CN115759786A - Method, device, equipment and storage medium for determining oil and gas reservoir development scheme - Google Patents
Method, device, equipment and storage medium for determining oil and gas reservoir development scheme Download PDFInfo
- Publication number
- CN115759786A CN115759786A CN202211582020.5A CN202211582020A CN115759786A CN 115759786 A CN115759786 A CN 115759786A CN 202211582020 A CN202211582020 A CN 202211582020A CN 115759786 A CN115759786 A CN 115759786A
- Authority
- CN
- China
- Prior art keywords
- development
- index
- oil
- data
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000011161 development Methods 0.000 title claims abstract description 354
- 238000000034 method Methods 0.000 title claims abstract description 150
- 238000003860 storage Methods 0.000 title claims abstract description 9
- 238000004519 manufacturing process Methods 0.000 claims abstract description 50
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 23
- 238000000605 extraction Methods 0.000 claims abstract description 8
- 238000013461 design Methods 0.000 claims abstract description 5
- 229930195733 hydrocarbon Natural products 0.000 claims description 32
- 150000002430 hydrocarbons Chemical class 0.000 claims description 32
- 238000013473 artificial intelligence Methods 0.000 claims description 31
- 239000004215 Carbon black (E152) Substances 0.000 claims description 30
- 238000013528 artificial neural network Methods 0.000 claims description 26
- 238000004088 simulation Methods 0.000 claims description 24
- 238000012545 processing Methods 0.000 claims description 21
- 238000004458 analytical method Methods 0.000 claims description 14
- 239000003129 oil well Substances 0.000 claims description 8
- 238000012800 visualization Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims 1
- 230000008901 benefit Effects 0.000 abstract description 5
- 239000007789 gas Substances 0.000 description 90
- 238000010586 diagram Methods 0.000 description 10
- 230000007246 mechanism Effects 0.000 description 6
- 238000004590 computer program Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000011160 research Methods 0.000 description 3
- 238000005553 drilling Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012407 engineering method Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 230000002829 reductive effect Effects 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000013475 authorization Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011234 economic evaluation Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a method, a device, equipment and a storage medium for determining an oil-gas reservoir development scheme, and relates to the field of oil-gas fields. Acquiring relevant data of a target oil and gas reservoir, wherein the relevant data comprises exploration data, production data, oil extraction engineering data and ground engineering data; designing a template based on the development schemes, and obtaining a plurality of development schemes corresponding to the target oil and gas reservoir according to relevant data; aiming at each development scheme in the plurality of development schemes, determining index values of the development schemes corresponding to different development indexes respectively according to related data, wherein the development indexes comprise an oil production index, a water content index, an oil production decrement index and a pressure index; and determining the target development scheme of the target oil and gas reservoir in the plurality of development schemes according to the index values of the plurality of development schemes corresponding to different development indexes respectively. The method has the advantages that the multiple development schemes can be automatically obtained on the basis of the development scheme design template, more assumed conditions do not need to be preset during each prediction, the operation is simple, and the oil and gas reservoir development schemes can be efficiently determined.
Description
Technical Field
The application relates to the technical field of oil and gas reservoir exploitation, in particular to a method, a device, equipment and a storage medium for determining an oil and gas reservoir development scheme.
Background
A reservoir is a trap that collects a certain amount of oil and gas. If only oil is accumulated in the trap, the trap is called a pure oil reservoir (or oil reservoir), and only natural gas is accumulated, and the trap is called a pure gas reservoir (or gas reservoir). An industrial reservoir is said to be a reservoir when the amount of hydrocarbons accumulated is sufficient for industrial production, and a non-industrial reservoir is said to be the reverse. The reservoir is the basic unit of oil and gas accumulation in the earth's crust, and one reservoir exists in an independent trap. Due to the high value of hydrocarbons, it is very important how to develop hydrocarbon reservoirs.
In the related technology, the method for determining the oil and gas reservoir development scheme mainly depends on oil and gas reservoir numerical simulation software, and through set model conditions, different well types, different development modes and the like are simulated to predict development indexes, so that the optimal oil and gas reservoir development scheme is obtained, but the problem of low efficiency exists.
Accordingly, there is a need for a method for efficiently determining reservoir development scenarios.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for determining an oil and gas reservoir development scheme, which are used for solving the problem of low efficiency in determining the oil and gas reservoir development scheme.
In a first aspect, the present application provides a method for determining a reservoir development scenario, comprising: acquiring relevant data of a target oil and gas reservoir, wherein the relevant data comprises exploration data, production data, oil extraction engineering data and ground engineering data; designing a template based on the development schemes, and obtaining a plurality of development schemes corresponding to the target oil and gas reservoir according to relevant data; aiming at each development scheme in the plurality of development schemes, determining index values of the development schemes corresponding to different development indexes respectively according to related data, wherein the development indexes comprise an oil production index, a water content index, an oil production decrement index and a pressure index; and determining the target development scheme of the target oil and gas reservoir in the plurality of development schemes according to the index values of the plurality of development schemes corresponding to different development indexes respectively.
In one possible embodiment, determining a target development plan of a target hydrocarbon reservoir among a plurality of development plans according to index values of a plurality of development plans corresponding to different development indexes, respectively, includes: and inputting the index values of the plurality of development schemes corresponding to different development indexes into the multilayer forward neural network for intelligent classification of the development indexes to obtain the target development scheme output by the multilayer forward neural network.
In one possible embodiment, determining index values of the development schemes corresponding to different development indexes according to the related data includes: and determining index values of the development schemes corresponding to different development indexes respectively according to related data by adopting a set prediction method, wherein the set prediction method comprises a conventional development index prediction method, a digital twin model prediction method, a rapid numerical simulation prediction method and an artificial intelligence prediction method, the artificial intelligence prediction method is to apply big data to learn the conventional development index prediction method to obtain a development index prediction analysis model, and the development index prediction analysis model is used for determining the development indexes corresponding to the development schemes.
In one possible embodiment, the setting prediction method is provided in the form of a widget in a data processing platform deployed in a server.
In one possible embodiment, the related data is stored in a general system, and the obtaining of the related data of the target hydrocarbon reservoir includes: and acquiring related data from the system through the data processing platform.
In one possible embodiment, the method for determining a reservoir development plan further comprises: after the index value is determined, at least one of a reservoir annual development index prediction table, a well annual development index prediction table, and a well annual production index prediction table is formed, and the formed prediction table is subjected to front-end visualization.
In a second aspect, the present application provides a reservoir development scenario determination apparatus, comprising:
the acquisition module is used for acquiring relevant data of the target hydrocarbon reservoir, wherein the relevant data comprises exploration data, production data, oil extraction engineering data and ground engineering data;
the processing module is used for designing a template based on the development scheme and obtaining a plurality of development schemes corresponding to the target oil and gas reservoir according to the related data;
the first determining module is used for determining index values of the development schemes corresponding to different development indexes respectively according to relevant data aiming at each development scheme in the plurality of development schemes, wherein the development indexes comprise an oil production index, a water content index, an oil production decrement index and a pressure index;
and the second determining module is used for determining the target development scheme of the target oil and gas reservoir in the plurality of development schemes according to the index values of the plurality of development schemes respectively corresponding to different development indexes.
In a possible implementation manner, the second determining module is specifically configured to: and inputting the index values of the plurality of development schemes corresponding to different development indexes into the multilayer forward neural network for intelligent classification of the development indexes to obtain the target development scheme output by the multilayer forward neural network.
In a possible implementation manner, the first determining module is specifically configured to: and determining index values of the development schemes corresponding to different development indexes respectively according to related data by adopting a set prediction method, wherein the set prediction method comprises a conventional development index prediction method, a digital twin model prediction method, a rapid numerical simulation prediction method and an artificial intelligence prediction method, the artificial intelligence prediction method is to apply big data to learn the conventional development index prediction method to obtain a development index prediction analysis model, and the development index prediction analysis model is used for determining the development indexes corresponding to the development schemes.
In one possible embodiment, the setting prediction method is provided in the form of a widget in a data processing platform, which is deployed in a server.
In a possible implementation manner, the obtaining module is specifically configured to: and acquiring related data from the system through the data processing platform.
In one possible implementation, the first determining module may be further configured to: after the index value is determined, at least one of a reservoir annual development index prediction table, an oil well annual development index prediction table, and a water well annual production index prediction table is formed, and the formed prediction table is subjected to front-end visualization.
In a third aspect, the present application provides an electronic device, comprising: a memory and a processor. The memory is used for storing program instructions; the processor is for invoking program instructions in the memory to perform the reservoir development scenario determination method of the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium having stored therein computer executable instructions, which when executed, implement the method for determining a reservoir development plan of the first aspect.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, is operable to carry out the method of determining a reservoir development scenario of the first aspect.
According to the method, the device, the equipment and the storage medium for determining the oil and gas reservoir development scheme, relevant data of a target oil and gas reservoir are obtained, wherein the relevant data comprise exploration data, production data, oil extraction engineering data and ground engineering data; designing a template based on the development schemes, and obtaining a plurality of development schemes corresponding to the target oil and gas reservoir according to relevant data; aiming at each development scheme in the plurality of development schemes, determining index values of the development schemes corresponding to different development indexes respectively according to related data, wherein the development indexes comprise an oil production index, a water content index, an oil production decrement index and a pressure index; and determining a target development scheme of the target oil and gas reservoir in the plurality of development schemes according to the index values of the plurality of development schemes corresponding to different development indexes respectively. The method comprises the steps that a template is designed on the basis of a development scheme, and a plurality of development schemes corresponding to a target oil and gas reservoir obtained by aiming at related data can include a plurality of development conditions, so that more assumed conditions do not need to be preset; furthermore, the method and the system can be used for executing continuous operation by the server, do not need related personnel to adjust parameters, are simple to operate, and can be used for determining the target development scheme of the oil and gas reservoir quickly and efficiently.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic view of an application scenario of a method for determining a reservoir development plan according to an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram of a method for reservoir development scenario determination provided by an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a digital twin model prediction method according to an embodiment of the present disclosure;
FIG. 4 is a schematic structural diagram of a fast numerical simulation prediction method according to an embodiment of the present application;
FIG. 5 is a schematic structural diagram of an artificial intelligence prediction method according to an embodiment of the present disclosure;
FIG. 6 is a schematic structural diagram of a multi-layer forward neural network provided in an embodiment of the present application;
FIG. 7 is a schematic structural diagram of a reservoir development scenario determination apparatus provided in an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terms first, second and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged under appropriate circumstances such that the embodiments of the application described herein may be implemented, for example, in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, article, or apparatus.
In the related technology, the method for determining the oil and gas reservoir development scheme mainly depends on oil and gas reservoir numerical simulation software, and is limited by the fact that the oil and gas reservoir numerical simulation software is expensive, related personnel are required to continuously perform parameter adjusting operation, and the operation is complex, so that the efficiency is low when the oil and gas reservoir development scheme is determined; on the other hand, each oil and gas reservoir development scheme is only related to the corresponding oil and gas reservoir, more assumed conditions need to be preset for single oil and gas reservoir data, and the problem of poor expansibility is obvious.
In addition, along with the continuous deepening of the development work of the oil and gas field, mass data, big data and artificial intelligence are generated, power is provided for the intelligent development of the oil and gas field development, and the intelligent development of the oil and gas field development is greatly promoted. At present, many experts and scholars have made a look at the application of new technologies such as big data, artificial intelligence and machine learning in the field of oil and gas fields, and the development of oil and gas field intelligence is considered to be the technological development direction. However, when the method for determining the oil and gas reservoir development scheme based on the big data is adopted, a large amount of time is wasted for obtaining the mass data.
In order to solve the problems, the application provides a method for determining an oil and gas reservoir development scheme, mass data can be rapidly and efficiently acquired based on a dream cloud platform, index values of different development indexes are predicted for different oil and gas reservoir development schemes according to the acquired mass data, and an optimal development scheme of an oil and gas reservoir is determined according to the index values of the different development indexes of the different oil and gas reservoir development schemes. The method is based on a plurality of development schemes which can be obtained by mass data, and the plurality of development schemes can comprise a plurality of development conditions, so that more assumed conditions do not need to be preset for single oil and gas reservoir data; furthermore, the method can be continuously operated by the server without the need of related personnel to call parameters, is simple to operate, and can quickly and efficiently determine the development scheme of the oil and gas reservoir.
Fig. 1 is a schematic view of an application scenario of a method for determining a reservoir development scheme according to an embodiment of the present application. As shown in fig. 1, the application scenario includes a development scheme for determining a hydrocarbon reservoir of structured data and unstructured data, wherein the structured data includes initial production of an oil well, initial production of a water well, a decrement rate, the number of oil wells, the number of water wells, a drilling thickness, a geological reserve, a recovery rate and the like, which are common data.
The method for determining the oil and gas reservoir development scheme can acquire structured data and unstructured data based on a dream cloud platform. The dream cloud platform is based on a 'platformization' strategy, an enterprise-level unified data lake, a unified technical platform and general business application are built as cores, an open shared new ecology of the oil and gas industry is built, a whole industry data ecology is built by the data lake, the cloud platform is used for fusing the open technical ecology, an industry sharing capacity unit is deposited and connected by a service middle platform, and a series of digital application scenes are built to provide a more agile support environment for enterprise resource sharing, flow optimization, organization reconstruction and business mode innovation and provide new kinetic energy for high-quality transformation development of enterprises. The dream cloud platform also comprises a digital twin, a widget, software and the like, and development index prediction of different development schemes is carried out on the basis of the widget and the software in the dream cloud platform. The development index prediction method based on the widget comprises a conventional development index prediction method, a rapid numerical simulation prediction method, an artificial intelligence prediction method and the like, the development index prediction method based on the software comprises a digital twin model prediction method and the like, a development index prediction result is obtained, and the prediction result can be input into a neural network in the form of a development index prediction table to obtain an optimal oil and gas reservoir development scheme, wherein the development index prediction table can be subjected to front-end visualization and can comprise an oil and gas reservoir annual development index prediction table, an oil well annual production index prediction table, a water well annual production index prediction table and the like.
A method for determining a reservoir development scenario according to an exemplary embodiment of the present application is described below with reference to fig. 2 in conjunction with the application scenario of fig. 1. It should be noted that the above application scenario is only shown for the convenience of understanding the spirit and principle of the present application, and the embodiments of the present application are not limited by the application scenario shown in fig. 1.
Fig. 2 is a schematic flow chart of a method for determining a reservoir development scenario according to an embodiment of the present disclosure. As shown in fig. 2, the method for determining a reservoir development scenario in the embodiment of the present application includes the following steps:
s201: and acquiring relevant data of the target oil and gas reservoir, wherein the relevant data comprises exploration data, production data, oil extraction engineering data and ground engineering data.
In this step, relevant data for the target reservoir may be obtained from the dream cloud platform. The dream cloud platform can be built into a local server and serves as an operating environment for executing the application, data acquisition keys can be set based on the operating environment, and relevant data of the target oil and gas reservoir are acquired through one key of the data acquisition keys, wherein the dream cloud platform stores the relevant data of the target oil and gas reservoir, so that time for acquiring mass data can be reduced, and the acquisition of the relevant data of the target oil and gas reservoir can be quickly and efficiently realized.
In one implementation, the data of the dream cloud platform may be generated based on a simulation of a digital twin numerical simulation model of the hydrocarbon reservoir, or may be obtained based on an existing database system, for example, the related data is stored in the system, and the obtaining of the related data of the target hydrocarbon reservoir may include: and acquiring related data from the system through the data processing platform. Wherein, the data processing platform can be a dream cloud platform. Illustratively, the system building system may include a geoscience and drilling system (A1), an oil-gas-water well production data management system (A2), an oil-gas-water well production and ground engineering operation management system (A5) and the like, wherein the data in A1 includes exploration data and the like, the data in A2 includes production data and the like, and the data in A5 includes oil production engineering data, ground engineering data and the like.
S202: and designing a template based on the development schemes, and obtaining a plurality of development schemes corresponding to the target oil and gas reservoir according to the relevant data.
The development scheme design template is a plurality of different reservoir development scheme templates formed according to reservoir development experience. In practical application, a plurality of different development schemes of the target hydrocarbon reservoir can be obtained according to related data of the existing target hydrocarbon reservoir based on a pre-generated development scheme design template. For example, depending on the amount of water injected when developing a reservoir, a plurality of development scenarios may be obtained requiring different amounts of water injected.
S203: and aiming at each development scheme in the plurality of development schemes, determining index values of the development schemes respectively corresponding to different development indexes according to relevant data, wherein the development indexes comprise an oil production index, a water content index, an oil production decrement index and a pressure index.
In this step, determining index values of the development solutions corresponding to different development indexes according to the related data may include: the method comprises the steps of determining index values of development schemes corresponding to different development indexes respectively according to related data by adopting a set prediction method, wherein the set prediction method comprises a conventional development index prediction method, a digital twin model prediction method, a rapid numerical simulation prediction method and an Artificial Intelligence (AI) prediction method, the Artificial Intelligence prediction method is used for learning the conventional development index prediction method by applying big data to obtain a development index prediction analysis model, and the development index prediction analysis model is used for determining the development index corresponding to the development scheme.
In the conventional development index prediction method, the index value of the development index is mostly predicted by a class comparison method or mainstream professional hydrocarbon reservoir numerical simulation software (such as Eclipse, CMG, VIP and the like). In the application, a conventional development index prediction method is adopted, and the index value of the development index can be predicted by deploying a traditional oil and gas reservoir engineering method in a dream cloud platform in a widget form and directly calling the widget. The result predicted by the traditional oil and gas reservoir engineering method is more practical, and the prediction result of the current stage is more accurate.
Fig. 3 is a schematic flowchart of a digital twin model prediction method according to an embodiment of the present disclosure. As shown in fig. 3, the digital twin model prediction method is consistent with the simulation mechanism and steps of professional hydrocarbon reservoir numerical simulation software, and performs descending analysis based on the production dynamic data of the actual hydrocarbon reservoir to perform history fitting and yield (including oil yield and gas yield) prediction, but in the early stage of the digital twin model prediction method, the used mass data can be acquired from the dream cloud platform by one key, and the quality of the acquired mass data is guaranteed after strict examination. And after the index value prediction of the development indexes is finished, obtaining prediction results of index values of different development indexes, sequencing different development schemes according to the prediction results to obtain an optimal development index, determining a development scheme of the target oil and gas reservoir according to the optimal development index, and implementing the optimal development scheme aiming at the target oil and gas reservoir. For example, before the development scheme of the target hydrocarbon reservoir is implemented, the target hydrocarbon reservoir may be submitted to relevant personnel for field investigation, and the implementability of the development scheme of the target hydrocarbon reservoir may be verified. Optionally, the digital twin model may also be automatically updated iteratively, for example, according to the development scheme of the target reservoir, if the actual oil production is greater than the predicted oil production in the actual development process, the development scheme parameters of the target reservoir, such as water injection amount, are adjusted to optimize the development scheme of the target reservoir.
Fig. 4 is a flowchart illustrating a fast numerical simulation prediction method according to an embodiment of the present application. As shown in fig. 4, the rapid numerical simulation prediction method is to predict an index value of a development index by calling a widget of a dream cloud platform. The simulation mechanism of the rapid numerical simulation prediction method is the same as that of common professional oil and gas reservoir numerical simulation software, but compared with the professional oil and gas reservoir numerical simulation software, the rapid numerical simulation prediction method is nested on a dream cloud platform, so that a large amount of time for data collection, data processing and the like can be saved, the rapid numerical simulation prediction method is in seamless connection with other related procedures for oil and gas reservoir research, fool-type operation can be performed, non-related professionals can rapidly start to work, and the use threshold is reduced. Predicting the prediction results of the index values of different development indexes by adopting a rapid numerical simulation prediction method, sequencing different development schemes according to the prediction results to obtain an optimal development index, determining the development scheme of the target oil and gas reservoir according to the optimal development index, and implementing the optimal development scheme aiming at the target oil and gas reservoir. Optionally, a data auditing mechanism and an iteration mechanism of the rapid numerical simulation prediction method are the same as those of the digital twin model prediction method, and are not described herein again.
In some embodiments, the widgets of the rapid numerical simulation prediction method may be used in parallel or in series with other widgets on the dream cloud platform that are relevant to reservoir research to form a custom workflow.
Fig. 5 is a schematic flowchart of an artificial intelligence prediction method according to an embodiment of the present application. As shown in fig. 5, the artificial intelligence prediction method establishes a development index prediction analysis model based on big data, extracts a characteristic value of a development index by learning a conventional development index prediction method, establishes an artificial intelligence algorithm model, obtains the trained artificial intelligence algorithm model through multiple training, evaluates the precision of the artificial intelligence algorithm model, and determines the artificial intelligence algorithm model as a final artificial intelligence algorithm model if test data predicts an index value of the development index through the artificial intelligence algorithm model, and the obtained index value is higher than a set model precision evaluation value, so as to predict the index value of the development index; and if the index value is lower than the set model precision evaluation value, continuing to train the model. For example, new data which is not collected in the dream cloud platform can be collected to serve as posterior data, and whether the precision of the artificial intelligence algorithm model prediction result meets the requirement or not can be further verified.
Optionally, the data auditing mechanism and the iteration mechanism of the artificial intelligence prediction method are the same as those of the digital twin model prediction method, and are not described herein again. Illustratively, the widgets of the artificial intelligence prediction method may be used in parallel or in series with other widgets on the dream cloud platform that are relevant to reservoir research.
The artificial intelligence prediction method comprises a conventional development index prediction process, can expand the probability analysis of further rationalizing the prediction result by using an artificial neural network technology, makes a decision through the artificial neural network, can reduce the workload of related workers or provide reference for the related workers, and can predict the development trend of the oil and gas reservoir.
It can be understood that the application can better optimize the development scheme of the oil and gas reservoir through the four different development index prediction methods provided by the embodiment.
Illustratively, a plurality of development schemes such as a development scheme A, a development scheme B, a development scheme C and a development scheme D are provided, and according to the related data of the existing target hydrocarbon reservoir, an artificial intelligence prediction method is adopted, so that index values of different development indexes can be correspondingly obtained, for example, the index value of the oil production index of the development scheme A is A1, the index value of the water content index is A2, the index value of the oil production decreasing index is A3, and the index value of the pressure index is A4; the index value of the oil production index of the development scheme B is B1, the index value of the water content index is B2, the index value of the oil production decrement index is B3, and the index value of the pressure index is B4; and by analogy, index values of different development indexes of the development scheme C and the development scheme D are obtained.
S204: and determining the target development scheme of the target oil and gas reservoir in the plurality of development schemes according to the index values of the plurality of development schemes corresponding to different development indexes respectively.
Based on the above embodiment, in this step, one development plan is determined among the development plans a, B, C, and D according to A1, A2, A3, A4, B1, B2, B3, B4, and the like, and the development plan is determined as the target development plan of the target hydrocarbon reservoir, for example, the development plan is determined according to the index value of the oil production index, and if the index value of the oil production index of the development plan C is the highest, the development plan C is taken as the target development plan of the target hydrocarbon reservoir, that is, the preferred development plan is obtained.
According to the method for determining the oil and gas reservoir development scheme, the plurality of development schemes obtained according to the related data can include a plurality of development conditions, so that more assumed conditions do not need to be preset for single oil and gas reservoir data; furthermore, the method can be continuously operated by the server, related personnel are not needed to adjust parameters, the operation is simple, and the oil and gas reservoir development scheme can be determined quickly and efficiently.
Further, on the basis of the foregoing embodiment, determining the target development scenario of the target hydrocarbon reservoir in the plurality of development scenarios according to the index values of the plurality of development scenarios respectively corresponding to different development indexes may include: and inputting the index values of the plurality of development schemes respectively corresponding to different development indexes into the multilayer forward neural network to intelligently classify the development indexes, so as to obtain a target development scheme output by the multilayer forward neural network.
Specifically, a plurality of results of development indexes predicted by a conventional development index prediction method, a digital twin model prediction method, a rapid numerical simulation prediction method and an artificial intelligence prediction method are used as input of a multilayer forward neural network input layer, feature extraction and other processing are carried out on the middle hidden layer, then iterative updating is carried out on the weights of different development index prediction methods through a back propagation algorithm until an oil and gas reservoir development scheme output by the multilayer forward neural network is the same as an actual oil and gas reservoir development scheme, the accuracy change of the multilayer forward neural network is stable, finally, the output layer of the multilayer forward neural network classifies different target oil and gas reservoir development schemes according to different development indexes, and the target oil and gas reservoir development scheme is optimized according to a classification result. Compared with the development index selection oil and gas reservoir development scheme directly predicted by a single development index prediction method, the advantage of the multilayer forward neural network method is mainly embodied in two aspects: the method comprises the steps of firstly, comprehensively considering the factors, comprehensively considering the development index prediction results of different development index prediction methods, and secondly, intelligently classifying and rationally selecting the optimal oil and gas reservoir development scheme.
Based on the above embodiments, fig. 6 is a schematic structural diagram of a multi-layer forward neural network according to an embodiment of the present application. As shown in fig. 6, the input of the multi-layer forward neural network further includes oil prices of different target reservoir development schemes, investment estimation results and cost estimation results of developing reservoirs, etc., and the input of the multi-layer forward neural network may be [ x1, x2, x3, x4 \8230;, xn, x (n + 1), x (n + 2), x (n + 3) ], where x1 represents the result predicted by professional reservoir numerical simulation software, x2 represents the result predicted by a digital twin model, x3 represents the result predicted by a rapid numerical simulation, and x4 represents the result predicted by an artificial intelligence, x (n + 1) represents oil price, x (n + 2) represents investment estimation results for developing oil and gas reservoirs, x (n + 3) represents cost estimation results for developing oil and gas reservoirs, and [ x1, x2, x3, x4 \8230 ], \8230, xn, x (n + 1), x (n + 2) and x (n + 3) ], through a hidden layer, x and x are compared and can be represented as K (xi, x), wherein xi represents x1, x2, x3 or x4 and the like, x represents development results of actual oil and gas reservoir development schemes and the like, and finally, different target oil and gas reservoir development schemes are classified to select the optimal target oil and gas reservoir development scheme y.
In some embodiments, the multi-layer forward neural network may also be built in the dream cloud platform in the form of a widget.
Furthermore, the method for determining the oil and gas reservoir development scheme can design single-well production allocation, development indexes, development potential risk analysis and economic evaluation results aiming at different oil and gas reservoir development schemes on the basis of the oil and gas reservoir digital twin body in the dream cloud platform, and ensures that the oil and gas reservoir development obtains better economic benefits and recovery rates.
Optionally, the setting prediction method is set in the form of a widget in a data processing platform, and the data processing platform is deployed in a server. Exemplarily, the set prediction method comprises a conventional development index prediction method, a digital twin model prediction method, a rapid numerical simulation prediction method and an artificial intelligence prediction method, wherein the conventional development index prediction method, the rapid numerical simulation prediction method and the artificial intelligence prediction method can be built in a dream cloud platform in a form of a widget; the digital twin model prediction method can be built in a dream cloud platform in the form of software.
In some embodiments, the method of determining a reservoir development plan further comprises: after the index value is determined, at least one of a reservoir annual development index prediction table, an oil well annual development index prediction table, and a water well annual production index prediction table is formed, and the formed prediction table is subjected to front-end visualization. For example, the determined index value of the hydrocarbon reservoir development index may be in the form of a development index prediction table, where the index prediction table includes a hydrocarbon reservoir annual development index prediction table, an oil well annual production index prediction table, and a water well annual production index prediction table.
In summary, the present application has at least the following advantages:
1. massive data can be rapidly and efficiently acquired on the basis of the dream cloud platform, and the efficiency of determining the development scheme of the oil and gas reservoir is improved.
2. The application of the digital twin model breaks through the operation problem of common professional hydrocarbon reservoir numerical simulation software, not only inherits the advantages of the professional hydrocarbon reservoir numerical simulation software, but also reduces the technical threshold, enables more related personnel to bear the work of determining the hydrocarbon reservoir development scheme, and saves the culture cost of the related personnel.
3. The range of users is enlarged, and the oil and gas reservoir development scheme can be determined only by numerical simulation personnel with strong service capability in the early period.
4. By applying the multilayer forward neural network, different oil and gas reservoir development schemes are intelligently classified, an optimal oil and gas reservoir development scheme is selected, and subjective factors are prevented from influencing.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Fig. 7 is a schematic structural diagram of a reservoir development scenario determination apparatus according to an embodiment of the present application. For convenience of explanation, only portions related to the embodiments of the present application are shown. As shown in fig. 7, the reservoir development scenario determination apparatus 70 includes: an acquisition module 71, a processing module 72, a first determination module 73 and a second determination module 74. Wherein:
an obtaining module 71, configured to obtain relevant data of the target hydrocarbon reservoir, where the relevant data includes exploration data, production data, oil production engineering data, and ground engineering data;
the processing module 72 is used for designing a template based on the development scheme and obtaining a plurality of development schemes corresponding to the target hydrocarbon reservoir according to the relevant data;
the first determining module 73 is configured to determine, for each of the plurality of development schemes, index values of the development schemes corresponding to different development indexes respectively according to the relevant data, where the development indexes include an oil production index, a water content index, an oil production decrement index, and a pressure index;
and a second determining module 74, configured to determine, according to the index values of the different development indexes corresponding to the multiple development schemes, a target development scheme of the target hydrocarbon reservoir among the multiple development schemes.
In a possible implementation, the second determining module 74 is specifically configured to: and inputting the index values of the plurality of development schemes respectively corresponding to different development indexes into the multilayer forward neural network to intelligently classify the development indexes, so as to obtain a target development scheme output by the multilayer forward neural network.
In a possible implementation, the first determining module 73 is specifically configured to: and determining index values of the development schemes corresponding to different development indexes respectively according to related data by adopting a set prediction method, wherein the set prediction method comprises a conventional development index prediction method, a digital twin model prediction method, a rapid numerical simulation prediction method and an artificial intelligence prediction method, the artificial intelligence prediction method is to apply big data to learn the conventional development index prediction method to obtain a development index prediction analysis model, and the development index prediction analysis model is used for determining the development indexes corresponding to the development schemes.
In one possible embodiment, the setting prediction method is provided in the form of a widget in a data processing platform, which is deployed in a server.
In a possible implementation, the obtaining module 71 is specifically configured to: and acquiring related data from the system through the data processing platform.
In a possible implementation, the first determining module 73 may be further configured to: after the index value is determined, at least one of a reservoir annual development index prediction table, an oil well annual development index prediction table, and a water well annual production index prediction table is formed, and the formed prediction table is subjected to front-end visualization.
The implementation principle and the technical effect of the determining device for the oil and gas reservoir development scheme provided by the embodiment of the application are similar to those of the embodiment, and the embodiment can be specifically referred to, and the implementation principle and the technical effect are not repeated herein.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application. For example, the electronic device may be provided as a server. As shown in fig. 8, the electronic apparatus 80 includes:
the processor 810, which further includes one or more processors, and memory resources, represented by memory 820, for storing instructions, such as application programs, that are executable by the processor 810. The application programs stored in memory 820 may include one or more modules that each correspond to a set of processing instructions. Further, the processor 810 is configured to execute instructions to perform the reservoir development scenario determination methods described above.
The electronic device 80 may also include a power component 830 configured to perform power management of the electronic device 80, a wired or wireless network interface 840 configured to connect the electronic device 80 to a network, and an input-output interface 850. The electronic device 80 may operate based on an operating system stored in the memory 820, such as X86, windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, or the like.
The Processor 810 mentioned in fig. 8 includes a Processor which may be a general-purpose Processor, including a central processing unit, a Network Processor (NP), and the like; a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
The Memory 820 may include a Random Access Memory (RAM), a Static Random Access Memory (SRAM), an electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM) a Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk, such as at least one disk Memory.
Those skilled in the art will appreciate that the electronic device illustrated in fig. 8 is not limiting and may include more or fewer components than those shown, or some of the components may be combined, or a different arrangement of components.
The embodiment of the application further provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed, the method for determining the oil and gas reservoir development scheme is implemented.
Embodiments of the present application also provide a computer program product comprising a computer program that, when executed, implements a method of determining a gas reservoir development scenario, as described above.
The embodiment of the application also provides a chip for operating the instructions, and the chip is used for executing the method for determining the oil and gas reservoir development scheme in any method embodiment.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) referred to in the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with the relevant laws and regulations and standards of the relevant country and region, and are provided with corresponding operation entrances for the user to choose authorization or denial.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method for determining a reservoir development scenario, comprising:
acquiring relevant data of a target oil and gas reservoir, wherein the relevant data comprises exploration data, production data, oil extraction engineering data and ground engineering data;
obtaining a plurality of development schemes corresponding to the target oil and gas reservoir according to the relevant data based on a development scheme design template;
aiming at each development scheme in the plurality of development schemes, determining index values of the development schemes corresponding to different development indexes respectively according to the relevant data, wherein the development indexes comprise an oil production index, a water content index, an oil production decrement index and a pressure index;
and determining a target development scheme of the target hydrocarbon reservoir in the plurality of development schemes according to the index values of the plurality of development schemes respectively corresponding to different development indexes.
2. The method according to claim 1, wherein the determining a target development scenario of the target hydrocarbon reservoir among the plurality of development scenarios according to the index values of the plurality of development scenarios respectively corresponding to different development indexes comprises:
and inputting the index values of the plurality of development schemes respectively corresponding to different development indexes into a multilayer forward neural network for intelligent classification of the development indexes to obtain the target development scheme output by the multilayer forward neural network.
3. The method according to claim 1 or 2, wherein the determining, according to the related data, index values of the development solutions corresponding to different development indexes respectively comprises:
and determining index values of the development schemes corresponding to different development indexes respectively according to the related data by adopting a set prediction method, wherein the set prediction method comprises a conventional development index prediction method, a digital twin model prediction method, a rapid numerical simulation prediction method and an artificial intelligence prediction method, the artificial intelligence prediction method is used for learning the conventional development index prediction method by applying big data to obtain a development index prediction analysis model, and the development index prediction analysis model is used for determining the development indexes corresponding to the development schemes.
4. The determination method according to claim 3, wherein the setting prediction method is provided in the form of a widget in a data processing platform deployed in a server.
5. The method of claim 4, wherein the related data is stored in a system of construction, and the obtaining related data of the target reservoir comprises:
and acquiring the related data from the system-built system through the data processing platform.
6. The determination method according to claim 1 or 2, further comprising:
after the index value is determined, at least one of a reservoir annual development index prediction table, an oil well annual development index prediction table and a water well annual production index prediction table is formed, and front-end visualization is performed on the formed prediction table.
7. An apparatus for determining a reservoir development plan, comprising:
the acquisition module is used for acquiring relevant data of the target oil and gas reservoir, wherein the relevant data comprises exploration data, production data, oil extraction engineering data and ground engineering data;
the processing module is used for designing a template based on a development scheme and obtaining a plurality of development schemes corresponding to the target oil and gas reservoir according to the related data;
the first determining module is used for determining index values of different development indexes respectively corresponding to the development schemes according to the relevant data aiming at each development scheme in the plurality of development schemes, wherein the development indexes comprise an oil production index, a water content index, an oil production decrement index and a pressure index;
and the second determining module is used for determining the target development scheme of the target hydrocarbon reservoir in the plurality of development schemes according to the index values of the plurality of development schemes respectively corresponding to different development indexes.
8. The determination apparatus according to claim 7, wherein the second determination module is specifically configured to: and inputting the index values of the plurality of development schemes respectively corresponding to different development indexes into a multilayer forward neural network to carry out intelligent classification of the development indexes, so as to obtain the target development scheme output by the multilayer forward neural network.
9. An electronic device, comprising: a memory and a processor;
the memory is to store program instructions;
the processor is configured to invoke program instructions in the memory to perform the method of determining a reservoir development plan of any of claims 1 to 6.
10. A computer readable storage medium having stored thereon computer executable instructions which, when executed, implement the method of determining a reservoir development plan of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211582020.5A CN115759786A (en) | 2022-12-09 | 2022-12-09 | Method, device, equipment and storage medium for determining oil and gas reservoir development scheme |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211582020.5A CN115759786A (en) | 2022-12-09 | 2022-12-09 | Method, device, equipment and storage medium for determining oil and gas reservoir development scheme |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115759786A true CN115759786A (en) | 2023-03-07 |
Family
ID=85345043
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211582020.5A Pending CN115759786A (en) | 2022-12-09 | 2022-12-09 | Method, device, equipment and storage medium for determining oil and gas reservoir development scheme |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115759786A (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050149307A1 (en) * | 2000-02-22 | 2005-07-07 | Schlumberger Technology Corporation | Integrated reservoir optimization |
| CN109102182A (en) * | 2018-08-01 | 2018-12-28 | 中国石油天然气股份有限公司 | Method and device for screening shale gas development scheme |
-
2022
- 2022-12-09 CN CN202211582020.5A patent/CN115759786A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050149307A1 (en) * | 2000-02-22 | 2005-07-07 | Schlumberger Technology Corporation | Integrated reservoir optimization |
| CN109102182A (en) * | 2018-08-01 | 2018-12-28 | 中国石油天然气股份有限公司 | Method and device for screening shale gas development scheme |
Non-Patent Citations (1)
| Title |
|---|
| 余海洋;俞锋;: "基于模糊法和神经网络的增压开采方案的优选研究" * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11852778B2 (en) | Static engine and neural network for a cognitive reservoir system | |
| Yin et al. | Multifidelity genetic transfer: an efficient framework for production optimization | |
| Wei et al. | Predicting injection profiles using ANFIS | |
| US8504335B2 (en) | Robust optimization-based decision support tool for reservoir development planning | |
| US8775347B2 (en) | Markov decision process-based support tool for reservoir development planning | |
| CN104881435B (en) | A kind of research process automation logging evaluation expert system based on data mining | |
| CN109711636A (en) | River water level prediction method based on chaotic firefly and gradient lifting tree model | |
| Bashari et al. | Estimation of deformation modulus of rock masses by using fuzzy clustering-based modeling | |
| CN104885101A (en) | Automatically selecting analogous members for new population members based on incomplete descriptions, including an uncertainty characterizing selection | |
| CN106062311A (en) | Ranking drilling locations among shale plays | |
| CN105550393A (en) | Firearm variant design method supporting rapid generation of schemes | |
| Asadi et al. | Development of optimal fuzzy models for predicting the strength of intact rocks | |
| Tao et al. | A new productivity prediction hybrid model for multi-fractured horizontal wells in tight oil reservoirs | |
| Cavalcante et al. | A continuous learning algorithm for history matching | |
| CN107506831A (en) | Blasting parameters determination method and system | |
| CN115759786A (en) | Method, device, equipment and storage medium for determining oil and gas reservoir development scheme | |
| Elwakil | Knowledge discovery based simulation system in construction | |
| Liu et al. | Accelerated completion optimization with uncertainty reduction through coupled data and physics based hybrid models | |
| CN108492014A (en) | A kind of data processing method and device of determining geological resources | |
| Suwito et al. | Novel machine learning and data analytics approach for history matching giant mature multilayered oil field | |
| Muñoz Vélez et al. | EOR screening and early production forecasting in heavy oil fields: A machine learning approach | |
| Qubian et al. | A Novel High-Speed AI-Physics Hybrid Model to Predict the Reservoir Behavior; A Case Study | |
| Qoqandi et al. | Production Forecasting in Tight Gas Reservoirs Using Long Short-Term Memory Methods (LSTM) | |
| Afradi et al. | Selecting the best mining method using analytical and numerical methods | |
| Zangl et al. | Holistic workflow for autonomous history matching using intelligent agents: A conceptual approach |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20230307 |
|
| RJ01 | Rejection of invention patent application after publication |