total number of oil and gas reservoirs in the evaluation unit

[0068] The number of oil and gas reservoirs discovered in the evaluation unit up to the current time node is used as the minimum total number of oil and gas reservoirs in the total number distribution model of the evaluation unit, and the discovery probability is 100%;

[0069] 2) The total number of the most likely oil and gas reservoirs in the evaluation unit

[0070] The total number of the most likely oil and gas reservoirs in the evaluation unit can be determined by a variety of techniques. For example, the number of closure clues obtained based on seismic interpretation in the evaluation unit is counted, and the number of closure clues is counted, combined with the current exploration success rate, and the number obtained by multiplying the two is used as the total number of the most likely oil and gas reservoirs in the evaluation unit. It is also possible to refer to other high-exploration areas with similar geological conditions, based on the number density of discovered oil and gas reservoirs in high-exploration areas, combined with the area of ​​the local area, and the number obtained by multiplying the two is used as the total number of the most likely oil and gas reservoirs in the evaluation unit. The probability of discovery of the most likely total number of oil and gas reservoirs in the evaluation unit is assigned to 50%.

[0071] 3) The maximum total number of oil and gas reservoirs in the evaluation unit

[0072] For the maximum total number of oil and gas reservoirs in the evaluation unit, the maximum total number of oil and gas reservoirs in the evaluation unit can be obtained by multiplying the most likely total number of oil and gas reservoirs in the evaluation unit by a coefficient greater than 1. For areas with medium exploration degree, this coefficient is usually selected as 2.

[0073] 4) Establishment of the total number distribution model of oil and gas reservoirs in the evaluation unit

[0074] Based on the minimum total number of oil and gas reservoirs, the most likely total number of oil and gas reservoirs, and the maximum total number of oil and gas reservoirs in the evaluation unit, their discovery probabilities were assigned as 100%, 50%, and 0%, respectively, and a distribution model of the total number of oil and gas reservoirs in the evaluation unit was constructed using triangular distribution.

[0075] According to the total number distribution model of oil and gas reservoirs in the evaluation unit, a group of total number of oil and gas reservoirs is randomly sampled, and the average value of the total number of oil and gas reservoirs in this group is taken as the final total number of oil and gas reservoirs in the evaluation unit.

[0076] like Figure 3 As shown in FIG. 1 , it is a distribution model of the total number of oil and gas reservoirs in a certain evaluation unit. The X-axis (abscissa) is the total number of oil and gas reservoirs in the evaluation unit; the Y-axis (ordinate) is the single-value probability distribution corresponding to the total number of oil and gas reservoirs in the evaluation unit; and the scattered points are the total number of oil and gas reservoirs obtained by sampling.

[0077] 4. Establishment of the overall oil and gas reservoir scale distribution model for the evaluation unit

[0078] Since a series of oil and gas reservoir samples have been discovered in the evaluation unit, the geological Pareto distribution parameters can be determined based on a priori methods, and further based on the a priori distribution parameters, the overall oil and gas reservoir scale geological Pareto distribution model of the evaluation unit can be established.

[0079] Oil and gas exploration is a gradual process, and the discovery of oil and gas reservoirs also has a time series. In response to the continuous discovery of oil and gas reservoirs, the geological Pareto distribution model is used to fit the scale distribution of oil and gas reservoirs, and a series of distribution parameters of the geological Pareto distribution model can be obtained. For example, as of the current time node, there are 107 oil and gas reservoirs discovered. The geological Pareto distribution model is used to fit the scale distribution of these 107 oil and gas reservoirs to obtain the distribution parameters of the corresponding geological Pareto distribution model; these 107 oil and gas reservoirs are arranged in chronological order. Assuming that the number of oil and gas reservoirs discovered before a certain time node is 30, the geological Pareto distribution model is used to fit the scale distribution of these 30 oil and gas reservoirs to obtain the distribution parameters of the corresponding geological Pareto distribution model; with the passage of time, the number of discovered oil and gas reservoirs continues to increase. Assuming that the number of oil and gas reservoirs discovered before a certain time node is 40, the geological Pareto distribution model is used to fit the scale distribution of these 40 oil and gas reservoirs to obtain the distribution parameters of the corresponding geological Pareto distribution model. Similarly, the geological Pareto distribution model is used to fit the scale distribution of oil and gas reservoirs discovered before each time node, and a series of distribution parameters of the geological Pareto distribution model are obtained, which are the prior distribution parameters of the geological Pareto distribution model. The distribution parameters of the geological Pareto distribution model include morphological parameters and position parameters. The morphological parameters are usually recorded as lamda, and the position parameters are usually recorded as gamma.

[0080] According to the prior distribution parameters of the geological Pareto distribution model and the number of oil and gas reservoirs discovered before the corresponding time node, fitting sample points are obtained and fitted to obtain the change trend between the distribution parameters (morphological parameters and location parameters) of the geological Pareto distribution model and the number of oil and gas reservoirs. Figure 4 The figure shows the relationship between the morphological parameter lamda of the geological Pareto distribution model of the scale of oil and gas reservoirs in a certain evaluation unit and the number of oil and gas reservoirs; Figure 5 The figure shows the relationship between the location parameter gamma of the geological Pareto distribution model of the size of oil and gas reservoirs in a certain evaluation unit and the number of oil and gas reservoirs. Figure 4 , 5 Where α is the exploration maturity, which is the ratio of the number of discovered reservoirs or the number of reservoirs presumed to be discovered in the future to the total number of estimated final reservoirs in the evaluation unit. (N:89) means that the total number of estimated final reservoirs in the evaluation unit is 89.

[0081] According to the variation relationship between the distribution parameters (morphological parameters and location parameters) of the geological Pareto distribution model and the number of oil and gas reservoirs, the distribution parameters (morphological parameters and location parameters) of the geological Pareto distribution model corresponding to different numbers of oil and gas reservoirs can be obtained, thereby obtaining the geological Pareto distribution model corresponding to different numbers of oil and gas reservoirs, that is, the scale distribution model of the oil and gas reservoirs. Combined with the estimated total number of oil and gas reservoirs in the evaluation unit and the variation relationship between the distribution parameters (morphological parameters and location parameters) of the geological Pareto distribution model and the number of oil and gas reservoirs, the distribution parameters of the geological Pareto distribution model based on the final total number of oil and gas reservoirs in the evaluation unit are obtained, thereby obtaining the geological Pareto distribution model based on the final total number of oil and gas reservoirs in the evaluation unit, that is, the scale distribution model of the oil and gas reservoirs based on the final total number of oil and gas reservoirs in the evaluation unit.

[0082] V. Resource Prediction and Scale Structure Analysis

[0083] According to the final total number of oil and gas reservoirs in the evaluation unit and the oil and gas reservoir scale distribution model based on the final total number of oil and gas reservoirs in the evaluation unit, the evaluation unit is predicted using Monte Carlo simulation, which can not only predict the resource potential of the evaluation unit but also predict the oil and gas reservoir scale structure of the evaluation unit. The resource potential is characterized by the resource amount, and the oil and gas reservoir scale structure is characterized by the number of oil and gas reservoirs in different oil and gas reservoir scale intervals.

[0084] Figure 6 It shows the scale structure of oil and gas reservoirs predicted based on the geological Pareto distribution model within a certain evaluation unit. The darker columns represent the number of discovered oil and gas reservoirs within a certain oil and gas reservoir scale interval, and the lighter columns represent the total number of predicted oil and gas reservoirs within a certain oil and gas reservoir scale interval. Table 2 shows the specific data.

[0085] Table 2 Prediction of the number of oil and gas reservoirs in different oil and gas reservoir size ranges

[0086]

[0087] Among them, there are 38 undiscovered reservoirs with 100,000-500,000 tons, 14 with 500,000-1 million tons, 2 with 1 million-2 million tons, 2 with 5 million-10 million tons, and 1 with 10 million-20 million tons. The sum of the predicted total number of reservoirs in each reservoir size range shown in Table 2 is the final total number of reservoirs in the evaluation unit.

[0088] Taking a certain evaluation unit in a certain bay basin as an example, the application and process of the resource evaluation method based on the prior solution of geological Pareto parameters are further explained.

[0089] 1. Evaluation unit division and sample data arrangement

[0090] Based on the exploration results of a certain evaluation unit in a certain bay basin, statistics were made on the annual discoveries of oil and gas reservoirs. As of the current time point, 107 oil and gas reservoirs have been discovered, as shown in Table 3.

[0091] Table 3 Reservoir data

[0092]

[0093]

[0094] II. Analysis of samples of discovered reservoirs in the evaluation unit

[0095] Based on the 107 oil and gas reservoir samples discovered by the evaluation unit up to the current time node, statistical analysis shows that the minimum value of the discovered oil and gas reservoir is 170,000 tons, and the maximum oil and gas reservoir scale is 27.76 million tons. Based on the geological Pareto distribution model, the scale distribution of these 107 oil and gas reservoirs is fitted, and its location parameter is 20.1 and the morphological parameter is 0.84.

[0096] Based on the geological Pareto distribution model and using the probit chart, it can be determined that the scale of discovered oil and gas reservoirs follows the geological Pareto distribution.

[0097] 3. Establishment of the distribution model of the total number of oil and gas reservoirs in the evaluation unit

[0098] 1) Minimum total number of oil and gas reservoirs in the evaluation unit

[0099] The 107 oil and gas reservoirs discovered in the evaluation unit up to the current time node are used as the minimum total number of oil and gas reservoirs in the total number distribution model of the evaluation unit, and the discovery probability is 100%;

[0100] 2) The total number of the most likely oil and gas reservoirs in the evaluation unit

[0101] The total number of the most likely oil and gas reservoirs in the evaluation unit can be determined by a variety of techniques. For example, the number of closure clues obtained based on seismic interpretation is counted, and the number of closure clues is counted, combined with the current exploration success rate, and the number obtained by multiplying the two is used as the total number of the most likely oil and gas reservoirs in the evaluation unit. It is also possible to refer to other high-exploration areas with similar geological conditions, based on the number density of discovered oil and gas reservoirs in high-exploration areas, combined with the area of ​​the local area, and the number obtained by multiplying the two is used as the total number of the most likely oil and gas reservoirs in the evaluation unit. The probability of discovery of the most likely total number of oil and gas reservoirs in the evaluation unit is assigned to 50%.

[0102] In this study, based on the density of oil and gas reservoirs in similar areas and the exploration area of ​​the evaluation unit, the total number of the most likely oil and gas reservoirs in the evaluation unit was 165.

[0103] 3) The maximum total number of oil and gas reservoirs in the evaluation unit

[0104] The maximum total number of oil and gas reservoirs in the evaluation unit can be obtained by multiplying the total number of the most likely oil and gas reservoirs by a coefficient greater than 1. Since the exploration degree of this evaluation unit is medium to high, the selection coefficient is 1.5, and the maximum total number of oil and gas reservoirs in the evaluation unit is 247.

[0105] 4) Establishment of the total number distribution model of oil and gas reservoirs in the evaluation unit

[0106] Based on the minimum total number of oil and gas reservoirs in the evaluation unit of 107, the most likely total number of oil and gas reservoirs of 165, and the maximum total number of oil and gas reservoirs of 247, their discovery probabilities are assigned as 100%, 50%, and 0%, respectively. A distribution model of the total number of oil and gas reservoirs in the evaluation unit can be constructed using triangular distribution.

[0107] According to the total number distribution model of oil and gas reservoirs in the evaluation unit, a group of total number of oil and gas reservoirs is randomly sampled, and the average value of the total number of oil and gas reservoirs in this group is taken as the final total number of oil and gas reservoirs in the evaluation unit.

[0108] 4. Establishment of the overall oil and gas reservoir scale distribution model for the evaluation unit

[0109] According to the method for establishing the overall oil and gas reservoir scale distribution model of the aforementioned evaluation unit in this embodiment, the distribution parameters of the geological Pareto distribution model (i.e., the oil and gas reservoir scale distribution model) based on the final total number of oil and gas reservoirs in the evaluation unit are obtained, wherein the position parameter is 10.34 and the morphological parameter is 0.91, thereby establishing the oil and gas reservoir scale distribution model based on the final total number of oil and gas reservoirs in the evaluation unit.

[0110] V. Resource Prediction and Scale Structure Analysis

[0111] According to the final total number of oil and gas reservoirs in the evaluation unit and the oil and gas reservoir scale distribution model based on the final total number of oil and gas reservoirs in the evaluation unit, the resource potential and oil and gas reservoir scale structure in the evaluation unit are studied by using Monte Carlo simulation, which can predict the oil and gas reservoir scale structure in the evaluation unit and the resource potential in the evaluation unit.

[0112] The reservoir scale structure is characterized by the number of reservoirs in different reservoir scale intervals.

[0113] The resource potential is characterized by the resource volume, and the total resource volume of the evaluation unit is predicted to be 290 million tons.

[0114] Although the present invention has been described with reference to preferred embodiments, various modifications may be made thereto and some or all of the technical features may be replaced by equivalents without departing from the scope of the present invention. In particular, the various technical features mentioned in the various embodiments may be combined in any manner as long as there is no logical or structural conflict. The present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A resource evaluation method based on a priori solution of geological Pareto parameters, characterized in that: The following steps are involved: S1: Statistics on the oil and gas reservoirs discovered in the evaluation unit of the study area up to the current time node; S2: using a geological Pareto distribution model to fit the scale distribution of oil and gas reservoirs discovered before each time point in the evaluation unit to obtain a priori distribution parameters of the geological Pareto distribution model; S3: according to the prior distribution parameters and the number of oil and gas reservoirs discovered before the corresponding time node, fitting sample points are obtained and fitted to obtain the variation relationship between the distribution parameters of the geological Pareto distribution model and the number of oil and gas reservoirs; the total number distribution model of oil and gas reservoirs in the evaluation unit is constructed, the final total number of oil and gas reservoirs in the evaluation unit is estimated according to the total number distribution model of oil and gas reservoirs in the evaluation unit, and the distribution parameters of the geological Pareto distribution model based on the final total number of oil and gas reservoirs in the evaluation unit are combined with the variation relationship between the distribution parameters of the geological Pareto distribution model and the number of oil and gas reservoirs, so as to obtain the oil and gas reservoir scale distribution model based on the final total number of oil and gas reservoirs in the evaluation unit; S4: predicting the distribution of oil and gas reservoir sizes within the evaluation unit according to an oil and gas reservoir size distribution model based on the final total number of oil and gas reservoirs within the evaluation unit.

2. The resource evaluation method according to claim 1, characterized in that: In step S3, a group of total numbers of oil and gas reservoirs is randomly sampled according to the total number distribution model of oil and gas reservoirs, and the average value of the total number of oil and gas reservoirs in this group is used as the final total number of oil and gas reservoirs in the evaluation unit.

3. The resource evaluation method according to claim 1, characterized in that: In step S3, the total number distribution model of oil and gas reservoirs is obtained according to the following method: Obtaining the minimum total number of oil and gas reservoirs, the most likely total number of oil and gas reservoirs, and the maximum total number of oil and gas reservoirs in the evaluation unit according to the number of oil and gas reservoirs discovered in the evaluation unit up to the current time node; According to the minimum total number of oil and gas reservoirs, the most likely total number of oil and gas reservoirs and the maximum total number of oil and gas reservoirs in the evaluation unit, a distribution model of the total number of oil and gas reservoirs in the evaluation unit is established by using triangular distribution.

4. The resource evaluation method according to claim 3, characterized in that: The minimum total number of oil and gas reservoirs in the evaluation unit is the number of oil and gas reservoirs discovered in the evaluation unit up to the current time node; The maximum total number of oil and gas reservoirs in the evaluation unit is the product of the most likely total number of oil and gas reservoirs and a coefficient greater than 1; The total number of the most likely oil and gas reservoirs in the evaluation unit is the product of the number of trap clues obtained by seismic interpretation and the current exploration success rate; or The product of the density of discovered oil and gas reservoirs in a highly explored area with similar geological conditions and the area of ​​the evaluation unit.

5. The resource evaluation method according to claim 1, characterized in that: In step S1, the oil and gas reservoirs discovered in the evaluation unit up to the current time node are counted according to the oil and gas reservoir reserve unit merging process; The steps of the oil and gas reservoir reserve unit merging process include: S11: Based on the range of the evaluation unit, determine the oil and gas fields within the range of the evaluation unit; S12: According to the oil and gas fields to which they belong, count the oil and gas reservoir reserve units discovered up to the current time node in the vertical direction according to different layers; S13: further merging oil and gas reservoir reserve units according to the types of oil and gas reservoirs; S14: Based on the merging result of the oil and gas reservoir reserve units and in combination with the scope of the evaluation unit, the oil and gas reservoirs discovered in the merged evaluation unit up to the current time node are formed.

6. The resource evaluation method according to claim 5, characterized in that: In step S13, the types of oil and gas reservoirs include: structural oil and gas reservoirs, lithological oil and gas reservoirs, and structural-lithological oil and gas reservoirs.

7. The resource evaluation method according to claim 1, characterized in that: The distribution parameters of the geological Pareto distribution model include morphological parameters and position parameters.

8. The resource evaluation method according to claim 1, characterized in that: The resource evaluation method further comprises a testing step between step S1 and step S2, wherein the testing step tests whether the size distribution of the oil and gas reservoirs discovered in the evaluation unit up to the current time node conforms to the geological Pareto distribution.

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