An evaluation method, device and medium for a middle-late stage heavy oil reservoir oil production method

Abstract

The embodiment of the specification discloses a method and device for evaluating oil extraction methods of middle-late stage heavy oil reservoirs and a medium, applied to the technical field of heavy oil reservoir oil extraction, and used for solving the problem that the existing methods lack standardized comprehensive evaluation of oil extraction methods. The method comprises the following steps: obtaining basic production data of each oil well in the current area to extract multidimensional change data, and determining a to-be-evaluated oil well in the production middle-late stage based on the multidimensional change data; determining the evaluation levels of multiple heavy oil reservoir oil extraction methods of the to-be-evaluated oil well according to the current production value of the current area; determining multiple evaluation indexes corresponding to each evaluation level based on historical evaluation data corresponding to each evaluation level; converting the evaluation indexes corresponding to each heavy oil reservoir oil extraction method into quantitative data based on a preset fuzzy evaluation method to determine the fuzzy scores of the evaluation indexes; and summarizing the fuzzy scores of the evaluation indexes in each evaluation level to determine the comprehensive evaluation of each heavy oil reservoir oil extraction method.

Description

TECHNICAL FIELD

[0001] The present specification relates to the technical field of heavy oil reservoir exploitation, and in particular to a method and device for evaluating a heavy oil reservoir production method in a middle-late stage and a medium. BACKGROUND

[0002] Heavy oil reservoir refers to an oil reservoir with high oil viscosity, and the viscosity of the crude oil is usually between 100-10000 mPa·s (density of 0.9-1.0 g / cm3). Compared with conventional light oil reservoirs, heavy oil reservoirs have poor oil flowability, are difficult to produce, and have low recovery. Heavy oil reservoirs usually include bitumen, heavy oil, and extra-heavy oil. Heavy oil reservoir production in a middle-late stage generally refers to a fast-waste oil well in a conventional sense or an oil well that produces little oil based on a conventional production method. At this time, other technical means need to be used for development and enhanced recovery because the conventional production method cannot fully produce the crude oil. Therefore, the evaluation of the production method at this time is an important link for realizing heavy oil production increase.

[0003] Currently, the evaluation of the heavy oil reservoir production method is generally based on the evaluation of the heavy oil reservoir production method by technical personnel. However, due to the complexity of the heavy oil reservoir production method, the evaluation of the recovery and economic benefits based on the human method is limited by the experience of the personnel, and the evaluation criteria are different, so it is difficult to objectively realize the comprehensive evaluation of the heavy oil reservoir production method. In addition, when the evaluation of the heavy oil reservoir production method is realized by simulating and predicting the production results of different production methods based on laboratory simulation and numerical simulation, the evaluation results have a certain deviation from the actual situation because the laboratory simulation and numerical simulation usually simplify the real reservoir conditions and the production process. Laboratory simulation and numerical simulation are often based on certain assumptions and premise conditions, including geological structure and fluid flow law. These assumptions and premise conditions may differ from the actual situation, thereby affecting the accuracy of the evaluation results. Therefore, how to comprehensively consider multiple influencing factors to evaluate the heavy oil reservoir production method and determine the current optimal heavy oil reservoir production method is a problem to be solved. SUMMARY

[0004] To solve the above technical problems, one or more embodiments of the present specification provide a method and device for evaluating a heavy oil reservoir production method in a middle-late stage and a medium.

[0005] One or more embodiments of the present specification adopt the following technical solutions:

[0006] One or more embodiments of the present specification provide a method for evaluating a heavy oil reservoir production method in a middle-late stage, which comprises:

[0007] obtaining basic production data of each oil well in a current area, extracting multidimensional change data in the basic production data, and determining an oil well to be evaluated in a late production stage based on the multidimensional change data;

[0008] determining evaluation levels of a plurality of heavy oil reservoir production methods of the oil well to be evaluated according to a current production value of the current area;

[0009] determining a plurality of evaluation indexes corresponding to each evaluation level based on historical evaluation data corresponding to each evaluation level;

[0010] converting the evaluation indexes corresponding to each heavy oil reservoir production method into quantitative data based on a preset fuzzy evaluation method, to determine fuzzy scores of the evaluation indexes;

[0011] summarizing the fuzzy scores of the evaluation indexes in each evaluation level, to determine comprehensive evaluations of the heavy oil reservoir production methods, so as to screen an optimal heavy oil reservoir production method.

[0012] Optionally, in one or more embodiments of the present specification, the multidimensional change data in the basic production data is extracted, and the oil well to be evaluated in the late production stage is determined based on the multidimensional change data, specifically including:

[0013] basic production data of the oil well to be evaluated is collected based on a preset collection period, and the basic production data is sorted based on collection time, to obtain a basic production data sequence; wherein the basic production data includes oilfield geological data, oil production process data, historical production data, water injection data, and reservoir pressure data;

[0014] differential values of adjacent collection times are calculated based on the basic production data sequence, and each differential value is classified based on a data type corresponding to the differential value, to obtain production dimension change data and technical dimension change data; wherein the production dimension change data includes production value, recovery ratio, remaining reservoir reserves, and reservoir pressure change value; and the technical dimension change data includes recovery method change data;

[0015] if the production dimension change data and the technical dimension change data both correspond to the late production stage based on platform period standard data corresponding to the production dimension change data and collection stage data corresponding to the technical dimension change data, it is determined that the oil well to be evaluated is in the late production stage.

[0016] Optionally, in one or more embodiments of the present specification, the evaluation levels of the plurality of heavy oil reservoir production methods of the oil well to be evaluated are determined according to the current production value of the current area, specifically including:

[0017] obtaining a current production situation of a current area, comparing a target production of the current area with the current production situation, determining a bottleneck level of the oil well to be evaluated, and determining that the bottleneck level is a first priority; wherein the bottleneck level is an evaluation level that limits the heavy oil production in the current area;

[0018] based on the target production of the current area and an oil production process corresponding to each heavy oil reservoir production method, determining a main level directly associated with the bottleneck level and a secondary level indirectly associated with the bottleneck level;

[0019] determining that the main level is a second priority and the secondary level is a third priority, and summarizing the bottleneck level, the main level and the secondary level as the evaluation level of each heavy oil reservoir production method; wherein the first priority is greater than the second priority, and the second priority is greater than the third priority.

[0020] Optionally, in one or more embodiments of the present specification, before determining the evaluation level of the multiple heavy oil reservoir production methods of the oil well to be evaluated according to the current production situation of the current area, the method further comprises:

[0021] determining the geological characteristics of the current heavy oil reservoir corresponding to the oil well to be evaluated based on the basic production data of the oil well to be evaluated;

[0022] determining multiple available heavy oil reservoir production methods of the oil well to be evaluated based on the geological characteristics and reservoir characteristics of the heavy oil reservoir;

[0023] determining the oil production equipment information and personnel information corresponding to each available heavy oil reservoir production method, and filtering the available heavy oil reservoir production methods based on the idle personnel and idle oil production equipment in the current area to obtain the multiple heavy oil reservoir production methods of the oil well to be evaluated.

[0024] Optionally, in one or more embodiments of the present specification, based on the historical evaluation data corresponding to each evaluation level, determining multiple evaluation indexes corresponding to each evaluation level, specifically comprising:

[0025] converting each historical evaluation data into a data vector, calculating the similarity between each data vector based on a cosine similarity algorithm, and performing data cleaning on the historical evaluation data based on the similarity to obtain analyzed historical evaluation data;

[0026] converting each analyzed historical evaluation data into a rank based on a Spearman correlation coefficient, and determining the correlation coefficient between each analyzed historical evaluation data according to the rank;

[0027] determine a correlation coefficient matrix among the historical evaluation data based on the correlation coefficients, and determine main historical evaluation data among the historical evaluation data based on the correlation coefficient matrix;

[0028] determine a plurality of evaluation indexes corresponding to each evaluation hierarchy according to a data type of the main historical evaluation data corresponding to each evaluation hierarchy.

[0029] Optionally, in one or more embodiments of the present specification, the evaluation indexes corresponding to each heavy oil reservoir production method are converted into quantitative data based on a preset fuzzy evaluation method to determine fuzzy scores of the evaluation indexes, specifically including:

[0030] determine a priority of each evaluation index in an evaluation hierarchy according to an evaluation hierarchy in which each evaluation index is located, and use the priority as a score coefficient of the evaluation index;

[0031] send the evaluation indexes of each evaluation hierarchy to a plurality of review terminals to receive fuzzy judgment degrees among the evaluation indexes in the same evaluation hierarchy returned by the plurality of review terminals;

[0032] compare the fuzzy judgment degrees among the evaluation indexes in the same evaluation hierarchy returned by the plurality of review terminals to extract controversial fuzzy judgment degrees;

[0033] create a trigonometric function for a controversial evaluation index corresponding to the controversial fuzzy judgment degrees to determine a membership degree corresponding to the controversial evaluation index based on the trigonometric function;

[0034] modify the controversial fuzzy judgment degrees based on the membership degree corresponding to the controversial evaluation index to obtain fuzzy judgment degrees among the evaluation indexes, and construct a fuzzy judgment matrix of the evaluation indexes in each evaluation hierarchy based on the fuzzy judgment degrees among the evaluation indexes;

[0035] calculate a maximum eigenvalue of the fuzzy judgment matrix according to the fuzzy judgment matrix to obtain a characteristic vector corresponding to the maximum eigenvalue as quantitative data of the evaluation indexes;

[0036] weight the quantitative data of the evaluation indexes based on the score coefficients of the evaluation indexes to determine fuzzy scores of the evaluation indexes.

[0037] Optionally, in one or more embodiments of the present specification, the fuzzy scores of the evaluation indexes in each evaluation hierarchy are summarized to determine a comprehensive evaluation of each heavy oil reservoir production method to facilitate screening of an optimal heavy oil reservoir production method, specifically including:

[0038] The fuzzy scores of the evaluation indexes in the same evaluation hierarchy are fused and calculated to obtain a comprehensive fuzzy evaluation of the evaluation hierarchy;

[0039] The comprehensive fuzzy evaluation is weighted according to the priority corresponding to the evaluation hierarchy to obtain a multi-dimensional score corresponding to each evaluation hierarchy, and a comprehensive evaluation of each heavy oil reservoir production mode is determined based on the multi-dimensional score corresponding to each evaluation hierarchy.

[0040] Optionally, in one or more embodiments of the present specification, the fuzzy scores of the evaluation indexes in each evaluation hierarchy are aggregated to determine a comprehensive evaluation of each heavy oil reservoir production mode, so that after the optimal heavy oil reservoir production mode is screened, the method further comprises:

[0041] According to the comprehensive evaluation of each heavy oil reservoir production mode, a label corresponding to each heavy oil reservoir production mode of the oil well to be evaluated is determined; wherein the label identifies the outstanding direction of the comprehensive evaluation of the heavy oil reservoir production mode;

[0042] If it is determined that the label corresponding to the current heavy oil reservoir production mode of the oil well to be evaluated does not conform to the current regional oil production strategy, the current heavy oil reservoir production mode is replaced based on the label.

[0043] One or more embodiments of the present specification provide an evaluation device for a heavy oil reservoir production mode in a middle-late stage, the device comprising:

[0044] at least one processor; and,

[0045] a memory in communication connection with the at least one processor; wherein,

[0046] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute any of the above-mentioned methods

[0047] One or more embodiments of the present specification provide a non-volatile computer storage medium storing computer executable instructions, which are configured to execute any of the above-mentioned methods.

[0048] The above-mentioned at least one technical solution adopted by the embodiments of the present specification can achieve the following beneficial effects:

[0049] The to-be-evaluated oil well in the middle and late production stage is determined according to the multi-dimensional change data, which facilitates subsequent evaluation and decision for the to-be-evaluated oil well in the middle and late production stage, and avoids the waste of computing power caused by invalid analysis. According to the current production value of the current area, the evaluation level of the to-be-evaluated oil well in the multi-dimensional heavy oil production mode is determined, and then the evaluation indexes of each evaluation level are determined, so as to realize the analysis of multi-dimensional evaluation data, evaluate the performance and effect of the to-be-evaluated oil well from different angles, and improve the comprehensiveness and comprehensiveness of evaluation. The fuzzy evaluation is converted into quantifiable data, which facilitates subsequent comparison and analysis. The comprehensive fuzzy evaluation is weighted according to the priority corresponding to the evaluation level, which helps to obtain the comprehensive evaluation score of each heavy oil reservoir production mode, and the comparison and decision adjustment are realized according to the quantifiable characteristics. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present specification or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments described in the present specification, and those skilled in the art can also obtain other drawings according to these drawings without creative labor. In the drawings:

[0051] Figure 1 A flowchart of an evaluation method of a middle and late heavy oil reservoir production mode provided by an embodiment of the present specification;

[0052] Figure 2 A structural diagram of an evaluation device of a middle and late heavy oil reservoir production mode provided by an embodiment of the present specification;

[0053] Figure 3 A structural diagram of a non-volatile storage medium provided by an embodiment of the present specification. DETAILED DESCRIPTION

[0054] The embodiments of the present specification provide an evaluation method, device and medium of a middle and late heavy oil reservoir production mode.

[0055] In order to enable personnel in the technical field to better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be described clearly and completely below in combination with the drawings in the embodiments of the present specification. Obviously, the described embodiments are only some embodiments of the present specification, not all embodiments. Based on the embodiments of the present specification, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present specification.

[0056] As Figure 1As shown, the embodiment of the present specification provides a flowchart of an evaluation method of a heavy oil reservoir production mode in a middle and late stage. The method comprises the following steps: Figure 1 As can be known from one or more embodiments of the present specification, the evaluation method of the heavy oil reservoir production mode in a middle and late stage comprises the following steps:

[0057] S101: Obtain basic production data of each oil well in a current area, to extract multi-dimensional change data in the basic production data, and determine a to-be-evaluated oil well in a production middle and late stage based on the multi-dimensional change data.

[0058] In order to accurately evaluate the production stage and performance of the oil well, so as to facilitate subsequent evaluation and decision for the to-be-evaluated oil well in the production middle and late stage, and avoid the problem of waste of computing power caused by invalid analysis. The present specification first obtains the basic production data of each oil well in the current area, extracts the multi-dimensional change data in the basic production data, which can reflect the changes of key indicators such as the yield, recovery rate, and water injection volume of the oil well, and then determines the to-be-evaluated oil well in the production middle and late stage according to the multi-dimensional change data.

[0059] Specifically, in one or more embodiments of the present specification, the multi-dimensional change data in the basic production data is extracted, and the to-be-evaluated oil well in the production middle and late stage is determined based on the multi-dimensional change data, which specifically comprises the following processes:

[0060] First, the basic production data of the to-be-evaluated oil well is collected based on a preset collection period, and the basic production data is sorted based on the collection time to obtain a basic production data sequence. It should be noted that the basic production data includes oilfield geological data, oil production process data, historical yield data, water injection data, reservoir pressure data, etc. Then, the difference values of adjacent collection times are calculated based on the basic production data sequence, and the data types corresponding to each difference value are classified to obtain yield dimension change data and technical dimension change data; it should be noted that the yield dimension change data is related data reflecting the heavy oil yield, including yield value, recovery ratio, remaining reservoir reserves, reservoir pressure change value, etc. The technical dimension change data is related data reflecting the process change and technical change in the collection process, including recovery mode change data, etc. If the yield dimension change data corresponds to the middle and late stage according to the platform period standard data corresponding to the yield dimension change data, and the collection stage data corresponding to the technical dimension change data is also in the middle and late stage, then it is determined that the to-be-evaluated oil well is in the production middle and late stage.

[0061] Through the collection and analysis of basic production data in the above process, the production situation of the oil well can be comprehensively understood, including geological data, oil production process data, historical production data, water injection data, reservoir pressure data, etc. This helps to determine the potential of the oil well, find problems and develop optimization schemes. By calculating the difference value and classification data, the production data can be divided into change data of the yield dimension and the technical dimension, further understanding the change situation of the heavy oil yield and the process. This helps to determine the production level of the oil well and understand the change situation of the production technology, so as to determine whether it is in the middle and late stage of production by combining the changes of the production level and the production technology, so as to evaluate the heavy oil production mode of the heavy oil reservoir in this stage, which helps to improve the production efficiency of the oil well in the middle and late stage.

[0062] S102: determining an evaluation level of the heavy oil production mode of the multiple heavy oil reservoirs of the oil well to be evaluated according to the current production value situation of the current area; wherein the evaluation level includes an economic level, a technical level, a management level and an environmental level.

[0063] Because different heavy oil production modes have different influences on the economic level, the technical level, the management level and the environmental level, bringing different benefits or losses, in order to realize the comprehensive evaluation of the multiple heavy oil production modes, and further help to determine the heavy oil production mode of the current area which is helpful to the development goal based on the evaluation result. In the embodiments of the present specification, the evaluation level of the multiple heavy oil production modes of the oil well to be evaluated is determined according to the current production value situation of the current area.

[0064] Specifically, in one or more embodiments of the present specification, the evaluation level of the multiple heavy oil production modes of the oil well to be evaluated is determined according to the current production value situation of the current area, specifically including the following processes:

[0065] The current production situation of the current area is obtained, the target production of the current area is compared with the current production situation, the bottleneck level of the oil well to be evaluated is determined, and the bottleneck level is determined as the first priority. It should be noted that the bottleneck level is the evaluation level that limits the heavy oil production in the current area. Then, based on the target production of the current area and the oil recovery process corresponding to each heavy oil reservoir recovery method, the main level directly associated with the bottleneck level and the secondary level indirectly associated with the bottleneck level are determined. Then, the main level is determined as the second priority, and the secondary level is determined as the third priority. The bottleneck level, the main level and the secondary level are summarized as the evaluation level of each heavy oil reservoir recovery method. It should be noted that the first priority is greater than the second priority, and the second priority is greater than the third priority. Assuming that the target production of the current area is 1000 barrels of heavy oil per day. Through analysis of the current production situation, it is found that the actual production is limited by the collection equipment and is full load with only 600 barrels of heavy oil. According to this comparison, it can be determined that the bottleneck level of the oil well to be evaluated is the equipment level. Then, according to the target production of the current area of 1000 barrels of heavy oil, the oil recovery process corresponding to each heavy oil reservoir recovery method is compared. Through analysis, it is found that the personnel operation technology is directly associated with the bottleneck level, that is, the technical level is directly associated with the bottleneck level, and the evaluation levels B and C are indirectly associated with the bottleneck level. At this time, the bottleneck level is determined as the first priority, the technical level is determined as the second priority, and the evaluation levels B and C are determined as the third priority.

[0066] In the specification, by comparing the production situation and the target production of the current area, the bottleneck level is determined as the first priority of the evaluation, which can help to quickly locate the problem and the field of key improvement, and improve the pertinence and efficiency of the evaluation. Based on the target production and the oil recovery process of each oil recovery method, the main level directly associated with the bottleneck level and the secondary level indirectly associated with the bottleneck level are determined, which can more comprehensively evaluate the effect and influencing factors of the oil reservoir recovery method. And by clearly defining the priority order of the evaluation level, it is helpful to better allocate resources and develop improvement strategies after evaluating the oil recovery method in the case of limited resources and time.

[0067] Further, in one or more embodiments of the specification, before determining the evaluation level of the plurality of heavy oil reservoir recovery methods of the oil well to be evaluated according to the current production situation of the current area, the method further includes the following process:

[0068] Firstly, based on the basic production data of the oil well to be evaluated, the geological characteristics of the current heavy oil reservoir corresponding to the oil well to be evaluated are determined. Then, based on the geological characteristics and the reservoir characteristics of the heavy oil reservoir, a plurality of available heavy oil reservoir production methods of the oil well to be evaluated are determined. The oil production equipment information and personnel information corresponding to each available heavy oil reservoir production method are determined to filter the available heavy oil reservoir production methods based on the idle personnel and idle oil production equipment in the current area, and obtain a plurality of heavy oil reservoir production methods of the oil well to be evaluated.

[0069] Because different production methods can be suitable for different geological conditions and reservoir characteristics. Therefore, based on the geological characteristics and the reservoir characteristics of the heavy oil reservoir, a plurality of available heavy oil reservoir production methods are determined, which can preliminarily screen the production methods that can be used by the oil well to be evaluated in the middle and later stages, thereby avoiding the problem of redundant evaluation caused by the fact that some production methods cannot be applied to the middle and later stages when a large number of production methods are evaluated. In addition, the oil production equipment information and personnel information corresponding to each available heavy oil reservoir production method are determined, which can better understand the execution and application of the production method. This helps to further evaluate the feasibility and operability of the production method, and then filters the available heavy oil reservoir production methods based on the idle personnel and idle oil production equipment in the current area, which determines a plurality of feasible heavy oil reservoir production methods for the oil well to be evaluated, and provides flexible selection of production methods and evaluation basis for the production methods for improving the heavy oil production value and production efficiency in the current area.

[0070] S103: Based on the historical evaluation data corresponding to each evaluation level, a plurality of evaluation indexes corresponding to each evaluation level are determined.

[0071] Specifically, after obtaining a plurality of evaluation levels of each heavy oil reservoir production method in the oil well to be evaluated based on the above step S102, in one or more embodiments of the present specification, based on the historical evaluation data corresponding to each evaluation level, a plurality of evaluation indexes corresponding to each evaluation level are determined, which specifically includes the following process:

[0072] In order to realize the cleaning process for the data, the historical evaluation data is converted into a data vector in the embodiments of the present specification, the similarity between the data vectors is calculated based on the cosine similarity algorithm, and the historical evaluation data is cleaned based on the similarity to obtain the to-be-analyzed historical evaluation data. Since there are multiple evaluation indexes in each evaluation level, such as the crude oil price index, the operation cost index, the investment recovery index and other multiple evaluatable indexes in the economic evaluation level, it is difficult to evaluate a large range when evaluating a large number of evaluation indexes respectively. Therefore, in order to reduce the complexity of the analysis data while ensuring the evaluation accuracy, the to-be-analyzed historical evaluation data is converted into a rank based on the Spearman correlation coefficient in the present specification, so as to determine the correlation coefficient between the to-be-analyzed historical evaluation data according to the rank. By converting the to-be-analyzed historical evaluation data into a rank based on the Spearman correlation coefficient, the complexity of the analysis data is reduced. Compared with directly evaluating multiple evaluation indexes, converting the data into a rank can simplify the analysis process and reduce the difficulty of correlation analysis and comparison between data. Then, the correlation coefficient matrix between the to-be-analyzed historical evaluation data is determined based on the correlation coefficient, so as to determine the main historical evaluation data in the to-be-analyzed historical evaluation data based on the correlation coefficient matrix. The correlation coefficient matrix between the to-be-analyzed historical evaluation data is determined based on the correlation coefficient, which can more comprehensively understand the correlation between the historical evaluation data. By analyzing the correlation coefficient matrix, the correlation degree between the evaluation indexes can be found, which helps to determine the main historical evaluation data and the key indexes. Then, according to the data type of the main historical evaluation data corresponding to each evaluation level, the multiple evaluation indexes corresponding to each evaluation level are determined. The process of determining the multiple evaluation indexes corresponding to each evaluation level can more comprehensively evaluate the effect and influencing factors of the evaluation level. By determining the multiple evaluation indexes, the performance and effect of the to-be-evaluated oil well can be evaluated from different angles, which improves the comprehensiveness and comprehensiveness of the evaluation.

[0073] S104: converting the evaluation indexes corresponding to each heavy oil reservoir production method into quantitative data based on the preset fuzzy evaluation method to determine the fuzzy scores of the evaluation indexes.

[0074] In order to comprehensively evaluate the performance of each production method in different indexes and perform multi-dimensional comparison and decision analysis, the evaluation indexes corresponding to each heavy oil reservoir production method are converted into quantitative data according to the pre-set fuzzy evaluation method in the embodiments of the present specification, so as to determine the fuzzy scores of the evaluation indexes.

[0075] Specifically, in one or more embodiments of the present specification, the evaluation indexes corresponding to each heavy oil reservoir production method are converted into quantitative data based on the preset fuzzy evaluation method to determine the fuzzy scores of the evaluation indexes, which specifically includes the following processes:

[0076] Firstly, according to the evaluation level of each evaluation index, the priority of each evaluation index in the evaluation level is determined, and the priority is used as the scoring coefficient of the evaluation index. Then, the evaluation indexes of each evaluation level are sent to multiple review terminals to receive the fuzzy judgment degrees between the evaluation indexes in the same evaluation level returned by the multiple review terminals. Since the review terminals have inconsistent review standards, there are some evaluation indexes with different fuzzy judgment degrees. At this time, the controversial fuzzy judgment degrees are extracted by comparing the fuzzy judgment degrees between the evaluation indexes in the same evaluation level returned by the multiple review terminals. A trigonometric function is created for the controversial evaluation indexes corresponding to each controversial fuzzy judgment degree to determine the membership degree of the controversial evaluation indexes based on the trigonometric function. Then, the controversial fuzzy judgment degrees are corrected according to the membership degree of the controversial evaluation indexes to obtain the fuzzy judgment degrees between the evaluation indexes, and a fuzzy judgment matrix of the evaluation indexes in each evaluation level is constructed based on the fuzzy judgment degrees between the evaluation indexes. The maximum eigenvalue of the fuzzy judgment matrix is calculated based on the fuzzy judgment matrix to obtain the characteristic vector corresponding to the maximum eigenvalue as the quantitative data of the evaluation indexes. Then, the quantitative data of the evaluation indexes are weighted according to the scoring coefficient of the evaluation indexes to determine the fuzzy scores of the evaluation indexes.

[0077] In the present specification, by determining the priority of the evaluation indexes, appropriate weights can be given to different evaluation indexes when calculating the fuzzy scores. This helps to more accurately reflect the importance and influence of different indexes in the evaluation results. By sending the evaluation indexes to multiple review terminals and comparing the fuzzy judgment degrees returned by different terminals, inconsistencies and disputes between the review terminals can be found. By extracting controversial evaluation indexes, the opinions and views of the review terminals can be more comprehensively considered, improving the objectivity and reliability of the evaluation results, and facilitating the unification of the review standards. By correcting the membership degree of the controversial evaluation indexes, the trade-offs and preferences of the review terminals can be more accurately reflected, improving the accuracy and reliability of the evaluation results. Based on the calculation of the maximum eigenvalue of the fuzzy judgment matrix, the quantitative data of the evaluation indexes can be determined. This helps to convert fuzzy evaluation into quantifiable data, facilitating subsequent comparison and analysis. And according to the scoring coefficient of the evaluation indexes, the importance and priority of different evaluation indexes can be considered comprehensively.

[0078] S105: Aggregate the fuzzy scores of the evaluation indexes in each evaluation level to determine the comprehensive evaluation of each heavy oil reservoir production method, so as to screen the optimal heavy oil reservoir production method.

[0079] After obtaining the fuzzy scores of the evaluation indexes in each evaluation hierarchy according to the step S104, the fuzzy scores of the evaluation indexes in each evaluation hierarchy are summarized to realize the comprehensive evaluation of each heavy oil reservoir production method, so as to screen the optimal heavy oil reservoir production method. Specifically, in one or more embodiments of the present specification, the fuzzy scores of the evaluation indexes in each evaluation hierarchy are summarized to determine the comprehensive evaluation of each heavy oil reservoir production method, specifically including the following processes:

[0080] Firstly, the fuzzy scores of the evaluation indexes in the same evaluation hierarchy are combined and operated to obtain the comprehensive fuzzy evaluation of the evaluation hierarchy. It should be noted that the fuzzy combination operation is used to combine the fuzzy scores of the multiple evaluation indexes in the same evaluation hierarchy to obtain the comprehensive fuzzy evaluation of the hierarchy, which can be the maximum minimum operation mode or weighted average operation, etc., which is not specifically limited here. Then, the comprehensive fuzzy evaluation is weighted processed according to the priority corresponding to the evaluation hierarchy to obtain the multi-dimensional score corresponding to each evaluation hierarchy, and the comprehensive evaluation of each heavy oil reservoir production method is determined based on the multi-dimensional score corresponding to each evaluation hierarchy. According to the priority corresponding to the evaluation hierarchy, the comprehensive fuzzy evaluation is weighted processed, which can reflect the importance of different evaluation indexes and more accurately perform the comprehensive evaluation. Based on the multi-dimensional score corresponding to each evaluation hierarchy, the comprehensive evaluation of each heavy oil reservoir production method is determined, which can comprehensively consider multiple evaluation indexes to obtain a more comprehensive and objective evaluation result. In addition, through the fuzzy score and the weighted processing, the comprehensive evaluation score of each heavy oil reservoir production method can be finally obtained, which has the quantifiable characteristic and is convenient for comparison and decision-making.

[0081] Further, in one or more embodiments of the present specification, after the fuzzy scores of the evaluation indexes in each evaluation level are aggregated to determine the comprehensive evaluation of each heavy oil reservoir production method, in order to achieve the effect of increasing production for the heavy oil reservoirs in the middle and later stages, the method further comprises: determining the label corresponding to each heavy oil reservoir production method of the to-be-evaluated oil well according to the comprehensive evaluation of each heavy oil reservoir production method; wherein it can be understood that the label indicates the outstanding direction of the comprehensive evaluation of the heavy oil reservoir production method. If it is determined that the label corresponding to the current heavy oil reservoir production method of the to-be-evaluated oil well does not conform to the current regional production strategy, then the current heavy oil reservoir production method is replaced according to the corresponding production method called by the label. For example, in a certain application scenario: according to the results of the comprehensive evaluation, labels are given to each heavy oil reservoir production method, and these labels can represent the outstanding direction of the comprehensive evaluation results. For example, we can use labels such as "A", "B", "C", etc. to represent different evaluation levels, where "A" represents the best and "C" represents the worst. Assuming that the comprehensive evaluation result shows that the comprehensive evaluation of the production method of a certain heavy oil reservoir is "A", but the label of the method currently used in the reservoir is "C". According to our production strategy, we want to use a better method to increase production. Then we can call the production method with label "A", such as horizontal well, to replace the current heavy oil reservoir production method to achieve the effect of increasing production. By labeling the comprehensive evaluation results and selecting the appropriate production method according to the label, we can better achieve the effect of increasing production for heavy oil reservoirs in the middle and later stages based on the current best heavy oil reservoir production method, and conform to the current regional production strategy.

[0082] As shown in Figure 2 , the present specification embodiment provides a structural schematic diagram of an evaluation device for heavy oil reservoir production methods in the middle and later stages, which comprises Figure 2 It can be known that the device comprises:

[0083] at least one processor; and,

[0084] a memory in communication connection with the at least one processor; wherein,

[0085] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute any of the above-mentioned methods.

[0086] As shown in Figure 3 , the present specification embodiment provides a structural schematic diagram of a non-volatile storage medium. As can be known in the present specification one or more embodiments, a non-volatile storage medium stores computer executable instructions 301, and the computer executable instructions 301 can execute any of the above-mentioned methods. Figure 3 ​

[0087] Each of the various embodiments in this specification are described in a progressive manner, and the same or similar parts among the various embodiments can be mutually referred to, and each of the various embodiments focuses on the differences from other embodiments. In particular, for the device, apparatus, and non-transitory computer storage medium embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0088] The above describes specific embodiments of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims can be performed in a different order than the order in which they are recited and still achieve desirable results. In addition, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In some implementations, multitasking and parallel processing can be advantageous or necessary.

[0089] The above only describes one or more embodiments of the present specification and is not intended to limit the present specification. One or more embodiments of the present specification can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of one or more embodiments of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. An evaluation method for oil recovery methods in mid-to-late stage heavy oil reservoirs, characterized in that, The method includes: The basic production data of each oil well in the current area is obtained to extract multi-dimensional change data from the basic production data, and oil wells to be evaluated that are in the middle and late stages of production are identified based on the multi-dimensional change data. The multi-dimensional change data includes: production dimension change data and technical dimension change data; the production dimension change data includes: production value, recovery ratio, remaining reservoir reserves, and reservoir pressure change value; the technical dimension change data includes: recovery method change data. Based on the current production value of the current region, the evaluation levels of the oil recovery methods for multiple heavy oil reservoirs of the oil wells to be evaluated are determined; wherein, the evaluation levels include: economic level, technical level, management level, and environmental level; Based on the historical evaluation data corresponding to each evaluation level, multiple evaluation indicators corresponding to each evaluation level are determined. Based on a pre-set fuzzy evaluation method, the evaluation indicators corresponding to each of the heavy oil reservoir production methods are converted into quantitative data to determine the fuzzy score of each evaluation indicator. Specifically, according to the evaluation level of each evaluation indicator, the priority of the evaluation level of each evaluation indicator is determined, and the priority is used as the scoring coefficient of the evaluation indicator. The quantitative data of the evaluation indicators are weighted based on the scoring coefficient of the evaluation indicators to determine the fuzzy score of each evaluation indicator. By summarizing the fuzzy scores of the evaluation indicators in each evaluation level, a comprehensive evaluation of the oil recovery methods for each heavy oil reservoir is determined, so as to select the optimal oil recovery method for heavy oil reservoirs. Specifically, determining the evaluation level of the oil recovery methods for multiple heavy oil reservoirs of the oil well to be evaluated based on the current production value of the current region includes: Obtain the current production value of the current region, compare the target production value of the current region with the current production value, determine the bottleneck level of the oil well to be evaluated, and determine the bottleneck level as the first priority; wherein, the bottleneck level is the evaluation level that limits the heavy oil production value of the current region. Based on the target output value of the current region and the oil production process corresponding to each heavy oil reservoir production method, determine the primary level directly associated with the bottleneck level and the secondary level indirectly associated with the bottleneck level. The primary level is determined as the second priority, and the secondary level is determined as the third priority. The bottleneck level, primary level, and secondary level are summarized as the evaluation level for each heavy oil reservoir oil recovery method. The first priority is greater than the second priority, and the second priority is greater than the third priority.

2. The evaluation method for oil recovery methods in mid-to-late stage heavy oil reservoirs according to claim 1, characterized in that, The step of extracting multi-dimensional change data from the basic production data and identifying oil wells to be evaluated that are located in the middle and late stages of production based on the multi-dimensional change data specifically includes: The basic production data of the oil well to be evaluated is collected based on a preset collection cycle, and the basic production data is sorted based on the collection time to obtain a basic production data sequence; wherein, the basic production data includes: oilfield geological data, oil production process data, historical production data, water injection data, and reservoir pressure data. Based on the basic production data sequence, the difference values ​​between adjacent acquisition times are calculated, and the data are classified according to the data type corresponding to each difference value to obtain production dimension change data and technical dimension change data; wherein, the production dimension change data includes: production value, recovery ratio, remaining reservoir reserves, and reservoir pressure change value; the technical dimension change data includes: recovery method change data; If, based on the standard data of the plateau period corresponding to the production dimension change data and the data of the collection stage corresponding to the technology dimension change data, it is determined that both the production dimension change data and the technology dimension change data correspond to the mid-to-late stage of production, then the oil well to be evaluated is determined to be in the mid-to-late stage of production.

3. The evaluation method for oil recovery methods in mid-to-late stage heavy oil reservoirs according to claim 1, characterized in that, Before determining the evaluation level of the oil recovery methods for multiple heavy oil reservoirs of the oil well to be evaluated based on the current production value of the current region, the method further includes: Based on the basic production data of the oil well to be evaluated, the geological characteristics of the current heavy oil reservoir corresponding to the oil well to be evaluated are determined. Based on the geological features and reservoir characteristics of the heavy oil reservoir, multiple viable heavy oil reservoir production methods are determined for the oil well to be evaluated. The oil production equipment and personnel information corresponding to each available heavy oil reservoir production method are determined, and the available heavy oil reservoir production methods are filtered based on the idle personnel and idle oil production equipment in the current area to obtain multiple heavy oil reservoir production methods for the oil well to be evaluated.

4. The evaluation method for oil recovery methods in mid-to-late stage heavy oil reservoirs according to claim 1, characterized in that, The determination of multiple evaluation indicators corresponding to each evaluation level based on historical evaluation data specifically includes: The historical evaluation data are converted into data vectors, and the similarity between the data vectors is calculated based on the cosine similarity algorithm. The historical evaluation data is then cleaned based on the similarity to obtain the historical evaluation data to be analyzed. The historical evaluation data to be analyzed are converted into ranks based on the Spearman correlation coefficient, and the correlation coefficient between the historical evaluation data to be analyzed is determined according to the ranks. Based on the correlation coefficient, a correlation coefficient matrix is ​​determined between each of the historical evaluation data to be analyzed, so as to determine the main historical evaluation data in the historical evaluation data to be analyzed based on the correlation coefficient matrix; Based on the data type of the main historical evaluation data corresponding to each evaluation level, multiple evaluation indicators corresponding to each evaluation level are determined.

5. The evaluation method for oil recovery methods in mid-to-late stage heavy oil reservoirs according to claim 1, characterized in that, The method of converting the evaluation indicators corresponding to each of the heavy oil reservoir production methods into quantitative data based on a pre-set fuzzy evaluation approach to determine the fuzzy score of each evaluation indicator specifically includes: The evaluation indicators of each evaluation level are sent to multiple review terminals to receive the degree of fuzzy judgment between the evaluation indicators of the same evaluation level returned by the multiple review terminals. The degree of fuzzy judgment among the evaluation indicators at the same evaluation level returned by multiple review terminals is compared to extract the degree of disputed fuzzy judgment. A trigonometric function is created for the dispute evaluation index corresponding to the degree of fuzzy judgment of the dispute, so as to determine the membership degree corresponding to the dispute evaluation index based on the trigonometric function; The degree of fuzzy judgment of the dispute is corrected based on the membership degree corresponding to the dispute evaluation index to obtain the degree of fuzzy judgment between each evaluation index, and a fuzzy judgment matrix of the evaluation index in each evaluation level is constructed based on the degree of fuzzy judgment between each evaluation index. The maximum eigenvalue of the fuzzy judgment matrix is ​​calculated based on the fuzzy judgment matrix, and the eigenvector corresponding to the maximum eigenvalue is used as the quantitative data of the evaluation index.

6. The evaluation method for oil recovery methods in mid-to-late stage heavy oil reservoirs according to claim 1, characterized in that, The fuzzy scores of the evaluation indicators at each evaluation level are summarized to determine the comprehensive evaluation of each heavy oil reservoir recovery method, specifically including: A fuzzy union operation is performed on the fuzzy scores of each evaluation indicator in the same evaluation level to obtain a comprehensive fuzzy evaluation of the evaluation level. The comprehensive fuzzy evaluation is weighted according to the priority corresponding to the evaluation level to obtain the multi-dimensional score corresponding to each evaluation level. Based on the multi-dimensional score corresponding to each evaluation level, the comprehensive evaluation of the oil recovery mode of each heavy oil reservoir is determined.

7. The evaluation method for oil recovery methods in mid-to-late stage heavy oil reservoirs according to claim 1, characterized in that, After summarizing the fuzzy scores of the evaluation indicators at each evaluation level to determine the comprehensive evaluation of each heavy oil reservoir recovery method, the method further includes: Based on the comprehensive evaluation of the oil recovery methods for each heavy oil reservoir, the labels corresponding to the oil recovery methods for each heavy oil reservoir of the oil well to be evaluated are determined; wherein, the labels indicate the prominent directions of the comprehensive evaluation of the oil recovery methods for heavy oil reservoirs; If it is determined that the label corresponding to the current heavy oil reservoir production method of the oil well to be evaluated does not conform to the current production strategy of the region, then the current heavy oil reservoir production method is replaced based on the label.

8. An evaluation device for oil recovery methods in mid-to-late stage heavy oil reservoirs, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1-7.

9. A non-volatile storage medium storing computer-executable instructions, said computer-executable instructions being capable of: performing the method described in any one of claims 1-7.

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