Full-process management method and system based on shale oil

Abstract

The invention relates to the technical field of electric digital data processing, in particular to a shale oil-based full-process management method and system, and the method comprises the steps: obtaining a distribution data set and a reserve data set; uploading the distribution data set to the principal component analysis model to obtain a distribution rule, and uploading the reserve data set to the principal component analysis model to obtain a reserve rule; development process determination is executed based on the distribution rule and the reserve rule; the determined development process is uploaded to a grey prediction model, and a prediction result of the transverse penetration oil layer zone of the horizontal section well is obtained; on the basis of the time sequence model, combined with the target market environment, production result estimation is executed; obtaining a recession process result in combination with the estimated production result and the attenuation environment of the target market; and based on the recession process, executing life cycle management of the shale oil in the target area. According to the invention, effective management of the whole life cycle of shale oil is realized.

Description

Technical Field

[0001] This application relates to the technical field of electrical digital data processing, and in particular to a full-process management method based on shale oil, and also relates to a system. Background Art

[0002] As an unconventional oil and gas resource, shale oil has become a major strategic resource both internationally and domestically. However, due to the ineffective replenishment of formation energy, the production decline is large and the recovery rate is low during the development process of shale oil. Therefore, how to achieve the full-life cycle management of shale oil is directly related to the investment in drilling and completion and the development efficiency of horizontal wells.

[0003] In the prior art, for example, Chinese invention patents with publication numbers CN112814669A and CN113868975A generally manage only one item in the exploration link of shale oil. Although point-to-point control can be achieved, there are still obvious technical defects in the management of the full life cycle of shale oil. Summary of the Invention

[0004] The inventors found through research that the degree of geological reserve control is the ratio of the geological reserve controlled by horizontal wells to the actual geological reserve. The degree of geological reserve control in the development of shale oil horizontal wells is a key indicator for judging whether the development technical policy is reasonable, which is directly related to the design of horizontal well development parameters and particularly affects the investment in drilling and completion and the development efficiency of horizontal wells. Based on this, if reasonable management can be implemented for the full life cycle of shale oil, it is certain to effectively improve the development, production, etc. efficiency of shale oil and ultimately improve the development efficiency of the plant area.

[0005] The purpose of this application is to provide a full-process management method and system based on shale oil, which can solve the technical problem that the prior art cannot provide a full-process management method for shale oil that can effectively ensure the development efficiency of the plant area through the comprehensive linkage management of exploration, development, production, and decline stages.

[0006] According to one aspect of this application, a full-process management method based on shale oil is provided. This method is executed by a processor, and the middle platform connected to the exploration data memory calls the distribution and reserves of oil shale in the same type of area corresponding to previous geological surveys to obtain a distribution data set and a reserve data set;

[0007] Upload the distribution data set to the principal component analysis model to obtain the distribution law, and upload the reserve data set to the principal component analysis model to obtain the reserve law;

[0008] Determine the development process based on the distribution law and the reserve law;

[0009] Upload the determined development process to the grey prediction model to obtain the prediction result of the lateral penetration of the horizontal section well into the oil layer zone;

[0010] Based on the time series model and combined with the target market environment, perform the estimation of production results;

[0011] Combined with the estimated production results and the decay environment of the target market, obtain the decay process results;

[0012] Based on the decay process, perform the life cycle management of shale oil in the target area.

[0013] In some embodiments, the process of obtaining the distribution dataset and the reserve dataset by calling the distribution and reserves of oil shale in the same type of area corresponding to the past geological surveys is as follows:

[0014] Obtain the distribution and reserve data of oil shale in areas of the same type as the target exploration area within the past at least three years in the exploration data memory;

[0015] Perform data cleaning on the obtained data to obtain interference-free data;

[0016] Perform hierarchical calls on the interference-free data to obtain the distribution dataset and the reserve dataset.

[0017] In some embodiments, the process of hierarchical call is as follows: arrange the interference-free data according to the time series; perform the call based on the arrangement of the time series.

[0018] In some embodiments, the process of uploading the distribution dataset to the principal component analysis model to obtain the distribution law and uploading the reserve dataset to the principal component analysis model to obtain the reserve law is as follows:

[0019] Based on the middleware, upload the distribution dataset and the reserve dataset to the analysis module, and call the principal component analysis model in the analysis module;

[0020] Sort the distribution dataset according to the time series to obtain the distribution eigenvalue of the distribution dataset marked with the time series, and perform distribution eigenvalue filtering based on data denoising to obtain the optimal distribution eigenvalue;

[0021] Sort the reserve dataset according to the reserve series to obtain the reserve eigenvalue of the reserve dataset marked with the time series, and associate the optimal distribution eigenvalue corresponding to the reserve eigenvalue to obtain the optimal reserve data eigenvalue;

[0022] Based on the optimal distribution eigenvalue of the reserve eigenvalue and the optimal distribution eigenvalue, determine the distribution law;

[0023] Based on the optimal reserve data eigenvalue, obtain the reserve law.

[0024] In some embodiments, the process of determining the development process based on the distribution law and the reserve law is as follows:

[0025] Perform matching analysis on the distribution pattern and the funds to be invested in the target area. If the analysis result is a match, development will be carried out; if the analysis result is a mismatch, development will be terminated;

[0026] For the target area where development is terminated based on the distribution law, a matching analysis is performed based on the reserve law. If the analysis result is a match, development will continue; if the analysis result is a mismatch, development will be terminated.

[0027] In some embodiments, the process of uploading the determined development process to the grey prediction model to obtain the prediction result of the lateral penetration oil layer zone of the horizontal well is as follows:

[0028] The optimal reserve data characteristic value and the optimal distribution characteristic value corresponding to the development process are sequentially input into the GM (1, n) model to perform the prediction of the reserve data characteristic value and the distribution characteristic value of the target area;

[0029] According to the predicted reserve data characteristic values and distribution characteristic values of the target area, combined with the standard content, the results of the lateral penetration of the oil layer zone in the horizontal section wells corresponding to the target area are obtained.

[0030] In some embodiments, based on the time series model and in combination with the target market environment, the process of performing production result estimation is:

[0031] According to the market production demand of the target exploration area, demand ranking is performed based on the time series model to obtain demand ranking from high to low.

[0032] In some embodiments, the process of obtaining the decay process result by combining the estimated production result and the decay environment of the target market is as follows: performing a comparison between the decay environment of the decay stage and the estimated production result, and determining the positive or negative correlation of the comparison result.

[0033] In some embodiments, based on the decline process, the process of executing life cycle management of shale oil in the target area is: according to the positive correlation comparison results, feedback is given to execute the management of the entire life cycle of shale oil.

[0034] According to another aspect of the present application, a full-process management system based on shale oil is provided, the system comprising a processor and further comprising:

[0035] An exploration stage determination module, which is used to connect to the middle platform of the exploration data storage, call the distribution and reserves of oil-bearing shale in the same type of area corresponding to previous geological surveys, and obtain a distribution data set and a reserve data set; upload the distribution data set to the principal component analysis model to obtain the distribution law, and upload the reserve data set to the principal component analysis model to obtain the reserve law;

[0036] The development stage determination module is used to determine the development process based on the distribution law and the reserve law, and is also used to upload the determined development process to the grey prediction model to obtain the prediction result of the lateral penetration of the horizontal section well into the oil-bearing layer zone;

[0037] The production stage determination module is used to estimate the production results based on the time series model and in combination with the target market environment;

[0038] The decline stage determination module is used to obtain the decline process result by combining the estimated production results and the decay environment of the target market;

[0039] The execution module is used to perform the life cycle management of shale oil in the target area based on the decline process.

[0040] Compared with the prior art, the present application has the following advantages and beneficial effects: The middle platform connected to the exploration data memory calls the distribution and reserves of oil shale in the same type of area corresponding to the previous geological survey to obtain the distribution data set and the reserve data set, realizing the reasonable utilization of the previous data and ensuring the authenticity and reliability of the subsequent data; uploading the distribution data set to the principal component analysis model to obtain the distribution law, and uploading the reserve data set to the principal component analysis model to obtain the reserve law, laying a true and reliable foundation for the reasonable prediction of the distribution and reserves of shale oil and realizing effective prediction; determining the development process based on the distribution law and the reserve law, uploading the determined development process to the grey prediction model to obtain the prediction result of the lateral penetration of the horizontal section well into the oil-bearing layer zone, realizing the acquisition of the accurate lateral penetration result of the horizontal section well into the oil-bearing layer, and greatly reducing the waste of a large amount of human and material resources in this link in the prior art; estimating the production results based on the time series model and in combination with the target market environment, and obtaining the decline process result by combining the estimated production results and the decay environment of the target market, so as to truly obtain the decline result and ensure the management rationality. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 is the flowchart of the full-process management method of the present application;

[0043] Figure 2 is the structure diagram of the full-process management system of the present application;

[0044] Figure 3 It is a usage scenario diagram of the full-process management system of this application;

[0045] Figure 4 It is a real-shot diagram of at least one component used in combination with the full-process management system of this application. Detailed implementation manners

[0046] Next, the technical solutions in the embodiments of this application will be described clearly and completely in conjunction with the accompanying Figures 1-3 drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Unless otherwise defined, all terms used in this specification should have the same meaning as commonly understood by those skilled in the art to which this application belongs.

[0047] Embodiment 1

[0048] Figure 1 It is a flowchart of the full-process management method of this application, which is illustrated as a full-process management method based on shale oil and is executed by a processor. The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0049] Specifically, the method includes:

[0050] Connect the middle platform to the exploration data memory, and call the distribution and reserves of oil shale in the same type of area corresponding to the previous geological survey to obtain a distribution data set and a reserve data set.

[0051] Connect the middle platform to the exploration data memory. The exploration data memory is used to record and store relevant data such as previous shale oil development. The middle platform is the central control part of the storage, and calls and controls the stored relevant data. Specifically, in this embodiment, the distribution and reserves of oil shale in the same type of area corresponding to the previous geological survey are called. Among them, the same type of area refers to the previous area that is the same or similar to the area to be explored. The distribution of shale oil and the reserves of shale oil are the key indicators required for exploration and are also the key basis data for subsequent stages.

[0052] In some possible implementation manners, the distribution and reserve data of oil shale in areas of the same type as the target exploration area in the past at least three years in the exploration data memory are acquired; the acquired data is subjected to data cleaning to obtain interference-free data; the interference-free data is subjected to hierarchical calling to obtain a distribution data set and a reserve data set. The process of hierarchical calling is as follows: the interference-free data is arranged according to the time series; the calling is performed based on the arrangement according to the time series. It should be noted that at least three years of data are selected because the areas where shale oil is located are generally relatively active, and in active areas, due to the relatively rapid change of geological conditions, data within 3 to 5 years or even shorter may need to be selected to better reflect the latest situation. The data cleaning in this embodiment at least includes deleting edge-blurred data, removing similar data, and deleting invalid data. After obtaining the interference-free data, by arranging the interference-free data according to the time series and performing the calling based on the arrangement according to the time series, it can be understood that the interference-free data is arranged from far to near according to the corresponding year or recording time, and the calling is performed from far to near based on the arrangement result to achieve hierarchical calling.

[0053] The distribution data set is uploaded to the principal component analysis model to obtain the distribution law, and the reserve data set is uploaded to the principal component analysis model to obtain the reserve law.

[0054] According to the distribution data set, the relevant data is uploaded to the principal component analysis model by the processor to perform the acquisition of the distribution law, and the reserve data set is uploaded to the principal component analysis model by the processor to obtain the reserve law. The principal component analysis model is a PCA model, which is a statistical data analysis method used to identify the main variables in the data and eliminate redundancy, so as to discover the possible hidden information and laws in the data.

[0055] In some possible implementation manners, based on the middle platform, the distributed data set and the reserve data set are uploaded to the analysis module, and the principal component analysis model in the analysis module is called; the distributed data set is sorted according to the time series to obtain the distribution characteristic values of the distributed data set marked with the time series, and the distribution characteristic values are filtered based on data denoising to obtain the optimal distribution characteristic values; the reserve data set is sorted according to the reserve series to obtain the reserve characteristic values of the reserve data set marked with the time series, and the optimal distribution characteristic values corresponding to the reserve characteristic values are associated to obtain the optimal reserve data characteristic values; the distribution law is determined based on the optimal distribution characteristic values of the reserve characteristic values and the optimal distribution characteristic values; the reserve law is obtained based on the optimal reserve data characteristic values. It can be understood that the distribution characteristic values in this embodiment are distributed data marked with time stamps, and the time stamps here are time series from far to near. After obtaining the foregoing distributed data, by using the data denoising principle, the edge data (data with a relatively far time), fuzzy data, and specific data are deleted to obtain the required optimal distribution characteristic values. It can be understood that the criteria or principles for the denoising process need to be set manually in advance, and the setting can be made with reference to relevant geological exploration descriptions and standard documents. Similarly, the obtaining of the optimal reserve data characteristic values is the same as that of the optimal distribution characteristic values. Further, based on the optimal distribution characteristic values of the reserve characteristic values and the optimal distribution characteristic values, a comparison is performed, and the same optimal distribution characteristics are retained. Based on the same optimal distribution characteristics, the distribution law is determined to obtain the relevant distribution of the previous shale oil in the same type of area. At the same time, the reserve law is obtained based on the optimal reserve data characteristic values.

[0056] The development process is determined based on the distribution law and the reserve law. According to the distribution law and the reserve law, the development process in the development stage of the entire life cycle of shale oil and the development benefits of the factory area can be accurately determined.

[0057] In some possible implementation manners, the distribution law is matched and analyzed with the funds to be invested in the target area. If the analysis result is a match, development is carried out; if the analysis result is a mismatch, development is aborted. For example, when the analysis result shows less distribution, but the invested funds are 30% more than the average invested funds, the result is a mismatch and development is aborted. On the contrary, when the analysis result shows less distribution, but the invested funds are 30% less than the average invested funds, the result is a match and development is carried out. For the target area where development is aborted based on the distribution law, a matching analysis is performed based on the reserve law. If the analysis result is a match, development continues; if the analysis result is a mismatch, development is terminated. For example, when the analysis result shows less distribution, but the invested funds are 30% more than the average invested funds, and the reserve of this area is greater than 5% of the reserve ratio of the same type, the analysis result is a match and development continues. On the contrary, when the reserve of this area is less than 5% of the reserve ratio of the same type, the analysis result is a mismatch and development is terminated.

[0058] Upload the determined development process to the grey prediction model to obtain the prediction result of the lateral penetration of the horizontal section well into the oil-bearing layer zone. The grey prediction model is a prediction model established under the condition that part of the information is known. By performing correlation analysis on the known part of the information, the original data is generated into a sequence with certain regularity, and then the corresponding differential equation model is established to predict the research object.

[0059] In some possible implementation manners, the optimal reserve data eigenvalue and the optimal distribution eigenvalue corresponding to the determined development process are sequentially input into the GM(1,n) model to perform the prediction of the reserve data eigenvalue and the distribution eigenvalue of the target area. Among them, according to the steps of GM(1,1) grey prediction, the same steps are performed on the optimal reserve data eigenvalue and the optimal distribution eigenvalue. According to the predicted reserve data eigenvalue and distribution eigenvalue of the target area, combined with the standard content, the result of the lateral penetration of the horizontal section well in the corresponding target area into the oil-bearing layer zone is obtained. Among them, the horizontal section well is to adjust the wellbore to a nearly horizontal state after vertically or obliquely drilling to reach the oil layer, forming a horizontal wellbore in the oil layer. The lateral penetration of the horizontal section well into the oil-bearing layer zone is to extend the horizontal well section horizontally in the oil layer and pass through as many oil layers as possible to increase the contact area with the oil layer.

[0060] Based on the time series model, combined with the target market environment, perform the estimation of production results. The target market environment is the existing environment of the place where the corresponding target exploration area is located, which at least includes the peak and trough conditions of the production environment. The reason for combining the market environment in this embodiment is to ensure that the corresponding management is more accurate and real. In some possible implementation manners, according to the market production demand of the target exploration area, based on the time series model, perform the sorting of demand degrees and obtain the sorting of demand degrees from high to low.

[0061] Combined with the estimated production results and the decay environment of the target market, obtain the result of the decay process. The decay environment of the target market is the decay stage corresponding to the target exploration area and the decline situation of the shale oil production. In some possible implementation manners, according to the decay environment of the decay stage and the estimated production results, perform a comparison and judge the positive and negative correlation of the comparison result.

[0062] Based on the decay process, perform the life cycle management of shale oil in the target area. The full life cycle management is to perform the full process management. In some possible implementation manners, according to the positive correlation comparison result, feedback and perform the management of the full life cycle of shale oil.

[0063] Based on the full process management method of this embodiment, it can effectively realize and fill the control of the full life cycle of shale oil in the existing technology, and maximize the benefits for the relevant factory areas to the greatest extent.

[0064] Embodiment 2

[0065] Based on the same inventive concept as the shale oil-based full-process management method in the foregoing Embodiment 1, as Figure 2 shown, the present application also provides a shale oil-based full-process management system, which includes: a processor. At the same time, in combination with the attached Figure 4 , the system of this embodiment will, in some cases or scenarios, at least combine with Figure 4 the device shown to perform relevant processing and management of shale oil.

[0066] Exemplarily, the method can be divided into one or more modules, and one or more modules are stored in the memory and executed by the processor to complete the present application. One or more modules can be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program. For example, the computer program can be divided into an exploration stage determination module, a development stage determination module, a production stage determination module, a decline stage determination module, and an execution module. The specific functions of each module are as follows: The exploration stage determination module is used to connect to the middle platform of the exploration data memory, call the distribution and reserves of oil shale in the same type of area corresponding to the previous geological survey, and obtain a distribution data set and a reserve data set; upload the distribution data set to the principal component analysis model to obtain the distribution law, and upload the reserve data set to the principal component analysis model to obtain the reserve law; the development stage determination module is used to determine the development process based on the distribution law and the reserve law, and at the same time is used to upload the determined development process to the grey prediction model to obtain the prediction result of the lateral penetration of the horizontal section well into the oil layer zone; the production stage determination module is used to estimate the production result based on the time series model and in combination with the target market environment; the decline stage determination module is used to obtain the decline process result by combining the estimated production result and the decline environment of the target market; the execution module is used to perform the life cycle management of shale oil in the target area based on the decline process.

[0067] The specific examples of the shale oil-based full-process management method in the foregoing Embodiment 1 are equally applicable to the shale oil-based full-process management system of this embodiment. Through the foregoing detailed description of the shale oil-based full-process management method, those skilled in the art can clearly know the shale oil-based full-process management system in this embodiment. Therefore, for the sake of brevity of the specification, it will not be elaborated herein.

[0068] The foregoing has shown and described the basic principles, main features and advantages of the present application. For those skilled in the art, it is obvious that the present application is not limited to the details of the above-mentioned exemplary embodiments, and without departing from the spirit or basic features of the present application, the present application can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present application is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes that fall within the meaning and scope of the equivalent elements of the claims in the present application.

[0069] In addition, it should be understood that although this specification is described in terms of embodiments, not every embodiment only contains an independent technical solution. This narrative manner of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A full-process management method based on shale oil, which is executed by a processor, characterized in that the middle platform connected to the exploration data memory calls the distribution and reserves of oil shale in the same type of area corresponding to previous geological surveys, and obtains a distribution data set and a reserve data set; upload the distribution data set to the principal component analysis model to obtain the distribution law, and upload the reserve data set to the principal component analysis model to obtain the reserve law; determine the development process based on the distribution law and the reserve law; upload the determined development process to the grey prediction model to obtain the prediction result of the lateral penetration of the horizontal section well into the oil-bearing layer zone; perform production result estimation based on the time series model and combined with the target market environment; combine the estimated production result and the decay environment of the target market to obtain the decay process result; perform life cycle management of shale oil in the target area based on the decay process.

2. The method according to claim 1, wherein The process of calling the distribution and reserves of oil shale in the same type of area corresponding to previous geological surveys to obtain a distribution data set and a reserve data set is as follows: obtain the distribution and reserve data of oil shale in areas of the same type as the target exploration area in the exploration data memory for at least the past 3 years; perform data cleaning on the obtained data to obtain interference-free data; perform hierarchical calling on the interference-free data to obtain a distribution data set and a reserve data set.

3. The method according to claim 2, wherein The process of the hierarchical calling is: arrange the interference-free data according to the time series; perform calling based on the arrangement of the time series.

4. The method according to claim 1, wherein The process of uploading the distribution data set to the principal component analysis model to obtain the distribution law, and uploading the reserve data set to the principal component analysis model to obtain the reserve law is as follows: based on the middle platform, upload the distribution data set and the reserve data set to the analysis module, and call the principal component analysis model in the analysis module; sort the distribution data set according to the time series to obtain the distribution characteristic values of the distribution data set marked with the time series, and perform distribution characteristic value filtering based on data denoising to obtain the optimal distribution characteristic values; sort the reserve data set according to the reserve sequence to obtain the reserve characteristic values of the reserve data set marked with the time series, and associate the optimal distribution characteristic values corresponding to the reserve characteristic values to obtain the optimal reserve data characteristic values; determine the distribution law based on the optimal distribution characteristic values of the reserve characteristic values and the optimal distribution characteristic values; obtain the reserve law based on the optimal reserve data characteristic values.

5. The method according to claim 1, wherein The process of determining the development process based on the distribution law and the reserve law is as follows: perform matching analysis on the distribution law and the funds to be invested in the target area. If the analysis result is a match, perform development; if the analysis result is a mismatch, abort the development; perform matching analysis on the target area where the development is aborted based on the distribution law based on the reserve law. If the analysis result is a match, continue the development; if the analysis result is a mismatch, terminate the development.

6. The method according to claim 5, wherein The process of uploading the determined development process to the grey prediction model to obtain the prediction result of the lateral penetration of the horizontal section well into the oil-bearing layer zone is as follows: successively input the optimal reserve data characteristic values and the optimal distribution characteristic values corresponding to the determined development process into the GM(1,n) model to perform the prediction of the reserve data characteristic values and the distribution characteristic values of the target area; According to the characteristic values and distribution characteristic values of the reserve data of the predicted target area, and in combination with the standard content, the result of the lateral penetration of the oil-bearing layer in the horizontal section well of the corresponding target area is obtained.

7. The method according to claim 1, characterized in that, The process of performing production result prediction based on the time series model and in combination with the target market environment is as follows: According to the market production demand of the target exploration area, based on the time series model, perform demand degree ranking to obtain the demand degree ranking from high to low.

8. The method system according to claim 7, wherein, The process of obtaining the recession process result by combining the predicted production result and the decay environment of the target market is as follows: Compare the decay environment in the decay stage with the predicted production result, and judge the positive and negative correlation of the comparison result.

9. The method system according to claim 8, characterized in that, The process of performing life cycle management of shale oil in the target area based on the recession process is as follows: According to the positive correlation comparison result, feedback and perform the management of the entire life cycle of shale oil.

10. A full-process management system based on shale oil, the system includes a processor, characterized in that, It further includes: An exploration stage determination module, which is used to connect to the middle platform of the exploration data storage, call the distribution and reserves of oil shale in the same type of area corresponding to the previous geological survey, and obtain the distribution data set and the reserve data set; upload the distribution data set to the principal component analysis model to obtain the distribution law, and upload the reserve data set to the principal component analysis model to obtain the reserve law; A development stage determination module, which is used to determine the development process based on the distribution law and the reserve law, and is also used to upload the determined development process to the grey prediction model to obtain the prediction result of the lateral penetration of the oil-bearing layer in the horizontal section well; A production stage determination module, which is used to perform production result prediction based on the time series model and in combination with the target market environment; A recession stage determination module, which is used to obtain the recession process result by combining the predicted production result and the decay environment of the target market; An execution module, which is used to perform life cycle management of shale oil in the target area based on the recession process.

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