Oil and gas reservoir resource evaluation method

Abstract

The invention relates to the technical field of favorable area selection and resource evaluation of oil and gas reservoirs, in particular to an oil and gas reservoir resource evaluation method. The method comprises the following steps: selecting a resource evaluation method adopted for oil and gas reservoir resource evaluation in a work area; performing data cleaning on all parameters required to be used during resource evaluation; respectively determining a data volume lower limit value corresponding to each parameter by using a significance test method; and performing oil and gas reservoir resource evaluation on the work area by using the selected resource evaluation method according to the cleaned parameter data, wherein the data volume of each parameter is not less than the data volume lower limit value corresponding to the parameter. The problems that in existing resource evaluation, many error data exist in parameter data adopted when the resource quantity is calculated, so that the calculation result is affected, and the calculation difficulty is increased; no specific quantitative standard exists for judging whether the number of used parameters can reach the minimum number of calculated resource quantities, and the problem that the credibility of a final resource evaluation result is low due to the fact that data is too small exists.

Description

Technical Field

[0001] The present invention relates to the technical field of favorable selection of oil and gas reservoirs and resource evaluation, and particularly relates to a method for evaluating oil and gas reservoir resources. Background Art

[0002] Based on geological understanding, estimating the scale and potential of oil and gas reserves and forming a numerical representation result is a challenging task. Although there are not many types of variables directly involved in the operation, in fact, there are still many implicit parameters that play a certain control role in determining the distribution of favorable areas and whether the starting conditions are met. During this process, the spatial variability of evaluation indicators, the heterogeneity of reservoir-forming conditions, the spiral rise of geological understanding in the time series, the rolling increase of evaluation content, the gradual enrichment of data and the sequential improvement of exploration levels will all affect the final evaluation result at a certain step or node, and at the same time, it will be presented in the form of "the evaluation results in different years, different regions, and different institutions have certain differences" in the predicted resource volume, that is, the "uncertainty" characteristic will always exist.

[0003] At present, many scholars and experts at home and abroad have carried out detailed and comprehensive research and discussion on the macro-geological conditions of enrichment and reservoir formation, reservoir physical properties, preservation conditions, and the optimization of resource volume calculation methods for various types and levels of oil and gas reservoirs. Some people also make qualitative descriptions of work such as evaluation processes and method optimization to judge the degree of agreement between the final evaluation result and geological understanding. However, according to the accuracy and breadth of the data and information mastered, especially whether the sample quality and quantity can meet the starting conditions for resource volume calculation, and whether the selected resource volume calculation method is the most suitable calculation method, no one has proposed and discussed it yet.

[0004] The evaluation of oil and gas resources is mainly a numerical reflection of the degree of geological understanding. During the process of calculating resource volume, although only a small number of parameters (direct parameters) are involved in the operation, in fact, it requires a large amount of support from various aspects of data (indirect parameters). The wider the scope of data included and the higher the accuracy of parameter acquisition, the more accurate the final evaluation result will be. Therefore, how to accurately describe relevant geological understanding using limited sample data, or how to improve the final credibility of the result by controlling each step in the resource evaluation process, are all necessary, important and urgent tasks to be carried out. Summary of the Invention

[0005] The present invention proposes a method for evaluating oil and gas reservoir resources to solve the problems in existing resource evaluations. There are many incorrect data in the parameter data used for calculating resource volume, which affects the calculation result and increases the calculation difficulty. There is no specific quantitative standard for whether the number of parameters used can reach the minimum number for calculating resource volume, and there is a problem that the credibility of the final resource evaluation result is low due to too little data.

[0006] According to one aspect of the present invention, there is provided a method for evaluating oil and gas reservoir resources, comprising:

[0007] selecting a resource evaluation method for evaluating oil and gas reservoir resources in a work area;

[0008] obtaining all parameters required for evaluating oil and gas reservoir resources in the work area by using the selected resource evaluation method, and performing data cleaning on the all parameters to obtain the cleaned parameter data;

[0009] using a significance test method to respectively determine the lower limit value of the data volume corresponding to each parameter in the all parameter data;

[0010] evaluating the oil and gas reservoir resources in the work area according to the cleaned parameter data by using the selected resource evaluation method, wherein when evaluating the oil and gas reservoir resources, the data volume of each parameter used is not less than the lower limit value of the data volume corresponding to the parameter.

[0011] Preferably, the resource evaluation method at least includes: genetic method, analogy method, statistical method and comprehensive method;

[0012] wherein, the genetic method includes: saturated residual hydrocarbon method, TOC method, chloroform bitumen A method, total hydrocarbon method, hydrocarbon production rate method and basin simulation method;

[0013] the analogy method includes: area abundance analogy method, volume density analogy method, gas content analogy, EUR analogy method and volume rate method;

[0014] the statistical method includes: volume method, probability volume method, evaluation unit division method, ACCESS method, FORSPAN method, material balance method, decline curve method and scale sequence method;

[0015] the comprehensive method includes: Delphi method.

[0016] Preferably, the method for selecting the resource evaluation method for evaluating oil and gas reservoir resources in the work area includes:

[0017] obtaining the exploration and development degree of the work area;

[0018] if the exploration and development degree is low, the resource evaluation methods available for the work area include: area abundance analogy method, volume density analogy method, volume rate method, genetic method and Delphi method;

[0019] If the exploration and development degree is medium, the oil and gas reservoir type of the work area is judged to be conventional or unconventional. If it is conventional, the resource evaluation methods that can be used in the work area include: gas content analogy, basin simulation method, volume method, evaluation unit division method, ACCESS method, EUR analogy method; if it is unconventional, the resource evaluation methods that can be used in the work area include: gas content analogy, volume method, evaluation unit division method, ACCESS method, EUR analogy method;

[0020] If the exploration and development degree is high, the oil and gas reservoir type of the work area is judged to be conventional or unconventional. If it is conventional, the resource evaluation methods that can be used in the work area include: material balance method, decline curve method, scale sequence method, FORSPAN method; if it is unconventional, the resource evaluation methods that can be used in the work area include: scale sequence method, decline curve method, FORSPAN method;

[0021] Among the available resource evaluation methods, the resource evaluation method to be adopted in the work area is selected according to the selection rules corresponding to each resource evaluation method.

[0022] Preferably, the selection rules corresponding to the resource evaluation method include:

[0023] Determine whether the available work area parameters include the hydrocarbon expulsion coefficient of the source rock, and whether the value of the organic matter thermal evolution maturity is greater than 0.5% or less than 4%. If yes, select the saturated residual hydrocarbon method, TOC method, chloroform asphalt A method, total hydrocarbon method, or hydrocarbon production rate method;

[0024] Determine whether it is possible to obtain the serial variation value of the parameter within the predetermined period of the work area. If yes, select the basin simulation method;

[0025] Determine whether the similarity between the oil and gas accumulation conditions in the work area and the corresponding scale area is greater than a predetermined similarity, and whether the analogy coefficient between the work area and the scale interval can be determined. If yes, select the area abundance analogy method, the volume density analogy method, the gas content analogy method, the EUR analogy method, or the volume rate method;

[0026] Determine whether the gas and oil content data of the work area can be obtained. If yes, select the volume method;

[0027] Determine whether the parameters of the work area to be used conform to the normal distribution law. If yes, select the probability volume method;

[0028] Determine whether the drilling and production data of more than the first predetermined number of wells in the work area can be obtained, whether the gas content or oil content data or single well EUR can be obtained, whether the evaluation unit in the work area has a definite boundary division, and whether the analog coefficient can be obtained if it is needed. If yes, select the evaluation unit division method, ACCESS method or FORSPAN method;

[0029] Determine whether it is possible to obtain development data of more than a second predetermined quantity in the work area, including at least cumulative hydrocarbon production, water production, average formation pressure, and PVT physical property parameters. If so, select the material balance method;

[0030] Determine whether it is possible to obtain production data within a predetermined period in the work area, and whether the flow state of the produced fluid during the curve fitting period is steady state or pseudo-steady state in the case of curve fitting. If so, select the decline curve method;

[0031] Determine whether there are more than a predetermined number of oil and gas fields or reservoirs in the work area, whether it is possible to obtain the reserves of the largest-scale oil and gas field or reservoir among them, and whether it is possible to obtain the rate of change of the scale decline of all oil and gas fields or reservoirs. If so, select the scale sequence method;

[0032] Determine whether it is possible to obtain the existing resource evaluation results of the work area in a third predetermined quantity. If so, select the Delphi method.

[0033] Preferably, before obtaining the exploration and development degree of the work area, determine the exploration and development degree of the work area. The method includes:

[0034] Determine whether the work area already has medium-scale geological, geophysical exploration, deployment parameter wells and reference wells, as well as data on the preferred prospective areas, and whether the predicted resource volume data has been calculated based on them. If so, the exploration and development degree of the work area is low;

[0035] Determine whether the work area already has large-scale geological, geophysical exploration, deployment of pre-exploration wells, and data on the preferred favorable areas, and whether the geological resource volume data has been calculated based on them. If so, the exploration and development degree of the work area is medium;

[0036] Determine whether the work area already has geological, 3D seismic, drilling, deployment of evaluation wells, and data on the preferred target areas, and whether the predicted, controlled, and proven reserve data has been calculated based on them. If so, the exploration and development degree of the work area is high.

[0037] Preferably, the method for data cleaning of all parameters includes:

[0038] Respectively establish box plots corresponding to each parameter required in the adopted resource evaluation method;

[0039] Use formula (1) to calculate the upper limit Ne of the valid data of the upper boundary corresponding to each parameter respectively max , and use formula (2) to calculate the lower limit Ne of the valid data of the lower boundary corresponding to each parameter respectively min ;

[0040] Delete the data in each parameter's data that is above its corresponding upper limit value and the data that is below its corresponding lower limit value to obtain the cleaned data;

[0041] Ne max = N u + 1.5(N u - N b ) (1);

[0042] Ne min = N b - 1.5(N u - N b ) (2);

[0043] Where: N b is the value corresponding to the 25% left shift of the peak of the distribution curve of the data group to be evaluated, and N u is the value corresponding to the 25% right shift of the peak of the distribution curve of the data group to be evaluated.

[0044] Preferably, the method of using the significance test method to respectively determine the lower limit value of the data volume corresponding to each parameter in all the parameter data includes:

[0045] Step 11: Randomly divide all the data M of the parameter into a groups, with each group containing b data;

[0046] Step 21: Respectively determine the within-group mean square corresponding to each group of data and the between-group mean square between each group;

[0047] Step 31: Divide the between-group mean square by the corresponding within-group mean square to obtain the significance test statistic F corresponding to each group;

[0048] Step 41: Compare the F with the theoretical significance test statistic F ɑ under the given conditions of M and a. If F < F ɑ , then the lower limit value of the data volume corresponding to the parameter is b, otherwise, return to Step 1.

[0049] Preferably, the method of using the selected resource evaluation method to evaluate the oil and gas reservoir resources in the work area based on the cleaned parameter data includes:

[0050] Based on the cleaned parameter data, use the selected resource evaluation method to respectively determine the resource volume of natural gas or petroleum corresponding to the work area, and determine the corresponding abundance value of natural gas or petroleum according to the resource volume;

[0051] Judge that if the abundance value of the natural gas is greater than 10, or the abundance value of the petroleum is greater than 300, the evaluation result of the oil and gas reservoir resources in the work area is excellent;

[0052] It is determined that if the abundance value of the natural gas is within the range of 2 to 10, or the abundance value of the petroleum is within the range of 50 to 300, the evaluation result of the oil and gas reservoir resources in the work area is medium;

[0053] It is determined that if the abundance value of the natural gas is less than 2, or the abundance value of the petroleum is less than 50, the evaluation result of the oil and gas reservoir resources in the work area is poor.

[0054] Preferably, before evaluating the oil and gas reservoir resources in the work area by using the selected resource evaluation method based on the cleaned parameter data, it further includes:

[0055] Using a normality test method to determine whether the cleaned parameter data conforms to a normal distribution. If not, perform a logarithmic transformation on it and determine whether the logarithmically transformed parameter data conforms to a normal distribution;

[0056] If not, make a violin plot corresponding to the parameter data, and identify whether there is a multimodal distribution phenomenon in the parameter data through the violin plot. If so, group the parameter data. When evaluating the oil and gas reservoir resources in the work area by using the selected resource evaluation method, if there is a data relationship fitting in this resource evaluation method, data relationship fitting needs to be performed on each group of the parameter data respectively;

[0057] If not, when evaluating the oil and gas reservoir resources in the work area by using the selected resource evaluation method, if the adopted resource evaluation method has a data relationship fitting, a non-parametric test method needs to be used.

[0058] Preferably, if there is a data fitting in the process of respectively determining the resource amounts of natural gas and petroleum corresponding to the work area by using the selected resource evaluation method, it is determined whether the goodness of fit of the data fitting meets the first predetermined requirement. If not, adjust the type of the data fitting until the first predetermined requirement is met;

[0059] and / or;

[0060] The method for determining whether the goodness of fit meets the first predetermined requirement includes:

[0061] Perform a White test on the regression model of the data fitting to obtain the corresponding p-value;

[0062] According to the R 2 value in the regression model of the data fitting, determine its corresponding confidence value;

[0063] Judge whether r in the regression model of the data fitting is greater than 0.3, whether F is less than 0.05, whether VIF is less than 10, whether D-W is within the range of 1.7 to 2.3, whether the confidence level is greater than or equal to a predetermined value, and whether the p-value is less than 0.05. If all are yes, the data fitting meets the first predetermined requirement;

[0064] and / or;

[0065] The R in the regression model according to the data fitting 2 value, the method for determining the corresponding confidence level value includes:

[0066] If the data fitting is a model established by several univariate regressions, use formula (3) to calculate the corresponding confidence level value. If the data fitting is a model established by multiple regressions, use formula (4) to calculate the corresponding confidence level value;

[0067]

[0068] In the formula: C is the confidence level of several univariate regressions, R 2 i is the determination coefficient of the i-th regression model, n i is the sample size of the independent variable in the i-th regression, m is the total number of regression models, N is the total number of independent variables in the m regression models, and i is the item number of the regression model;

[0069]

[0070] In the formula: C c is the confidence level of multiple regressions, R 2 is the determination coefficient of the multiple regression model, β j is the determination coefficient of the j-th independent variable, k is the number of independent variables, j is the item number of the independent variable, and n is the total number of k independent variables.

[0071] The present invention has at least the following beneficial effects:

[0072] The present invention provides a method for evaluating oil and gas reservoir resources. By cleaning the data of the parameters participating in the resource evaluation, the effectiveness of the data is improved; by determining the lower limit value of the data volume of each parameter, the integrity of the data is ensured, and at the same time, the evaluation efficiency is prevented from being too low due to excessive data. Thereby, the accuracy of the oil and gas reservoir resource evaluation result is improved, the integrity of the evaluation is ensured, and a theoretical foundation is laid for the subsequent exploration and deployment work. Brief Description of the Drawings

[0073] The drawings here are incorporated into the specification and constitute a part of this specification. These drawings show embodiments in line with the present invention and are used together with the specification to explain the technical solutions of the present invention.

[0074] Figure 1 A flowchart showing a method for evaluating oil and gas reservoir resources according to an embodiment of the present invention. Detailed implementation manners

[0075] Various exemplary embodiments, features and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings do not have to be drawn to scale unless otherwise specified.

[0076] The term "exemplary" used herein means "serving as an example, embodiment or illustration". Any embodiment described as "exemplary" herein does not have to be construed as superior or better than other embodiments.

[0077] The term "and / or" in this document merely describes an association relationship of associated objects and indicates that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" in this document means any one of a plurality or any combination of at least two of a plurality. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set composed of A, B, and C.

[0078] In addition, for better illustration of the present invention, numerous specific details are given in the following detailed implementation manners. Those skilled in the art should understand that the present invention can be implemented without some specific details. In some instances, methods, means, elements and circuits well-known to those skilled in the art are not described in detail so as to highlight the gist of the present invention.

[0079] Figure 1 A flowchart showing a method for evaluating oil and gas reservoir resources according to an embodiment of the present invention. As Figure 1 shown, a method for evaluating oil and gas reservoir resources includes: Step S01: Select a resource evaluation method for evaluating oil and gas reservoir resources in a work area; Step S02: Obtain all parameters required for evaluating oil and gas reservoir resources in the work area using the selected resource evaluation method, and perform data cleaning on the all parameters to obtain cleaned parameter data; Step S03: Use a significance test method to respectively determine the lower limit value of the data volume corresponding to each parameter in the all parameter data; Step S04: According to the cleaned parameter data, use the selected resource evaluation method to evaluate the oil and gas reservoir resources in the work area, wherein when evaluating the oil and gas reservoir resources, the data volume of each parameter used is not less than the lower limit value of the data volume corresponding to the parameter.

[0080] A method for evaluating oil and gas reservoir resources provided by an embodiment of the present invention specifically includes the following steps:

[0081] Step S01: Select a resource evaluation method for oil and gas reservoir resource evaluation in the work area.

[0082] In the present invention, the resource evaluation method at least includes: genetic method, analogy method, statistical method, and comprehensive method; among them, the genetic method includes: saturated residual hydrocarbon method, TOC method, chloroform bitumen A method, total hydrocarbon method, hydrocarbon production rate method, and basin simulation method; the analogy method includes: area abundance analogy method, volume density analogy method, gas content analogy, EUR analogy method, and volume rate method; the statistical method includes: volume method, probabilistic volume method, evaluation unit division method, ACCESS method, FORSPAN method, material balance method, decline curve method, and scale sequence method; the comprehensive method includes: Delphi method.

[0083] In the present invention, the method for selecting a resource evaluation method for oil and gas reservoir resource evaluation in the work area includes: obtaining the exploration and development degree of the work area; if the exploration and development degree is low, the resource evaluation methods available for the work area include: area abundance analogy method, volume density analogy method, volume rate method, genetic method, and Delphi method; if the exploration and development degree is medium, determine whether the oil and gas reservoir type in the work area is conventional or unconventional. If it is conventional, the resource evaluation methods available for the work area include: gas content analogy, basin simulation method, volume method, evaluation unit division method, ACCESS method, EUR analogy method. If it is unconventional, the resource evaluation methods available for the work area include: gas content analogy, volume method, evaluation unit division method, ACCESS method, EUR analogy method; if the exploration and development degree is high, determine whether the oil and gas reservoir type in the work area is conventional or unconventional. If it is conventional, the resource evaluation methods available for the work area include: material balance method, decline curve method, scale sequence method, FORSPAN method. If it is unconventional, the resource evaluation methods available for the work area include: scale sequence method, decline curve method, FORSPAN method; among the available resource evaluation methods, select the adopted resource evaluation method for the work area according to the selection rules corresponding to each resource quantity evaluation method.

[0084] In the present invention, before obtaining the exploration and development degree of the work area, the exploration and development degree of the work area is determined, and the method includes: judging whether the work area already has medium-scale geology, geophysical exploration, deployment of parameter wells and reference wells, and data of preferred prospective areas, and has calculated the predicted resource volume data based on them. If so, the exploration and development degree of the work area is low; judging whether the work area already has large-scale geology, geophysical exploration, deployment of exploration wells, and data of preferred favorable areas, and has calculated the geological resource volume data based on them. If so, the exploration and development degree of the work area is medium; judging whether the work area already has geology, 3D seismic, drilling, deployment of evaluation wells, and data of preferred target areas, and has calculated the predicted, controlled and proved reserve data based on them. If so, the exploration and development degree of the work area is high.

[0085] In the embodiment of the present invention, by analyzing the shale gas evaluation work process, it can be found that the integrity of the geological evaluation process, the rationality of the selection of the resource volume calculation formula, and the scientificity of the parameters used play a major controlling role in the accuracy and credibility of the evaluation results.

[0086] Common resource evaluation methods (resource volume calculation methods) can be divided into analogy method, genetic method, statistical method and comprehensive method according to the principle and formula; each of these major categories can be further divided into 4-7 sub-categories according to different specific parameters selected. The different proposed times, backgrounds and exploration purposes of the resource evaluation methods result in different application conditions and scopes. On the other hand, the different mechanisms of the methods also lead to large differences in the inheritance, rationality and effectiveness of the methods in the calculation of shale gas resource volume.

[0087] Among them, the saturated residual hydrocarbon method, TOC (organic matter abundance) method, chloroform bitumen "A" method, total hydrocarbon method, hydrocarbon production rate method and basin simulation method in the genetic method can also be called the geochemical material balance method. Its principle is based on the principle of mass conservation of substances, and calculates the total hydrocarbon generation, discharge, adsorption, migration and loss in the source rock, so as to predict the oil and gas resource volume in the block to be evaluated.

[0088] The principles of the area abundance analogy method, volume density analogy method, gas content analogy, EUR (ultimate recoverable quantity per well) analogy method and volume rate method in the analogy method are to infer the unknown information by analogy with the corresponding information of the evaluated block (calibration area) with the known information of the block to be evaluated under the condition of maximum similarity.

[0089] In the statistical method, the volume method multiplies the oil or gas content per unit volume by the volume of the block to be evaluated to obtain the total resource; the probability volume method is similar to the volume method, except that more prediction values can be obtained by establishing a probability density function for each parameter. The evaluation unit division method, the ACCESS method (Microsoft software, mainly used for grid division), and the FORSPAN method (Forspan) have principles similar to the volume method. The difference is that these three methods are all based on the "finite element" principle. Before evaluation, the block to be evaluated is further refined into several small evaluation units according to the hydrocarbon accumulation conditions or well control area, and then the original resource volume of the undeveloped or data-free area in the block is calculated by using the existing data or exploration success rate. The material balance method follows the law of conservation of matter that "under certain conditions during a certain development time, the sum of the cumulative produced fluid volume and the remaining fluid storage volume underground is equal to the original fluid reserve". Based on material balance, the relationship between the average formation pressure and the hydrocarbon production volume is established, and the abscissa intercept of the linear extrapolation of the equation corresponds to the geological reserve of the target gas reservoir. The decline curve method establishes the relationship between the oil and gas production curve and the production time through numerical simulation, and based on this, the rising part of the curve is extrapolated to the intersection point with the y-axis to obtain the oil and gas reservoir reserve under theoretical conditions. The scale sequence method is a method based on the Pareto law, which uses the scale and sequence of the discovered oil and gas reservoirs in the evaluation area to predict the scale of the undiscovered oil and gas reservoirs.

[0090] In the comprehensive method, the principle of the Delphi method is to synthesize the understandings of different geological experts on shale gas in the study area.

[0091] In the present invention, the selection rules corresponding to the resource quantity evaluation method include: determining whether the hydrocarbon source rock hydrocarbon expulsion coefficient is included in the work area parameters that can be obtained, and whether the value of the organic matter thermal evolution maturity is greater than 0.5% or less than 4%. If so, the saturated residual hydrocarbon method or the TOC method or the chloroform bitumen A method or the total hydrocarbon method or the hydrocarbon production rate method is selected; determining whether the sequence change value of the parameters within a predetermined period of the work area can be obtained. If so, the basin simulation method is selected; determining whether the similarity of the hydrocarbon accumulation conditions between the work area and the corresponding calibration area is greater than a predetermined similarity, and whether the analogy coefficient between the work area and the calibration area can be determined. If so, the area abundance analogy method or the volume density analogy method or the gas content analogy or the EUR analogy method or the volume rate method is selected; determining whether the gas content and oil content data of the work area can be obtained. If so, the volume method is selected; determining whether the parameters of the work area to be used conform to the normal distribution law. If so, the probability volume method is selected; determining whether the drilling and production data of more than a first predetermined number of wells in the work area can be obtained, whether the gas content or oil content data or the single well EUR can be obtained, whether the evaluation units in the work area have a determined boundary division, and whether the analogy coefficient can be obtained in the case where the analogy coefficient is required. If so, the evaluation unit division method or the ACCESS method or the FORSPAN method is selected; determining whether the development data of more than a second predetermined number in the work area can be obtained, including at least: the cumulative hydrocarbon production and water production, the average formation pressure, and the PVT physical property parameters. If so, the material balance method is selected; determining whether the production data within a predetermined period of the work area can be obtained, and whether the flow state of the produced fluid corresponding to the curve fitting time period is in a steady state or a pseudo-steady state in the case where curve fitting is required. If so, the decline curve method is selected; determining whether there are more than a predetermined number of oil and gas fields or reservoirs in the work area, whether the reserves of the largest-scale oil and gas field or reservoir can be obtained, and whether the change rate of the scale decline of all oil and gas fields or reservoirs can be obtained. If so, the scale sequence method is selected; determining whether the resource evaluation results of a third predetermined number of existing work areas can be obtained. If so, the Delphi method is selected.

[0092] In an embodiment of the present invention, since there are multiple available resource evaluation methods selected according to the exploration and development degree of the work area, further selection of a specific resource quantity calculation method needs to be further screened according to the usage conditions when calculating by each resource quantity method.

[0093] When using the saturated residual hydrocarbon method, the TOC method, the chloroform bitumen "A" method, the total hydrocarbon method, and the hydrocarbon production rate method, the organic matter thermal evolution maturity in the parameters obtained by the work area needs to be greater than 0.5% or less than 4%, and the hydrocarbon source rock hydrocarbon expulsion coefficient can be obtained.

[0094] When using the basin simulation method, the parameters required for modeling must have the characteristics of directly participating in the geological history process, that is, different change values (sequence change values) corresponding to a certain parameter in the work area during a certain period (predetermined period).

[0095] When using the area abundance analogy method, volume density analogy method, gas content analogy, EUR analogy method and volume rate method, the hydrocarbon accumulation conditions in the work area and the calibration area need to be similar, and at the same time, the analogy coefficient between the two blocks needs to be determined.

[0096] When using the volume method, accurate data of gas content or oil content need to be mastered.

[0097] When using the probability volume method, the parameters directly involved in the calculation need to conform to the normal distribution law.

[0098] When using the evaluation unit division method, ACCESS method and FORSPAN method, a large amount of drilling and production data are required. The first predetermined quantity needs to be specifically set according to the size of the work area and the number of wells included. At the same time, the gas content, or oil content or single-well EUR need to be mastered; the boundaries of the evaluation unit division are clear and obvious; in some cases, the analogy coefficient needs to be mastered.

[0099] When using the material balance method, a large amount of development data need to be mastered. Among them, the second predetermined quantity is 30. Generally, when the data volume of the parameters is greater than 30, it belongs to a large amount of data; the development data include cumulative hydrocarbon production and water production, average formation pressure, and PVT physical property parameters. The PVT physical property parameters include gas critical pressure, critical temperature, deviation factor, compressibility factor, volume factor, and natural gas viscosity, etc.

[0100] When using the decline curve method, production data within a certain period of time are required, such as development data with a predetermined period of ≥3 years; it is necessary to ensure that when fitting the production and time curves, the flow type of the produced fluid corresponding to the curve fitting time period is steady state or pseudo-steady state, and the type does not change.

[0101] When using the scale sequence method, at least 3 oil and gas fields or reservoirs discovered in the work area are required, that is, the predetermined number is 3, and their scales can be accurately located and sorted. It is necessary to master the reserves of the largest known oil and gas field or reservoir and the scale decline rate (k value).

[0102] When using the Delphi method, the resource evaluation results of multiple experts on the block to be evaluated need to be obtained, and the third predetermined quantity is set according to its own situation.

[0103] From the principles and application conditions of the above different resource quantity calculation methods, it can be concluded that:

[0104] 1. The key parameters of the genetic method are the total hydrocarbon generation amount and the hydrocarbon expulsion amount. The accuracy of the calculation results of these two depends to a great extent on the correct selection of geochemical parameters and the profound understanding of geological problems such as hydrocarbon migration and accumulation. Generally, the process parameters we obtain, such as the total hydrocarbon generation rate and the TOC recovery coefficient, are mostly theoretical values of thermal simulation under laboratory conditions, and there are more or less differences between them and the real geological conditions, which will affect the accuracy of the final calculation results. In addition, in areas with high - thermal evolution maturity (high R o value > 3.5% or 4%, and the specific value needs to refer to the organic matter composition in the block to be evaluated), because the samples collected do not have the ability to generate hydrocarbons at all, and it is difficult to obtain the above - mentioned key parameters under laboratory conditions, so this genetic method is not applicable; in addition, in areas transformed by complex tectonic movements, the repeated opening and termination of the hydrocarbon - generation process have changed the structure of the hydrocarbon - generating parent material (organic matter), thus changing its hydrocarbon - generating behavior characteristics, resulting in the final trend being difficult to predict, and this genetic method is also not applicable.

[0105] 2. The analogy method is simple to operate. First, it is necessary to master the hydrocarbon enrichment background and reservoir - forming conditions of the block to be evaluated. Then, select the calibration area based on the maximum similarity as the main basis and establish a similarity coefficient. Finally, based on the data of the calibration area, analogize or predict the key parameters that are desired but unknown in the area to be evaluated. For example, if the block A to be evaluated has 80% similarity with the calibration area B in geological conditions, the analogy coefficient is set to 0.8; if at this time we want to calculate the resource amount or reserve through the gas content or EUR, the gas content or EUR of B is n and m respectively, then the corresponding parameters of block A are n*(1 ± 0.2) or m*(1 ± 0.2). However, in this process, the selection of both the analogy coefficient and the calibration area B is subjective and will affect the final calculated quantity value and accuracy. Generally, this analogy method is less used, except when the non - data type and abundance are already known to be very low.

[0106] 3. In the volumetric method of the statistical method, the difficulty lies in obtaining the gas content or oil content. The pressure-maintained coring can ensure the accuracy of the above values, but the cost is too high, and it is difficult to obtain a large amount of data in the block to be evaluated under actual conditions. If the empirical formula is used for calculation, under the influence of the heterogeneity of the geological conditions in the plane and profile, the same problem as in the process parameter acquisition of the genetic method will exist, that is, the calculation accuracy is difficult to control. When the material balance method is applied to low-porosity (<12%) and low-permeability (0.1 mD) oil and gas reservoirs such as shale or tight reservoirs, due to the phase changes of natural gas or oil (changing among adsorbed state, free state, and dissolved state with the changes of production pressure and temperature), it is difficult to obtain the relationship between formation pressure and cumulative production. In the decline curve method, most curve models require the hydrocarbon flow to conform to (pseudo) steady state. On the other hand, a sufficiently long production period is required to obtain a stable relationship between time and cumulative production (ensuring that the fluid is in the steady state stage within 10 years of production). Among them, when the Arps curve faces unconventional oil and gas reservoirs as mentioned above, its decline index will be >1, resulting in an overestimated prediction result; the Doung and PLE models are difficult to obtain values because there is no relevant determination standard for the constants in the formula, resulting in the possibility of multiple solutions; the parameters in SPED need to be continuously iterated / attempted to find a suitable prediction formula. In comparison, in terms of model accuracy from large to small in a short time: Doung > SPED > PLE; such differences gradually narrow with the increase of time. The scale sequence method requires at least 3 oil and gas fields with discovered and determined reserve scales in the block to be evaluated, and it is not applicable to blocks with low exploration degrees.

[0107] 4. The Delphi method requires several experts to make corresponding evaluations on the resource potential of the block to be evaluated, and then assign different weight coefficients to different results, and sum the evaluation results multiplied by the corresponding weights. In this process, subjectivity is very strong, and the evaluation results of the same block by different scholars or institutions may vary greatly.

[0108] In addition, in the process of optimizing the resource volume calculation method, it is necessary to follow the order of selecting geological parameters from those with high exploration degree characteristics to those with low exploration degree. For example, if there are development wells in the block and the number is sufficient, the decline curve or material balance method is preferred; if the number is not so sufficient, the method including parameters such as single-well EUR is selected downward; if there is no development or exploration well data, the method including gas content or oil content is selected; finally, the method including parameters related to geological conditions such as sedimentary environment and preservation conditions is selected, and these theories, principles, and processes also determine the preferred methods under different conditions.

[0109] Step S02: Obtain all the parameters required for the oil and gas reservoir resource evaluation of the work area using the selected resource evaluation method, and perform data cleaning on all the parameters to obtain the cleaned parameter data.

[0110] In the present invention, the method for cleaning data of all parameters includes: respectively establishing box plots corresponding to each parameter required in the adopted resource evaluation method; using formula (1) to calculate the upper limit Ne of the valid data at the upper boundary corresponding to each parameter respectively max , and using formula (2) to calculate the lower limit Ne of the valid data at the lower boundary corresponding to each parameter respectively min ; deleting the data above the corresponding upper limit value and the data below the corresponding lower limit value in the data included in each parameter to obtain the cleaned data;

[0111] Ne max =N u +1.5(N u -N b )(1);

[0112] Ne min =N b -1.5(N u -N b )(2);

[0113] In the formula: N b is the value corresponding to the peak of the distribution curve of the data group to be evaluated (the data included in the parameter to be cleaned) shifted 25% to the left, and N u is the value corresponding to the peak of the distribution curve of the data group to be evaluated shifted 25% to the right.

[0114] In the embodiment of the present invention, it is recommended to use DoubleFox software based on the Kriging interpolation method to perform grid and vector processing on the parameters of the entire work area. This method can ensure that the key parameters of geological evaluation are within the same evaluation system or range of variation, improve the accuracy and precision of the evaluation results, and can also make up for the data blank phenomenon in the undeveloped blocks and increase the data abundance.

[0115] In addition, before carrying out the evaluation work on more than a thousand or even tens of thousands of relevant geological parameters obtained through multiple channels (including various parameters participating in the selection area evaluation and resource volume calculation), it is also necessary to clean the data to ensure the effectiveness of the data for use. Data cleaning is mainly to eliminate outliers (data that does not conform to the concentrated distribution area of most data).

[0116] Data cleaning can first establish the data to be selected, that is, the box plot of all the data included in the parameter. The data exceeding the upper and lower boundaries of the graph are outlier data. Among them, the upper boundary is the upper limit Ne of the valid data (the data group not including outlier data), max calculated by (1); the lower boundary is the lower limit Ne of the valid data (the data group not including outlier data), min calculated by (2). Eliminate the data exceeding Nemax and Ne min After outliers are removed, the accuracy of subsequent calculation results or the acquisition of a certain overall law is improved.

[0117] Step S03: Using the significance test method, determine the lower limit value of the data volume corresponding to each parameter in all the parameter data respectively.

[0118] In the present invention, the method of using the significance test method to determine the lower limit value of the data volume corresponding to each parameter in all the parameter data respectively includes: Step 11: Randomly divide all the data M of the parameter into a groups, and each group contains b data; Step 21: Determine the within-group mean square corresponding to each group of data and the between-group mean square between each group respectively; Step 31: Divide the between-group mean square by the corresponding within-group mean square to obtain the significance test statistic F corresponding to each group; Step 41: Compare the F with the theoretical significance test statistic F corresponding to the given M and a conditions ɑ for comparison. If F < F ɑ , then the lower limit value of the data volume corresponding to the parameter is b, otherwise, return to Step 1.

[0119] In the embodiment of the present invention, it can be seen from the calculation formula of the standard error that there is a negative correlation between the sampling error and the sample size. That is to say, the greater the data density, the more accurate the evaluation result. However, geological data has spatio-temporal consistency. For example, a set of strata in the same sedimentary environment has experienced the same tectonic movement transformation, and the degree of change (dispersion degree) of a certain geological parameter may be smaller compared with the parameters in other fields. At this time, we do not need to use all thousands of data for calculation or relationship analysis, but only need to use part of the data that can express the distribution trend or law of the parameters in the whole area; the number of this part of the data is the lower limit of the data volume. To explore the minimum lower limit value of the geological (parameter) data volume required for the evaluation object (work area), in fact, it is necessary to clarify how many data can represent the statistical population in a given area, that is, to explore how to ensure the representativeness and typicality of the sample to the population while minimizing the data volume. Here, we use the significance test principle to give a quantitative method for determining the lower limit value of the parameter data volume based on the significance test statistic F.

[0120] If you want to calculate the lower limit of the data volume of a certain parameter, first randomly divide it into a groups, with b data in each group, and the product of a and b is equal to the total number of data M of the parameter; calculate the within-group mean square of each group after grouping and the between-group mean square between each group respectively; divide the between-group mean square by the within-group mean square (with the between-group mean square as the numerator) to obtain the significance test statistic F, and the specific formula is shown in Equation (5).

[0121]

[0122] Where: M is the total sample size (data volume) of the parameters for the lower limit of the data volume to be calculated, a is the number of groups, b is the data sample size in each group, g is the group item number (g ≤ a), s is the data item number in each group (s ≤ b), and x gs is the s-th data in the g-th group, and X is the total average value of the M data.

[0123] Compare the F obtained according to Equation (5) with the theoretical significance test statistic F under the given M and a conditions ɑ If the former is less than the latter, it indicates that there is no significant difference between the data of each group, and the expression ability of each group for the overall parameter is the same. Under this grouping scheme, the b data can fully express the change trend and law of the M data, indicating that the grouping is appropriate. At this time, b is the lower limit value of the data volume; if the situation is contrary to the above, it is necessary to return to step 11 to regroup the parameters of the M data volume, that is, adjust the values of a and b and recalculate the F value until F < F ɑ .

[0124] Among them, F ɑ represents the theoretical F value at the significance level of α, and this value can be queried in the F distribution table. F ɑ has three limiting conditions for its value: 1. Generally, it is sufficient to take α = 0.05, which means the confidence level of the F value at this time is 95%; if you want to improve the accuracy of the calculation result, α can be appropriately reduced. For example, if its value is 0.02, it means the confidence level of the F value is 98%; 2. The degree of freedom of the between-group mean square df1 = a - 1; 3. The degree of freedom of the within-group mean square df2 = M - a. Given the above three values of α, df1, and df2, the corresponding F ɑ value can be found in the F distribution table.

[0125] Step S04: According to the cleaned parameter data, use the selected resource evaluation method to conduct an oil and gas reservoir resource evaluation for the work area. Among them, when conducting an oil and gas reservoir resource evaluation, the data volume of each parameter used is not less than the lower limit value of the data volume corresponding to the parameter.

[0126] In the present invention, the method for evaluating oil and gas reservoir resources in a work area by using the selected resource evaluation method according to the cleaned parameter data includes: according to the cleaned parameter data, using the selected resource evaluation method to respectively determine the resource amounts of natural gas or petroleum corresponding to the work area, and determining the corresponding abundance values of natural gas or petroleum according to the resource amounts; judging that if the abundance value of natural gas is greater than 10, or the abundance value of petroleum is greater than 300, the evaluation result of the oil and gas reservoir resources in the work area is excellent; judging that if the abundance value of natural gas is in the range of 2 to 10, or the abundance value of petroleum is in the range of 50 to 300, the evaluation result of the oil and gas reservoir resources in the work area is medium; judging that if the abundance value of natural gas is less than 2, or the abundance value of petroleum is less than 50, the evaluation result of the oil and gas reservoir resources in the work area is poor.

[0127] In an embodiment of the present invention, the amount of resources per unit area (resource abundance) can be used to judge the quality of the resource evaluation result. Among them, the method for determining the abundance values of natural gas and petroleum corresponding to the resource amount is: abundance value = resource amount / area of the evaluation block (work area).

[0128] Among them, the unit of the natural gas abundance value is 10 8 m 3 / km 2 and the unit of the petroleum abundance value is 10 4 t / km 2 .

[0129] In the present invention, before evaluating the oil and gas reservoir resources in the work area by using the selected resource evaluation method according to the cleaned parameter data, it further includes: using the normality test method to determine whether the cleaned parameter data conforms to the normal distribution. If not, perform logarithmic transformation on it and judge whether the logarithmically transformed parameter data conforms to the normal distribution; if not, make a violin plot corresponding to the parameter data, and identify whether there is a multimodal distribution phenomenon in the parameter data through the violin plot. If so, group the parameter data. When using the selected resource evaluation method to evaluate the oil and gas reservoir resources in the work area, if there is a data relationship fitting in the resource evaluation method, the data relationship fitting needs to be performed on each group of the parameter data respectively; if not, when using the selected resource evaluation method to evaluate the oil and gas reservoir resources in the work area, if the resource evaluation method used has a data relationship fitting, the nonparametric test method needs to be used.

[0130] In the embodiments of the present invention, the normality test of data, that is, determining whether the sample conforms to the normal distribution, is a very important but often overlooked data preprocessing means and step. It is an important special goodness-of-fit hypothesis test in statistical decision-making. Common t-distribution, F-distribution, and u-distribution are all evolved on this basis. If the data distribution does not have normality, it may cause the failure of many subsequent parameter estimation and inference methods, and even affect the rationality of geological understanding. For example, if a linear correlation is established between parameter A that conforms to the normal distribution and parameter B that does not conform to the normal distribution, it is easy to cause inaccurate relationship simulation results due to the heteroscedasticity between the two. Another example is that when calculating the resource volume using the probability volume method, if the parameters directly involved in the calculation do not conform to the normal distribution, then the probability values and the subsequent established resource volume probability density curve will both fail.

[0131] There are many methods for performing the normality test. Here, a method recommendation table is sorted out according to the statistical significance and the requirements for the sample size as shown in Table 1 below.

[0132] Table 1: Method Recommendation Table for Normality Test

[0133]

[0134]

[0135] In the case of known parameter data volume, select a suitable test method according to Table 1 to obtain the normality test result of the parameter.

[0136] If the test result shows that the parameter data does not conform to the normal distribution, first, the parameter data can be logarithmically transformed to reduce the variable scale and range of change and weaken the influence of the data group variance, and then a certain method in Table 1 can be selected again for normality analysis.

[0137] If it still does not conform to the normal distribution after logarithmic transformation, consider making a violin plot of the parameter data group to identify whether there is a "multi-modal distribution" phenomenon in the data, that is, there are two or more concentrated distribution regions in the data. If so, in the subsequent work, the data needs to be grouped and then processed separately. The grouping method is to divide the data in each concentrated region in the violin plot into a group, and when there is a fit in a certain subsequent resource volume calculation method, each group of data is separately fitted.

[0138] If no matter how it is operated, the data still does not conform to the normal distribution, then in the subsequent fitting of the relationship between multiple groups of data, hypothesis parameter tests such as linear regression cannot be used, but 4 non-parametric test methods including "sign, Wilcoxon-signed rank, McNemar, and marginal homogeneity" need to be adopted.

[0139] In the present invention, when using the selected resource evaluation method to determine the resource amounts of natural gas and petroleum corresponding to the work area respectively, if there is data fitting, it is necessary to determine whether the goodness of fit of the data fitting meets the first predetermined requirement. If not, adjust the type of the data fitting until the first predetermined requirement is met;

[0140] and / or; The method for determining whether the goodness of fit meets the first predetermined requirement includes: performing a White test on the regression model of the data fitting to obtain the corresponding p-value; according to the R 2 value in the regression model of the data fitting, determining its corresponding confidence value; judging whether r in the regression model of the data fitting is greater than 0.3, whether F is less than 0.05, whether VIF is less than 10, whether D-W is within the range of 1.7 to 2.3, whether the confidence is greater than or equal to the predetermined value, and whether the p-value is less than 0.05. If all are yes, the data fitting meets the first predetermined requirement;

[0141] and / or; The method for determining the corresponding confidence value according to the R 2 value in the regression model of the data fitting includes: if the data fitting is a model established by several univariate regressions, calculate the corresponding confidence value using formula (3); if the data fitting is a model established by multiple regressions, calculate the corresponding confidence value using formula (4);

[0142]

[0143] In the formula: C is the confidence of several univariate regressions, R 2 i is the determination coefficient of the i-th regression model, n i is the sample size of the independent variable in the i-th regression, m is the total number of regression models, N is the total number of independent variables in m regression models, and i is the item number of the regression model;

[0144]

[0145] In the formula: C c is the confidence of multiple regressions, R 2 is the determination coefficient of the multiple regression model, β j is the determination coefficient of the j-th independent variable, k is the number of independent variables, j is the item number of the independent variable, and n is the total number of k independent variables.

[0146] In the embodiment of the present invention, determining whether the goodness of fit of the data fitting meets the first predetermined requirement is mainly to judge the goodness of fit of the fitting model and the abundance of the data when there is a data fitting relationship or a formula calculation requirement.

[0147] Before performing relationship fitting, the parameter data is first cleaned and subjected to a normality test. The goodness of fit of the model is mainly used to judge the predictive ability of the model used to establish the relationship. It mainly includes two aspects. One is whether there is a significant relationship between the parameters for which the relationship is established, and the other is whether the selected model can best explain the mutual expression ability between the parameters for which the relationship is to be established. The above two aspects mainly rely on six statistical variables, namely r, F, VIF, D-W, R in the regression model (fitting model) 2 and the p-value in the White test. The statistical significance they possess and the values when they are acceptable, that is, the first predetermined requirements, are shown in Table 2 below.

[0148] Table 2: Recommended Table of Statistical Variables and Values for Judging the Goodness of Fit of the Model

[0149]

[0150] In actual operation, the judgments of r, F, and p-values on the goodness of fit of the entire fitting model are often ignored, which is worthy of attention. Additionally, because R 2 cannot be quantitatively evaluated, therefore, the present invention provides a quantitative calculation method for calculating the credibility of the final result based on the determination coefficient R of the model 2

[0151] The first type mainly focuses on the goodness of fit of the model when establishing relationships between multiple "single independent variables and single dependent variables", that is, multiple (several) simple regressions. Given that the determination coefficient R 2 expresses the meaning of "what proportion of the variation in the dependent variable value can be explained by the change in the independent variable", then 1 - R 2 is the proportion of "the change in the independent variable that cannot explain the change in the dependent variable", and this part can be regarded as uncertainty. Then the credibility quantification calculation formula when multiple simple regressions are used jointly (such as establishing a parameter system, etc.) is Formula (3).

[0152] The second type mainly focuses on the goodness of fit of the model when establishing relationships between "multiple single independent variables and single dependent variables", that is, multiple regressions. The principle of the quantification calculation of the goodness of fit of this model is similar to the above. The difference is that the increase in the number of independent variables will affect the proportion of the variation in the dependent variable that is explained by the regression equation. Therefore, there are slight adjustments here, as shown in Formula (4) specifically. Among them, the predetermined value can be set according to the specific accuracy requirements. For example, if the predetermined value is 0.9, it means the accuracy needs to reach 90%. Generally, it can be set to 0.5.

[0153] ​If the goodness of fit of the model fails to meet the first predetermined requirement, it is necessary to adjust the type of the fitted curve. The types of fitted curves include, for example, logarithm, quadratic, exponential, etc. Then, it is determined again whether the fitted model after adjusting the type of the fitted curve meets the first predetermined requirement. If all types of curves do not meet the requirement, when calculating the resource quantity using a certain resource quantity calculation method, a quantitative model is not established for the parameter data, and instead, cluster analysis or chi-square test is adopted.

[0154] It can be understood that, without violating the principle logic, the above-mentioned method embodiments mentioned in the present invention can be combined with each other to form combined embodiments. Due to space limitations, the present invention will not elaborate further.

[0155] The execution subject of the oil and gas reservoir resource evaluation method can be an oil and gas reservoir resource evaluation device. For example, the oil and gas reservoir resource evaluation method can be executed by a terminal device, a server, or other processing devices. Among them, the terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementation manners, the oil and gas reservoir resource evaluation method can be implemented by a processor invoking computer-readable instructions stored in a memory.

[0156] Those skilled in the art can understand that in the above method of the specific implementation manner, the writing order of each step does not mean a strict execution order that constitutes any limitation to the implementation process, and the specific execution order of each step should be determined according to its function and possible internal logic.

[0157] Due to the adoption of the above technical solution, the present invention has at least the following advantages: (1) By means of data cleaning and normality test, the rationality and scientificity of data fitting relationship, data threshold determination, using the mean to replace the data group and data multiplication operation in the evaluation process are greatly improved. Fundamentally, the quality of the resource evaluation process is controlled, and ultimately the purpose of controlling the credibility of the resource evaluation result from the source can be achieved. It has strong foresight, pioneering and innovation. (2) The present invention disassembles the steps that may affect the credibility of the evaluation result in resource evaluation, and gives corresponding methods and processes for improving the credibility of the final evaluation result for each step. For example, a method for selecting the resource quantity calculation method under different geological conditions and exploration degrees in the work area is given. (3) The present invention gives a method for judging the goodness of fit of the model when fitting the relevant parameter relationship in the process of resource evaluation, and establishes a quantitative calculation formula for credibility under multiple types of regression models. This enhances the quantification and visualization of uncertainty in the data processing process, and provides a calculable means for the intuitive judgment of the credibility of the resource evaluation result. (4) Based on the expressiveness of data, the present invention gives a calculation method for the lower limit of the data volume required for the evaluation parameters. This measure reduces unnecessary workload while ensuring that the sample volume of the data used has high objectivity and accuracy.

[0158] The various embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, practical applications or improvements to the technology in the market, or to enable other ordinary skilled persons in the technical field to understand the embodiments disclosed herein.

Claims

1. A method for evaluating oil and gas reservoir resources, characterized in that Including: The resource evaluation method used for evaluating oil and gas reservoir resources in a selected work area; Obtaining all parameters required for evaluating oil and gas reservoir resources in the work area using the selected resource evaluation method, and performing data cleaning on all the parameters to obtain the cleaned parameter data; Using the significance test method to respectively determine the lower limit value of the data volume corresponding to each parameter in all the parameter data; Based on the cleaned parameter data, using the selected resource evaluation method to evaluate the oil and gas reservoir resources in the work area, wherein, when performing the oil and gas reservoir resource evaluation, the data volume of each parameter used is not less than the lower limit value of the data volume corresponding to the parameter.

2. The method for evaluating oil and gas reservoir resources according to claim 1, wherein The resource evaluation method at least includes: genetic method, analogy method, statistical method, and comprehensive method; Among them, the genetic method includes: saturated residual hydrocarbon method, TOC method, chloroform bitumen A method, total hydrocarbon method, hydrocarbon production rate method, and basin simulation method; The analogy method includes: area abundance analogy method, volume density analogy method, gas content analogy, EUR analogy method, and volume rate method; The statistical method includes: volume method, probabilistic volume method, evaluation unit division method, ACCESS method, FORSPAN method, material balance method, decline curve method, and scale sequence method; The comprehensive method includes: Delphi method.

3. The oil and gas reservoir resource evaluation method according to claim 2, characterized in that The method for selecting the resource evaluation method used for evaluating oil and gas reservoir resources in the work area includes: Obtaining the exploration and development degree of the work area; If the exploration and development degree is low, the resource evaluation methods that can be used in the work area include: area abundance analogy method, volume density analogy method, volume rate method, genetic method, and Delphi method; If the exploration and development degree is medium, determine whether the oil and gas reservoir type in the work area is conventional or unconventional. If it is conventional, the resource evaluation methods that can be used in the work area include: gas content analogy, basin simulation method, volume method, evaluation unit division method, ACCESS method, EUR analogy method. If it is unconventional, the resource evaluation methods that can be used in the work area include: gas content analogy, volume method, evaluation unit division method, ACCESS method, EUR analogy method; If the exploration and development degree is high, determine whether the oil and gas reservoir type in the work area is conventional or unconventional. If it is conventional, the resource evaluation methods that can be used in the work area include: material balance method, decline curve method, scale sequence method, FORSPAN method. If it is unconventional, the resource evaluation methods that can be used in the work area include: scale sequence method, decline curve method, FORSPAN method; Among the available resource evaluation methods, select the adopted resource volume evaluation method for the work area according to the selection rules corresponding to each resource volume evaluation method.

4. The method for evaluating oil and gas reservoir resources according to claim 3, wherein The selection rules corresponding to the resource volume evaluation method include: Judge whether the hydrocarbon expulsion coefficient of the source rock is included in the parameters of the work area that can be obtained, and whether the value of the organic matter thermal evolution maturity is greater than 0.5% or less than 4%. If so, select the saturated residual hydrocarbon method or TOC method or chloroform bitumen A method or total hydrocarbon method or hydrocarbon production rate method; Judge whether the sequence change value of the parameters in the work area within a predetermined period can be obtained. If so, select the basin simulation method; Judge whether the similarity of the hydrocarbon accumulation conditions between the work area and the corresponding calibration area is greater than the predetermined similarity, and whether the analogy coefficient between the work area and the calibration area can be determined. If so, select the area abundance analogy method, or the volume density analogy method, or the gas content analogy, or the EUR analogy method, or the volume rate method; Judge whether the gas content and oil content data of the work area can be obtained. If so, select the volume method; Judge whether the parameters of the work area to be used conform to the normal distribution law. If so, select the probability volume method; Judge whether the drilling and production data of more than the first predetermined number of wells in the work area can be obtained, whether the gas content or oil content data or single-well EUR can be obtained, whether the evaluation units in the work area have a definite boundary division, and whether the analogy coefficient can be obtained in the case where an analogy coefficient is required. If so, select the evaluation unit division method, or the ACCESS method, or the FORSPAN method; Judge whether the development data of more than the second predetermined number in the work area can be obtained, including at least: cumulative hydrocarbon production and water production, average formation pressure, and PVT physical properties parameters. If so, select the material balance method; Judge whether the production data within a predetermined period of the work area can be obtained, and whether the flow state of the corresponding produced fluid during the curve fitting time period is in a steady state or a pseudo-steady state in the case where curve fitting is required. If so, select the decline curve method; Judge whether there are more than a predetermined number of oil and gas fields or reservoirs in the work area, whether the reserves of the largest-scale oil and gas field or reservoir can be obtained, and whether the change rate of the scale decline of all oil and gas fields or reservoirs can be obtained. If so, select the scale sequence method; Judge whether the resource evaluation results of the work area of more than the third predetermined number of copies can be obtained. If so, select the Delphi method.

5. The method for evaluating oil and gas reservoir resources according to claim 3, wherein Before obtaining the exploration and development degree of the work area, determine the exploration and development degree of the work area. The method includes: Judge whether the work area already has medium-scale geological, geophysical exploration, deployment parameter wells and reference wells, and data of preferred prospective areas, and has calculated the predicted resource volume data based on them. If so, the exploration and development degree of the work area is low; Judge whether the work area already has large-scale geological, geophysical exploration, deployment of exploratory wells, and data of preferred favorable areas, and has calculated the geological resource volume data based on them. If so, the exploration and development degree of the work area is medium; Judge whether the work area already has geology, 3D seismic, drilling, deployment of evaluation wells, and data of preferred target areas, and has calculated the predicted, controlled, and proven reserve data based on them. If so, the exploration and development degree of the work area is high.

6. The method for evaluating oil and gas reservoir resources according to claim 1, wherein: The method for cleaning the data of all parameters includes: Establish box plots corresponding to each parameter required in the adopted resource evaluation method respectively; Use formula (1) to calculate the upper limit Ne of the effective data corresponding to each parameter. max , and use formula (2) to calculate the lower limit Ne of the valid data corresponding to each parameter min ; Delete the data above the corresponding upper limit value and the data below the corresponding lower limit value in the data included in each parameter to obtain the cleaned data; Ne max = N u + 1.5(N u - N b ) (1); Ne min = N b - 1.5(N u - N b ) (2); Where: N b is the value corresponding to the point where the peak of the distribution curve of the data group to be evaluated moves 25% to the left, and N u is the value corresponding to the point where the peak of the distribution curve of the data group to be evaluated moves 25% to the right.

7. The method for evaluating oil and gas reservoir resources according to claim 1, wherein The method for respectively determining the lower limit value of the data volume corresponding to each parameter in all the parameter data by using the significance test method includes: Step 11: Randomly divide all the data M of the parameter into a groups, and each group contains b data; Step 21: Determine the within-group mean square corresponding to each group of data and the between-group mean square between each group respectively; Step 31: Divide the between-group mean square by the corresponding within-group mean square to obtain the significance test statistic F corresponding to each group; Step 41: Compare the F with the theoretical significance test statistic F corresponding to the given M and a conditions, and determine that if F < F ɑ , then the lower limit value of the data volume corresponding to the parameter is b; otherwise, return to Step 1. ɑ ​ 8. The method for evaluating oil and gas reservoir resources according to claim 1, wherein The method for evaluating the oil and gas reservoir resources of the work area by using the selected resource evaluation method according to the cleaned parameter data includes: According to the cleaned parameter data, use the selected resource evaluation method to respectively determine the resource amounts of natural gas or petroleum corresponding to the work area, and determine the corresponding abundance values of natural gas or petroleum according to the resource amounts; Judge that if the abundance value of the natural gas is greater than 10, or the abundance value of the petroleum is greater than 300, the evaluation result of the oil and gas reservoir resources of the work area is excellent; Judge that if the abundance value of the natural gas is in the range of 2-10, or the abundance value of the petroleum is in the range of 50-300, the evaluation result of the oil and gas reservoir resources of the work area is medium; Judge that if the abundance value of the natural gas is less than 2, or the abundance value of the petroleum is less than 50, the evaluation result of the oil and gas reservoir resources of the work area is poor.

9. The method for evaluating oil and gas reservoir resources according to claim 1, characterized in that Before evaluating the oil and gas reservoir resources of the work area by using the selected resource evaluation method according to the cleaned parameter data, it further includes: Use the normality test method to determine whether the cleaned parameter data conforms to the normal distribution. If not, perform logarithmic transformation on it, and judge whether the parameter data after logarithmic transformation conforms to the normal distribution; If not, make a violin plot corresponding to the parameter data, and identify whether there is a multimodal distribution phenomenon in the parameter data through the violin plot. If so, group the parameter data. When using the selected resource evaluation method to evaluate the oil and gas reservoir resources of the work area, if there is a data relationship fitting in the resource evaluation method, the data relationship fitting needs to be performed on each group of the parameter data respectively; If not, when using the selected resource evaluation method to evaluate the oil and gas reservoir resources of the work area, if the resource evaluation method used has a data relationship fitting, a non-parametric test method needs to be used.

10. The oil and gas reservoir resource evaluation method according to any one of claims 1-9, characterized in that: If there is data fitting in the process of respectively determining the resource amounts of natural gas and petroleum corresponding to the work area by using the selected resource evaluation method, judge whether the goodness of fit of the data fitting meets the first predetermined requirement. If not, adjust the type of the data fitting until the first predetermined requirement is met; and / or; The method for judging whether the goodness of fit meets the first predetermined requirement includes: Perform a White test on the regression model of the data fitting to obtain the corresponding p value; Determine the corresponding confidence value according to the R 2 value in the regression model fitted with the said data; Judge whether r in the regression model of the data fitting is greater than 0.3, F is less than 0.05, VIF is less than 10, D-W is in the range of 1.7-2.3, the confidence level is greater than or equal to the predetermined value, and the p value is less than 0.

05. If all are yes, the data fitting meets the first predetermined requirement; and / or; The R in the regression model fitted according to the data 2 value, and the method for determining the corresponding credibility value includes: If the data fitting is a model established by several univariate regressions, the corresponding credibility value is calculated using formula (3). If the data fitting is a model established by multiple regressions, the corresponding credibility value is calculated using formula (4). Where: C is the credibility of several simple linear regressions, R 2 i is the determination coefficient of the i-th regression model, n i is the sample size of the independent variable in the i-th regression, m is the total number of regression models, N is the total number of independent variables in the m regression models, and i is the item number of the regression model; Where: C c is the confidence level of multiple regression, R 2 is the determination coefficient of the multiple regression model, β j is the determination coefficient of the j-th independent variable, k is the number of independent variables, j is the term number of the independent variable, and n is the total number of k independent variables.

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