Oil and gas economic reserve prediction method and system, medium and electronic equipment

Abstract

The invention provides an oil and gas economic reserve prediction method and system, a medium and electronic equipment. The oil and gas economic reserve prediction method comprises the following steps: acquiring basic data of oil and gas economic reserves; determining evaluation indexes of the oil and gas economic reserves; acquiring risk parameters influencing the oil and gas economic reserves; the risk parameters are quantified, and values of the risk parameters are obtained; and obtaining the minimum-scale oil and gas economic reserves according to the basic data, the evaluation indexes and the values of the risk parameters by using an oil and gas economic reserve prediction model. According to the method provided by the invention, the accuracy of oil and gas economic reserve prediction can be improved.

Description

Technical Field

[0001] The present application belongs to the technical field of oil and gas exploration and development, and relates to an oil and gas economic reserve prediction method, system, medium and electronic equipment. Background Art

[0002] The recoverable reserves of an oil field are the material basis for oil field development. Under the existing well network, process technology and economic conditions, the total amount of oil extracted from the reservoir is the economic reserves of oil and gas. The prediction of economic reserves of oil and gas is a scientific tool for oil and gas economic decision-making and one of the effective means for enterprises to improve their management.

[0003] The commonly used oil and gas economic reserve prediction methods lack comprehensive correlation analysis of multiple factors, especially for high-investment, high-risk offshore oil and gas projects. Once the parameters fluctuate, the calculation results will deviate greatly, resulting in inaccurate prediction results. Summary of the invention

[0004] The purpose of this application is to provide a method, system, medium and electronic equipment for predicting oil and gas economic reserves, so as to improve the accuracy of oil and gas economic reserves prediction.

[0005] In a first aspect, the present application provides a method for predicting oil and gas economic reserves, the prediction method comprising: obtaining basic data of oil and gas economic reserves; determining evaluation indicators of the oil and gas economic reserves; obtaining risk parameters affecting the oil and gas economic reserves; quantifying the risk parameters to obtain values ​​of the risk parameters; and using an oil and gas economic reserves prediction model to obtain the minimum scale economic reserves of oil and gas based on the basic data, the evaluation indicators and the values ​​of the risk parameters.

[0006] In an implementation of the first aspect, quantifying the risk parameter and obtaining the range of the risk parameter includes: selecting a probability distribution model according to the distribution characteristics of the risk parameter; and quantifying the risk parameter using the probability distribution model to obtain the value of the risk parameter.

[0007] In an implementation of the first aspect, the process of quantifying the risk parameter and obtaining the value of the risk parameter includes: quantifying the risk parameter using a random simulation model, and obtaining the value of the risk parameter according to a Wiener process obeyed by the risk parameter;

[0008] The Wiener process is expressed as:

[0009] dx(t)=adt+bdz;

[0010]

[0011] Among them, ε(t) is a standard normally distributed random variable, x represents the risk parameter, t represents time, a represents the drift coefficient, and b represents the average volatility of the risk parameter.

[0012] In an implementation of the first aspect, the process of quantifying the risk parameter by using the probability distribution model and obtaining the value of the risk parameter further includes:

[0013] The probability density function of the risk parameter determined by the probability distribution model is:

[0014]

[0015] The cumulative distribution function is:

[0016]

[0017] Where x is the risk parameter, p(x) is the probability value of the risk parameter, and the parameters μ and σ satisfy μ∈R, σ>0;

[0018] According to the inverse function of the cumulative distribution function x=F -1 (x) Obtaining the value of the risk parameter.

[0019] In an implementation of the first aspect, the basic data includes basic parameters of the well, production data, applicable engineering models, historical investment costs and / or oil and gas prices; and / or the risk parameters include development investment, operating costs and / or oil and gas prices.

[0020] In an implementation of the first aspect, the prediction method further includes: constructing a calculation expression of an evaluation index according to the basic data and the value of the risk parameter:

[0021]

[0022] Wherein, CI is the capital inflow, CO is the capital outflow, CI and CO are obtained according to the basic data and the risk parameters, i is the discount rate, T is the evaluation period, and t is the discount period.

[0023] In an implementation of the first aspect, the evaluation index includes a net present value, and using the oil and gas economic reserves prediction model to obtain the minimum economic reserves of oil and gas according to the basic data, the evaluation index and the value of the risk parameter includes: constructing an equation of NPV=0 according to the oil and gas economic reserves prediction model, repeatedly solving the equation, and obtaining the value range of the minimum economic reserves of oil and gas.

[0024] In a second aspect, the present application provides an oil and gas economic reserves prediction system, which includes: a data acquisition module for acquiring basic data of oil and gas economic reserves; an evaluation index acquisition module for determining the evaluation index of the oil and gas economic reserves; a risk parameter acquisition module for acquiring risk parameters affecting the oil and gas economic reserves; a quantification module for quantifying the risk parameters and obtaining the values ​​of the risk parameters; an oil and gas economic reserves prediction module for obtaining the minimum scale economic reserves based on the basic data, the evaluation index and the values ​​of the risk parameters.

[0025] In a third aspect, the present application provides an electronic device, comprising: a memory on which a computer program is stored; and a processor, communicatively connected to the memory, for executing the computer program to implement the oil and gas economic reserve prediction method as described above.

[0026] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the above-mentioned method for predicting economic reserves of oil and gas.

[0027] As described above, the oil and gas economic reserve prediction method, system, medium and electronic equipment described in the present application have the following beneficial effects:

[0028] The oil and gas economic reserves prediction method can quantify the risk parameters that affect the oil and gas economic reserves, and predict the oil and gas economic reserves based on the basic data of the oil and gas economic reserves, the range of the risk parameters and the evaluation indexes to obtain the minimum scale economic reserves of oil and gas, thereby improving the accuracy of the oil and gas economic reserves prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Shown is a schematic diagram of an electronic device described in an embodiment of the present application.

[0030] Figure 2 Shown is a process diagram of the oil and gas economic reserves prediction method described in an embodiment of the present application.

[0031] Figure 3 Shown is a schematic diagram of the process of obtaining risk parameter values ​​described in an embodiment of the present application.

[0032] Figure 4 Shown is a schematic diagram of the process of obtaining risk parameter values ​​described in an embodiment of the present application.

[0033] Figure 5 Shown is a structural schematic diagram of the oil and gas economic reserves prediction system described in an embodiment of the present application.

[0034] Figure 6 Shown is a schematic diagram of the structure of an electronic device described in an embodiment of the present application.

[0035] Component number description

[0036] 1 Oil and Gas Economic Reserves Prediction System

[0037] 11 Data acquisition module

[0038] 12 Evaluation Index Acquisition Module

[0039] 13 Risk parameter acquisition module

[0040] 14 Quantization module

[0041] 15 Oil and gas economic reserves prediction module

[0042] 2 Electronic devices

[0043] 21 Memory

[0044] 22 Processor

[0045] 23 Display

[0046] 31 Cell Phone

[0047] 32 Tablets

[0048] 33 Laptop

[0049] Steps S11 to S15

[0050] Steps S21 to S22

[0051] Steps S31 to S32 DETAILED DESCRIPTION

[0052] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0053] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0054] The recoverable reserves of an oil field are the material basis for the development of the oil field. Under the existing well network, process technology and economic conditions, the total amount of oil extracted from the reservoir is the economic reserves of oil and gas. The prediction of economic reserves of oil and gas is a scientific tool for oil and gas economic decision-making, and is also one of the effective means for enterprises to improve their management. Only when the oil and gas reserves are greater than the minimum economic reserves of oil and gas, do the oil and gas reserves have economic value for exploitation.

[0055] The minimum economic reserves of oil and gas are affected by geological conditions and mining technology. It is a dynamic value that changes with the fluctuation of oil and gas prices and the changes in the cost of production. The commonly used method for predicting oil and gas economic reserves is the break-even method, which is a static analysis method and ignores the time value of money. In addition, when calculating the minimum economic reserves of oil and gas, deterministic parameters are usually selected, lacking a comprehensive correlation analysis with factors such as oil and gas prices, investment, operating costs, and related parameters. Especially for high-investment, high-risk offshore oil and gas projects, once the parameters fluctuate, the calculation results will deviate greatly, resulting in inaccurate prediction results.

[0056] At least to address the above problems, the following embodiments of the present application provide a method for predicting oil and gas economic reserves, which can be applied to Figure 1 Electronic equipment shown.

[0057] The electronic devices described in the present invention may include mobile phones 31 with wireless charging function, tablet computers 32, laptop computers 33, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), etc. The embodiments of the present invention do not impose any restrictions on the specific types of electronic devices.

[0058] For example, the electronic device may be a station (STAION, ST) in a WLAN with a wireless charging function, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with a wireless charging function, a computing device or other processing device, a computer, a laptop computer, a handheld communication device, a handheld computing device, and / or other devices for communicating on a wireless system and a next generation communication system, such as a mobile terminal in a 5G network, a mobile terminal in a future evolved Public Land Mobile Network (PLMN), or a mobile terminal in a future evolved Non-terrestrial Network (NTN).

[0059] For example, the electronic device can communicate with the network and other devices through wireless communication. The above wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobilecommunication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), BT, GNSS, WLAN, NFC, FM, and / or IR technology. The GNSS can include Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou navigation Satellite System (BDS), Quasi-Zenith Satellite System (QZSS) and / or Satellite Based Augmentation Systems (SBAS).

[0060] The technical solutions in the embodiments of the present application will be described in detail below in conjunction with the drawings in the embodiments of the present application.

[0061] Figure 2 The diagram shows a process diagram of a method for predicting economic reserves of oil and gas in one embodiment of the present application. Figure 2 As shown, the prediction method includes:

[0062] S11, obtaining basic data of oil and gas economic reserves. When predicting oil and gas economic reserves, it is necessary to first obtain basic data related to the development of oil and gas economic reserves based on the different geographical locations, geological characteristics and geological risks of the offshore target blocks. The basic data is data related to oil and gas exploitation and oil and gas economic reserves.

[0063] S12, determining the evaluation index of the economic reserves of oil and gas. The evaluation index is an index for evaluating the economic value of oil and gas reserves, that is, determining what goals need to be achieved in predicting the economic reserves of oil and gas. Among them, the evaluation index can be the profit, revenue, total assets and / or added value of the oil and gas company. The evaluation index is generally designed and selected based on the overall goal of the future oil and gas economy. Determining the evaluation index of the economic reserves of oil and gas is a prerequisite for prediction, and can provide a basis for formulating a prediction analysis plan and selecting a prediction method.

[0064] S13, obtaining risk parameters that affect the economic reserves of oil and gas. Risk parameters are one of the important factors that affect the economic benefits of oil and gas reserve exploitation. Through risk parameters, the benefits of oil and gas reserve exploitation can be obtained more comprehensively and meticulously, thereby improving the accuracy of oil and gas economic reserve exploitation. Among them, risk parameters are parameters that can be changed within a certain range.

[0065] S14, quantify the risk parameter to obtain the value of the risk parameter. Since the economic reserves of oil and gas are greatly affected by the risk parameter, when the value of the risk parameter fluctuates, the prediction result of the minimum economic reserves of oil and gas will change significantly. If the risk parameter is used as a fixed value to predict the minimum economic reserves of oil and gas, the accuracy of the prediction result will be greatly reduced. To address this problem, in this embodiment, the risk parameter is quantified to obtain the value of the risk parameter, and the minimum economic reserves of oil and gas are obtained by prediction based on the repeated risk parameter values.

[0066] S15, using the oil and gas economic reserve prediction model to obtain the minimum scale economic reserves of oil and gas according to the basic data, the evaluation index and the values ​​of the risk parameters.

[0067] According to the above description, the oil and gas economic reserves prediction method provided in the embodiment of the present application can quantify the risk parameters that affect the oil and gas economic reserves, and predict the oil and gas economic reserves based on the basic data of the oil and gas economic reserves, the values ​​of the risk parameters and the evaluation indicators to obtain the minimum scale economic reserves of oil and gas. Compared with the technical solution of using the risk parameters as a fixed value to predict the minimum scale economic reserves of oil and gas, the minimum scale economic reserves of oil and gas predicted by the embodiment of the present application have higher accuracy.

[0068] Figure 3 The diagram shows a process of obtaining risk parameter values ​​in one embodiment of the present application. Figure 3 As shown, quantifying the risk parameter and obtaining the value of the risk parameter includes:

[0069] S21, select a probability distribution model according to the distribution characteristics of the risk parameters. The risk parameters are random variables that vary within a certain range, and the risk parameters include discrete random variables and continuous random variables. Select a suitable probability distribution model according to the distribution characteristics of the risk parameters. The given ranges of the risk parameters include: the range of oil and gas prices, and the range of investment and costs. The range of oil and gas prices is determined based on the fluctuations in previous market prices; the range of investment and costs is determined based on the magnitude of data deviation after comparison with actual historical project occurrences, feasibility study reports and / or infrastructure reports.

[0070] S22: quantify the risk parameter using the probability distribution model to obtain a value of the risk parameter.

[0071] By selecting an appropriate probability distribution model to quantify the risk parameters, the probability of the risk parameters can be obtained, and further the possible values ​​of the risk parameters can be obtained.

[0072] In one embodiment of the present application, the process of quantifying the risk parameter and obtaining the value of the risk parameter includes: quantifying the risk parameter using a random simulation model, and obtaining the value of the risk parameter according to a Wiener process to which the risk parameter obeys;

[0073] The Wiener process is expressed as:

[0074] dx(t)=adt+bdz;

[0075]

[0076] Among them, ε(t) is a standard normally distributed random variable, x represents the risk parameter, t represents time, a represents the drift coefficient, and b represents the average volatility of the risk parameter.

[0077] Discretizing the Wiener process yields:

[0078] From the above equation, we can infer that the factors that affect the change of risk parameters are mainly the following two points: one is the trend of risk parameter value changing over time, and the other is that the change of risk parameter value is affected by average volatility. The contribution of the former to the growth of risk parameter value depends on the length of time. If Δz is not considered, dx(t) = adt, that is, X = X0 + at, indicating that the risk parameter has the property of linear growth. The latter depends on the random fluctuation caused by Brownian motion. If the Δz term is taken into account, dX = X0 + at + bdz, that is, the change of risk parameter value can be regarded as jointly determined by the above two factors, that is, while the risk parameter x is growing linearly, there is also a tendency of random fluctuation.

[0079] Figure 4 The diagram shows a process of obtaining risk parameter values ​​in one embodiment of the present application. Figure 4 As shown, the process of quantifying the risk parameter by using the probability distribution model and obtaining the value of the risk parameter also includes:

[0080] S31, determining the probability density function of the risk parameter using the probability distribution model.

[0081] Exemplarily, the risk parameter is symmetrically distributed around the mean, where the mean is Variance σ 2 , expressed as Its probability density function is expressed as:

[0082]

[0083] The cumulative distribution function is expressed as:

[0084]

[0085] Among them, x is the risk parameter, p(x) is the probability value of the risk parameter, and the parameters μ and σ satisfy μ∈R, σ>0.

[0086] According to the inverse function of the cumulative distribution function x=F -1 (x) Obtaining the value of the risk parameter.

[0087] S32, performing multiple simulations on the risk parameter to obtain the value of the risk parameter.

[0088] Assuming that the range of the risk parameter is x∈[1,100], since the probability density function of the risk parameter satisfies the normal distribution, the probability value of the risk parameter x can be obtained. The probability value corresponding to the middle position of the interval is the largest, and the probability value corresponding to the risk parameter x=1 and the risk parameter x=100 is the smallest. For example, the probability value corresponding to the middle position of the interval is 20%, and the probability value corresponding to the risk parameter x=1 and the risk parameter x=100 is 0.5%. Perform multiple probability simulation calculations on the risk parameter x, taking 1000 times as an example here, according to the probability value corresponding to the risk parameter x and the number of repeated simulations, the corresponding number of occurrences of each value of the risk parameter in x∈[1,100] can be obtained according to the corresponding probability. When the risk parameter is a single parameter, for example, the risk parameter is investment x, simulating the risk parameter x 1000 times will result in 200 occurrences of the risk parameter when x=50, 5 occurrences when x=1 and x=100, and 1000 simulations will result in 1000 values, which are grouped into a set of data, which is the value of the risk parameter. When the risk parameter is multiple parameters, for example, the risk parameter is investment x and oil price y, simulating the risk parameters x and y 1000 times will result in 1000 combinations of risk parameters [x, y], which are grouped into a set of data, which is the value of the risk parameter.

[0089] In one embodiment of the present application, the basic data includes basic parameters of the well, production data, applicable engineering model, historical investment costs and / or oil and gas prices; and / or the risk parameters include development investment, operating costs and / or oil and gas prices. Among them, the production data is the oil and gas production output. The basic parameters of the well are, for example, the well name, number of wells, well type, well depth, water depth, and footage. Applicable engineering models are, for example, unmanned wellhead platforms, manned wellhead platforms, and integrated platforms. Development investment is, for example, investment in drilling and completion projects, investment in offshore drilling platforms, investment in submarine pipelines, investment in submarine cables, and production preparation costs. Operating costs are, for example, oil and gas water treatment fees, well repair operation fees, insurance premiums, warehousing, port miscellaneous fees, production safety fees, and production well research fees.

[0090] In one embodiment of the present application, the calculation expression for constructing the evaluation index according to the basic data and the value of the risk parameter is:

[0091]

[0092] Among them, CI and CO are related to economic reserves, CI is capital inflow, CO is capital outflow, CI and CO are obtained according to the basic data and the risk parameters, i is the discount rate, T is the evaluation period, and t is the discount period.

[0093] The capital inflow is the economic value of the oil and gas reserves. The capital outflow is the costs associated with the oil and gas reserves. The evaluation period is the number of years the oil and gas reserves are mined. The discount period is the number of years the oil and gas reserves are mined.

[0094] The development data and risk parameters of oil and gas reserves are different every year, and the corresponding costs, investment costs and mining benefits are also different. Different time spans will also affect the accuracy of the prediction results. The types of time spans can be divided into two categories, short-term predictions and long-term predictions. Among them, the short-term prediction can be the year of oil and gas reserves extraction, such as 1, 2, 3, ..., n, that is, the first year, the second year, the third year, ..., the nth year. The time span of the long-term prediction is the number of years of oil and gas reserves extraction, such as 1-n, that is, the first to nth year. In practical applications, short-term predictions help to understand market dynamics in a timely manner, grasp the favorable opportunities for market changes, and improve the level of business decision-making. Long-term predictions focus on studying the long-term development trends of market elements and provide decision-making basis for determining the long-term development direction.

[0095] For example, the source of capital inflow is oil and gas sales revenue, where oil and gas sales revenue = crude oil production * crude oil sales price + natural gas production * natural gas sales price. Capital outflow includes development investment, exploration investment, operating costs, working capital, operating costs, sales taxes and surcharges, and abandonment costs.

[0096] For example, the evaluation index includes the net present value, which takes into account the time value of money, can clearly show the absolute return of oil and gas reserves during the entire operation period, and can better reflect the economic benefits of oil and gas reserves extraction. In addition, the evaluation index also includes the internal rate of return and the payback period.

[0097] In one embodiment of the present application, using the oil and gas economic reserve prediction model to obtain the minimum scale economic reserves of oil and gas according to the basic data, the evaluation index and the value of the risk parameter includes: constructing an equation of NPV=0 according to the oil and gas economic reserve prediction model, repeatedly solving the equation, and obtaining the value range of the minimum scale economic reserves of oil and gas. Among them, the oil and gas economic reserve prediction model is, for example, a fully parameterized cash flow model, which can be established in computer Excel software. The risk parameter is quantified by a probability distribution model to obtain the value of the risk parameter, and the value of the risk parameter is input into the oil and gas economic reserve prediction model, and the lower limit and upper limit of the minimum scale economic reserves of oil and gas can be calculated, and the upper limit and lower limit constitute the range of the minimum scale economic reserves of oil and gas.

[0098] In some embodiments, determining the probability density function of the risk parameter using the probability distribution model further includes:

[0099]

[0100] Among them, x represents the risk parameter, p(x) represents the probability value of the risk parameter, and satisfies the uniform distribution.

[0101] The cumulative distribution function is expressed as:

[0102]

[0103] Where x represents the risk parameter, and F(x) represents the probability distribution range of the risk parameter;

[0104] The value of the risk parameter is obtained according to the inverse function of the cumulative distribution function.

[0105] When a≤x≤b, x=a+(ba)F(x).

[0106] Assuming that the range of risk parameters is x∈[1,100], since the probability density function of risk parameters satisfies uniform distribution, the probability value of risk parameter x can be obtained, and the probability value corresponding to each risk parameter in the interval [1,100] is 1 / 100. Perform multiple simulation calculations on the risk parameters, taking 1000 times as an example, 1000 risk parameter values ​​can be obtained, among which each risk parameter value in the interval [1,100] appears 10 times. The 1000 risk parameter values ​​are regarded as a set of data, which is the value of the risk parameter.

[0107] In some embodiments, determining the probability density function of the risk parameter using the probability distribution model further includes:

[0108]

[0109] Among them, x represents the risk parameter, and p(x) represents the probability value of the risk parameter.

[0110] The cumulative distribution function is expressed as:

[0111]

[0112] Among them, x represents the risk parameter, F(x) represents the probability distribution range of the risk parameter, a represents the minimum value, b represents the maximum value, and c represents the mode, that is, the value of the risk parameter that appears most frequently.

[0113] The value of the risk parameter is obtained according to the inverse function of the cumulative distribution function.

[0114] When a<x≤c,

[0115] When c<x<b,

[0116] In some embodiments, determining the probability density function of the risk parameter using the probability distribution model further includes:

[0117]

[0118] Among them, x represents the risk parameter, p(x) represents the probability value of the risk parameter, p and q represent two shape parameters, and a and b represent the lower and upper control points of the probability distribution graph.

[0119] The cumulative distribution function is expressed as:

[0120]

[0121] Among them, x represents the risk parameter, and F(x) represents the probability distribution range of the risk parameter.

[0122] According to the inverse function of the cumulative distribution function x=F -1 (x), obtaining the value of the risk parameter.

[0123] In summary, the embodiments of the present application provide a method for predicting economic reserves of oil and gas. The method for predicting economic reserves of oil and gas comprehensively considers risks such as geological risks, investment risks, and oil and gas price fluctuations according to the different geographical locations, geological characteristics, and applicable engineering models of offshore target blocks, quantifies risk parameters, and obtains the values ​​of risk parameters through repeated simulations. By solving, the value range of the minimum economic reserves of oil and gas within a certain interval and the corresponding visual simulation results can be obtained. The method for predicting economic reserves of oil and gas provided by the embodiments of the present application can improve the accuracy of the minimum economic reserves of oil and gas and ensure the economic value of oil and gas reserve exploitation.

[0124] The protection scope of the oil and gas economic reserve prediction method described in the embodiment of the present application is not limited to the execution order of the steps listed in the present embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0125] The embodiment of the present application also provides an oil and gas economic reserves prediction system, which can implement the oil and gas economic reserves prediction method described in the present application, but the implementation device of the oil and gas economic reserves prediction method described in the present application includes but is not limited to the structure of the oil and gas economic reserves prediction system listed in the present embodiment. All structural deformations and replacements of the prior art made according to the principles of the present application are included in the protection scope of the present application.

[0126] Figure 5 The diagram shows the structure of the oil and gas economic reserve prediction system in one embodiment of the present application. Figure 5As shown, the oil and gas economic reserves prediction system 1 includes: a data acquisition module 11, an evaluation index acquisition module 12, a risk parameter acquisition module 13, a quantification module 14 and an oil and gas economic reserves prediction module 15. Among them, the data acquisition module 11 is used to obtain the basic data of the oil and gas economic reserves. The evaluation index acquisition module 12 is used to determine the evaluation index of the oil and gas economic reserves. The risk parameter acquisition module 13 is used to obtain the risk parameters that affect the oil and gas economic reserves. The quantification module 14 is used to quantify the risk parameters and obtain the values ​​of the risk parameters. The oil and gas economic reserves prediction module 15 is used to obtain the minimum scale economic reserves according to the basic data, the evaluation index and the values ​​of the risk parameters.

[0127] In the several embodiments provided in the present application, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules or units, which can be electrical, mechanical or other forms.

[0128] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0129] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0130] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by the processor, the oil and gas economic reserve prediction method provided by the present application is implemented. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state disk (SSD)), etc.

[0131] The embodiment of the present application may also provide an electronic device. Figure 6 The diagram shows a schematic diagram of the structure of an electronic device 2 in an embodiment of the present application. Figure 6 As shown, in this embodiment, the electronic device 2 includes a memory 21 and a processor 22 .

[0132] The memory 21 is used to store computer programs. In some possible implementations, the memory 21 may include: ROM, RAM, a disk, a USB flash drive, a memory card, or an optical disk, etc., various media that can store program codes.

[0133] In the embodiment of the present application, the memory 21 may include a computer system readable medium in the form of a volatile memory, such as a RAM and / or a cache memory. The electronic device 2 may further include other removable / non-removable, volatile / non-volatile computer system storage media. The memory 21 may include at least one program product having a set (e.g., at least one) of program modules, which are configured to perform the functions of the various embodiments of the present application.

[0134] The processor 22 is connected to the memory 21 and is used to execute the computer program stored in the memory 21 so that the electronic device 2 executes the oil and gas economic reserve prediction method.

[0135] Exemplarily, the processor 22 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. In other embodiments, the processor 22 may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0136] In some implementations, the electronic device 2 provided in the embodiment of the present application may further include a display 23. The display 23 is communicatively connected to the memory 21 and the processor 22, and is used to display a graphical user interface (GUI) related to the oil and gas economic reserve prediction method.

[0137] In the embodiment of the present application, the display 23 may include a display screen (display panel). In some implementations, the display panel may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. In addition, the display 23 may also be a touch panel (touch screen, touch screen), and the touch panel may include a display screen and a touch-sensitive surface. When the touch-sensitive surface detects a touch operation on or near it, it is transmitted to the processor 22 to determine the type of touch event, and then the processor 22 provides a corresponding visual output on the display device according to the type of touch event.

[0138] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0139] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for predicting economic reserves of oil and gas, characterized in that: The prediction method comprises: Obtain basic data on economic reserves of oil and gas; Determining evaluation indicators for the economic reserves of the oil and gas; Obtain risk parameters affecting the economic reserves of the oil and gas; quantifying the risk parameter to obtain a value of the risk parameter; The oil and gas economic reserves prediction model is used to obtain the minimum scale economic reserves of oil and gas according to the basic data, the evaluation index and the values ​​of the risk parameters.

2. The prediction method according to claim 1, characterized in that: The risk parameter is quantified, and the range of obtaining the risk parameter includes: selecting a probability distribution model according to the distribution characteristics of the risk parameters; The risk parameter is quantified using the probability distribution model to obtain the value of the risk parameter.

3. The prediction method according to claim 1, characterized in that: The process of quantifying the risk parameter and obtaining the value of the risk parameter includes: quantifying the risk parameter using a random simulation model, and obtaining the value of the risk parameter according to a Wiener process obeyed by the risk parameter; The Wiener process is expressed as: dx(t)=adt+bdz; Among them, ε(t) is a standard normally distributed random variable, x represents the risk parameter, t represents time, a represents the drift coefficient, and b represents the average volatility of the risk parameter.

4. The prediction method according to claim 2, characterized in that: The process of quantifying the risk parameter by using the probability distribution model and obtaining the value of the risk parameter further includes: The probability density function of the risk parameter determined by the probability distribution model is: The cumulative distribution function is: Where x is the risk parameter, p(x) is the probability value of the risk parameter, and the parameters μ and σ satisfy μ∈R, σ>0; According to the inverse function of the cumulative distribution function x=F -1 (x) Obtaining the value of the risk parameter.

5. The prediction method according to claim 1, characterized in that: The basic data include basic parameters of the well, production data, applicable engineering models, historical investment costs and / or oil and gas prices; and / or The risk parameters include development investment, operating costs and / or oil and gas prices.

6. The prediction method according to claim 5, characterized in that: The prediction method further comprises: The calculation expression of the evaluation index is constructed according to the basic data and the value of the risk parameter: Wherein, CI is the capital inflow, CO is the capital outflow, CI and CO are obtained according to the basic data and the risk parameters, i is the discount rate, T is the evaluation period, and t is the discount period.

7. The prediction method according to claim 6, characterized in that: The evaluation index includes the net present value. The method of using the oil and gas economic reserve prediction model to obtain the minimum scale economic reserves of oil and gas according to the basic data, the evaluation index and the value of the risk parameter includes: An equation of NPV=0 is constructed according to the oil and gas economic reserve prediction model, and the equation is repeatedly solved to obtain the value range of the minimum scale economic reserves of oil and gas.

8. An oil and gas economic reserve prediction system, characterized in that: The oil and gas economic reserves prediction system comprises: Data acquisition module, used to obtain basic data of oil and gas economic reserves; An evaluation index acquisition module, used to determine the evaluation index of the oil and gas economic reserves; A risk parameter acquisition module, used to acquire risk parameters affecting the economic reserves of oil and gas; A quantification module, used to quantify the risk parameter and obtain the value of the risk parameter; The oil and gas economic reserve prediction module is used to obtain the minimum scale economic reserves according to the values ​​of the basic data, the evaluation index and the risk parameter.

9. An electronic device, characterized in that: The electronic device comprises: a memory having a computer program stored thereon; A processor is communicatively connected to the memory and is used to execute the computer program to implement the oil and gas economic reserve prediction method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by an electronic device, the method for predicting oil and gas economic reserves according to any one of claims 1 to 7 is implemented.

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