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WO2014197637A1 - Sélection et optimisation de contrôles de champs de pétrole pour plateau de production - Google Patents

Sélection et optimisation de contrôles de champs de pétrole pour plateau de production Download PDF

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Publication number
WO2014197637A1
WO2014197637A1 PCT/US2014/040963 US2014040963W WO2014197637A1 WO 2014197637 A1 WO2014197637 A1 WO 2014197637A1 US 2014040963 W US2014040963 W US 2014040963W WO 2014197637 A1 WO2014197637 A1 WO 2014197637A1
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WO
WIPO (PCT)
Prior art keywords
production rate
production
plateau
determining
providing
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.)
Ceased
Application number
PCT/US2014/040963
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English (en)
Other versions
WO2014197637A4 (fr
Inventor
Sonia Mariette EMBID DROZ
Ruben Rodriguez Torrado
Mohamed Ahmed HEGAZY
David ECHEVERRIA CIAURRI
Ulisses Mello
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Repsol SA
International Business Machines Corp
Original Assignee
Repsol SA
International Business Machines Corp
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Priority claimed from EP13382214.8A external-priority patent/EP2811107A1/fr
Application filed by Repsol SA, International Business Machines Corp filed Critical Repsol SA
Publication of WO2014197637A1 publication Critical patent/WO2014197637A1/fr
Publication of WO2014197637A4 publication Critical patent/WO2014197637A4/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E21B43/12—Methods or apparatus for controlling the flow of the obtained fluid to or in wells
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B2200/00—Special features related to earth drilling for obtaining oil, gas or water
    • E21B2200/20—Computer models or simulations, e.g. for reservoirs under production, drill bits

Definitions

  • European Patent Application No. EP 13382214 (Attorney Docket No. P9366EP00), "METHOD FOR SELECTING AND OPTIMIZING OIL FIELD CONTROLS FOR PRODUCTION PLATEAU” to Embid Droz et al; and is related to European Patent Application No. EP13382215 (Attorney Docket No. P9367EP00), "METHOD FOR ASSESING PRODUCTION STRATEGY PLANS” to Embid Droz et al, both filed June 6, 2013 with the Spanish Patent Office, assigned to the assignees of the present invention and incorporated herein by reference.
  • the present invention is related to determining energy production controls for a given subterranean hydrocarbon (oil) field production and more particularly to specifying controls for sustaining optimal field production (by means of a plateau-like profile over time).
  • Energy production facility designs are normally based on general field production considerations for a given subterranean hydrocarbon (oil) field.
  • economic business units provide a lower design limit. This limit can based on the plateau production duration and on the length of time required to recover facility construction costs. Plateau production means sustained, constant energy production for the selected production duration.
  • the economic business units also prescribe an upper design limit for the plateau duration, typically set contractually, e.g., a contractual provision limiting production from a given field to a certain time frame, or tied to field concessions.
  • determining field production typically includes finding production controls (e.g. field rate) that aim at sustaining constant production for a selected production duration.
  • field oil production rate refers to the rate of oil production for the entire field, and it is typically measured in barrels per day (bbl/day). Together with oil, producer wells may also produce other fluids (e.g. water). Thus, field fluid production rate (FFPR) refers to rate of fluid production for the entire field.
  • field fluid injection rate is related to the total amount of fluid injected to sweep the oil.
  • Commercial fluid flow simulators e.g. ECLIPSE
  • ECLIPSE electronic fluid flow simulators
  • this last strategy is known as voidage replacement and aims at maintaining internal pressure in the reservoir for better structural stability. Since the amount of oil initially present in a reservoir is finite, it is not possible to sustain a given value of FOPR indefinitely.
  • the plateau duration L P it associated with FOPR is the longest duration for which the field yields an oil production rate equal to FOPR.
  • An aspect of the invention is the generation of field production controls for production plateau, being a method that comprises
  • this development plan comprising:
  • Drilling schedule lay down the order of drilling of the wells and the time instant in which each well is drilled.
  • the control of the wells lay down the flow rate of each well over time.
  • a ranking measure such as the net present value (NPV) can be determined.
  • the method comprises the following iterative procedure for the selection of the controls for production plateau:
  • the generation of field production controls are calculated under quantifiable uncertainty, maximize net present value (NPV) for a particular reservoir and that sustain constant field production (by means of a plateau-like profile over time).
  • the NPV calculation may include simple cost models for the production/injection facilities.
  • Embodiments of the invention relates to a method for determining field controls for oil production that, under quantifiable uncertainty, maximize some exploitation performance metric such as net present value.
  • a field oil production rate (FOPR) is selected when making the exploitation performance metric maximum, the optimized production profiles and associated uncertainty are made available (and can be used, for example, for assessing facilities designs). Otherwise, a new FOPR is selected for another iteration and the average production is determined until a maximum is found.
  • the present invention also relates to a system, method and a computer program product for determining field controls for oil production that, under quantifiable uncertainty, yield a production plateau of a desired duration (this production plateau duration may be given, for example, by economic business units, and can be related to considerations on injection/production facilities).
  • Figure 1 shows an example of an example of a facility design system for selecting a hydrocarbon energy (oil/gas) facility configuration for optimal production from a particular field or reservoir, according to a preferred embodiment of the present invention
  • Figures 2A - B show an example of iteratively developing a production profile of desired plateau duration and an optimizer for the production profile.
  • aspects of the present invention may be embodied as a system, method or computer program product.
  • aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit,” “module” or “system.”
  • aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.
  • the computer readable medium may be a computer readable signal medium or a computer readable storage medium.
  • a computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
  • a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
  • a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof.
  • a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
  • Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
  • Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • LAN local area network
  • WAN wide area network
  • Internet Service Provider for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.
  • These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
  • the computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
  • Figure 1 shows an example of a system for determining field production controls 100 that selects hydrocarbon energy (oil/gas) field production configurations, according to a preferred embodiment of the present invention.
  • the preferred system 100 determines field oil production rate (which is sustained over time as much as possible; this yields a plateau-like production profile of duration L p it) from a subterranean oil reservoir; and further, assesses an associated uncertainty for resulting oil production.
  • a preferred design system 100 includes one or more computers 102,
  • the network 108 may be, for example, a local area network (LAN), the Internet, an intranet or a combination thereof.
  • the computers 102, 104, 106 include one or more processors, e.g., central processing unit (CPU) 110, memory 1 12, local storage 1 14 and some form of input/output device 1 16 providing a user interface.
  • the local storage 114 may generate and/or include a set of models 118 for a reservoir being evaluated for production.
  • the preferred design system 100 develops production profiles (i.e. descriptions of production-related quantities as functions of time) from economic criteria, such as net present value (NPV) for example, in consideration of facilities design related capital expenditure (CAPEX) elements. From these production profiles, one can determine field production controls required for maximizing production in exploiting a corresponding field. In particular, the production profiles are for plateaulike energy production with a constant energy production rate (during at least some portion of the production time frame), that corresponds to the total amount of material produced in the particular field. The preferred design system 100 and method also facilitates field controls for energy production during a previously specified plateau duration (Lpit). The output of the design system 100 can be used to design
  • a preferred design system 100 optimizes field oil production rate for a set of reservoir models, in a single variable optimization problem constrained by upper and lower variable bounds.
  • a preferred design system 100 formulates this single variable optimization problem as an optimization cost function for exploiting a respective energy production field.
  • Application of the optimization cost function to production field simulation provides a maximum average field production for the set, where the maximum average production identifies the optimal production rate (with an associated production duration) under a quantifiable uncertainty.
  • the single optimization variable is the field oil production rate (FOPR) and all other simulation inputs are known or previously determined.
  • FOPR field oil production rate
  • individual rates at production/injection wells can be obtained by means of commercial simulators which often incorporate well rate allocation algorithms.
  • Optimization constraints include upper and lower bounds for FOPR. These bounds may be given by economic business units, or alternatively, this bounds are imposed for instance by plateau durations. For the range of interest we assume a one-to-one correspondence between FOPR and L P it. Some other bounds may be dealt with by a pre-processing step or through nonlinear constraints in the optimization.
  • the single optimization variable is the plateau duration wherein the correspondence between FOPR and L P it is used.
  • a further iterative method is used for the precise value of the FOPR for a determined L P it value.
  • a preferred design system 100 determines the necessary energy (oil/gas) field production rate (and corresponding production plateau duration) to maximize NPV by solving for FOPR as the only unknown. Additionally, and with FOPR as only unknown as well, the system 100 calculates the energy field production rate needed to obtain a specified production plateau duration, that is, the
  • optimization culminates in a maximum average NPV (over the set of reservoir models used) together with a quantification of uncertainty associated with this maximum average NPV.
  • the uncertainty may be quantified (in particular for the FOPR that maximizes average NPV) by means of all the production responses determined in the average production unit 134 wherein these production responses are also used to determine the average NPV.
  • the field oil production rate (and corresponding production plateau duration) to arrive at that maximum average NPV identify an optimal solution for the optimization cost function and an optimal operating point for the production field.
  • Designers use the optimized field oil production rate and production plateau duration to design and specify design facilities for the production field.
  • Designers can determine field injection controls, subsequently, after selecting a specific control strategy. For example, injection wells may not be needed in aquifer-driven fields.
  • voidage replacement defines a field water injection rate equal to the field fluid production rate. Voidage replacement aims at maintaining reservoir pressure and endowing production with structural stability.
  • the design system 100 can determine an individual production rate for each well.
  • Reservoir flow simulators are commercially available for flow rate distribution. Typical available commercial reservoir flow simulators include, for example, ECLIPSE from Schlumberger Limited, and IMEX from Computer
  • Figure 2A shows an example of one aspect of the invention using a preferred design system (e.g., 100 of Figure 1) for iteratively developing an optimization unit 120 that maximizes NPV.
  • This NPV is determined for an average production profile obtained for instance via reservoir flow simulation for all models used to quantify uncertainty (e.g., 1 18 in Figure 1).
  • this average production profile could be approximated using only one reservoir flow simulation for an average reservoir model or may be provided as a known function.
  • the NPV calculation includes cost models for the facilities 132 that require as input average production profiles. These models can be, for example, exponential cost models that penalize large production.
  • a value for FOPR 122 is projected or supplied (e.g., by an engineer, designer or other expert) to the average production unit 134 (this unit also quantifies the associated/propagated uncertainty).
  • the unit 120 optimizes for a single variable (field oil/liquid production rate).
  • the unit 124 checks the result to determine if the average NPV is a maximum. Otherwise, the optimization unit 120 receives or determines a new FOPR 126 within FOPR bounds 128.
  • the new FOPR may be proposed for instance by means of Newton-Raphson methods estimating tangent values using previous values obtained in former steps or other numerical methods.
  • bounds may e.g. correspond to limits of FOPR or of the plateau duration value given by economic units.
  • these limits may be
  • FOPRmin limited by a maximum plateau duration. In this case, if the production rate or the plateau duration is not feasible, the preferred design system 100 provides field controls that yield closest feasible values.
  • the unit 120 In the next iteration, the unit 120 generates a new average NPV (and associated uncertainty) from the new value for FOPR 126. If the check 124 determines that the result is a maximum, the final result 130 is outputted together with the corresponding distributions. This result can be used for selecting a final design of the injection/production facilities.
  • a user, engineer or other designer may provide an optimization value 122.
  • the design system 100 may select the initial optimization value 122 automatically determined from available data considerations, or from the optimization variable bounds 128. Available data considerations may include, for example, estimated original oil in place and expected plateau duration and oil recovery factor. Automatically determining the initial optimization value 122 from optimization variable bounds 128 includes, for example, selecting the midpoint between bounds. [0048] Since optimization is for a single variable, the optimization in 120 arrives at a solution with acceptable precision in practice in a relatively small number of iterations, generally less than ten.
  • the optimization is solved using a bound-constrained nonlinear optimizer (the cost function is an average NPV 136 for the value of interest for FOPR, and the optimization bounds are specified e.g. by economic business constraints).
  • the present invention quickly arrives at an optimized FOPR plateau value (which has a corresponding optimal plateau duration due to the one-to-one relationship between L P it and FOPR).
  • the optimization in 120 considers averaging operators.
  • the porosity may be modeled as a probability -weighted arithmetic mean for the porosity distribution for each model in a set (the porosity distribution for the reservoir model describes the amount of pore/empty space for the spatial region modeled).
  • Other reservoir properties may be averaged likewise. Thereafter, a reservoir flow simulation is performed considering average properties.
  • a reservoir flow simulation can be run for each reservoir model in the set, and the average operator is applied to all the production profiles obtained.
  • This second option captures better nonlinear effects in the propagation of uncertainty but at the expense of being computationally more expensive than performing a single simulation on an average reservoir model.
  • the present invention propagates the uncertainty from the reservoir model to the final, resulting 130 FOPR, plateau duration and net present value (this latter including cost models for the injection/production facilities).
  • the optimal NPV is obtained by an iterative method using the FOPR variable. This method may also be solved using the plateau duration because the one- to-one correspondence between both variables. That is, the optimization 120 is reproduced replacing the FOPR variables by the unit 140 resulting in a method expressed in the L P it variable.
  • Figure 2B shows an example using a preferred design system (e.g., 100 of Figure 1) for iteratively developing a production profile that yields a previously specified oil production plateau duration 146.
  • the inverse relation between FOPR and plateau duration cannot be directly obtained from flow simulation.
  • the inverse relation can be estimated providing a one-dimensional function based on the knowledge of the engineer, designer or other expert; or, it can be obtained in an accurate manner as described hereinbelow.
  • flow simulation over a reservoir model provides the production plateau duration for such reservoir model.
  • the plateau duration 148 is determined for an average production profile obtained via reservoir flow simulation for all models used to quantify uncertainty (e.g., 1 18 in Figure 1).
  • this average production profile could be approximated using only one reservoir flow simulation for an average reservoir model.
  • a value for FOPR 122 is projected or supplied (e.g., by an engineer, designer or other expert) to the average production unit 134 (this unit also quantifies the associated/propagated uncertainty).
  • the unit 140 solves for a single variable (field oil/liquid production rate).
  • the unit 150 checks the result to determine if the average plateau duration is the value desired 146 or, alternatively, if it is the closest value to the desired value that can be obtained.
  • the solving unit 140 receives or determines a new FOPR 152 within FOPR bounds 128. These bounds may e.g. correspond to limits of FOPR or of the plateau duration value given by economic units. In the next iteration, the unit 140 solves for a new production plateau value and associated uncertainty for the new value FOPR 152. [0055] If the check 150 determines that the result is the value desired 146 or, alternatively, if it is the closest value to the desired value that can be obtained, the final FOPR result 154 is made available.
  • the unit 140 solves the average oil production given a plateau duration for a set of reservoir models.
  • the design system 100 may apply clustering/sampling techniques to the reservoir model set to reduce/limit the number of models considered to a relatively small number, e.g., a few tens of models.
  • the present invention can be applied to a set of wells previously grouped, for example, using geological and/or surface distance constraints.
  • the nonlinear optimizer propagates uncertainty, for example, by averaging production profiles obtained from reservoir flow simulation for each reservoir model in the set.
  • production forecasts are thus a collection of predictions from all reservoir models in the set and associated uncertainties.
  • a preferred design system 100 determines predictions (where oil production presents plateau profiles) from complex reservoir flow simulations, and further, while preserving a level of accuracy in the design satisfactory for most practical applications. Accordingly, the present invention has application to scenarios that may require a quick decision in a short period of time (i.e., in hours or at most a few days).
  • the optimal FOPR is determined, for a predetermined replacement factor (the rate between the injected flow and the produced flow, typically 1), the rate over time for each individual well (injectors and producers) is determined for instance using a commercial program and therefore the controls for the production plateau.
  • a predetermined replacement factor the rate between the injected flow and the produced flow, typically 1.
  • the present invention provides a much more efficient approach for finding field production controls than prior art ad-hoc and trial- and-error design approaches. Further, the present invention is computationally much less expensive than prior approaches that were based on general simulation-based optimizations, typically considering a large number of optimization variables (e.g., a few hundreds of variables) that very often required thousands of reservoir flow simulations. Instead, the present invention reduces design decisions to a single variable solution for quickly arriving at a solution, e.g., the field oil production plateau rate FOPR or the duration of that plateau.
  • FOPR field oil production plateau rate

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Abstract

L'invention concerne un système, un procédé et un produit de programme informatique pour déterminer des contrôles de production d'énergie pour une production de champs d'hydrocarbures (pétrole) souterraine, et plus particulièrement pour spécifier des contrôles permettant de maintenir une production de champs optimales (au moyen d'un profil de type plateau dans le temps).
PCT/US2014/040963 2013-06-06 2014-06-04 Sélection et optimisation de contrôles de champs de pétrole pour plateau de production Ceased WO2014197637A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
ESEP13382214 2013-06-06
EP13382214.8A EP2811107A1 (fr) 2013-06-06 2013-06-06 Procédé de sélection et d'optimisation de commande de champ de pétrole d'un plateau de production
US14/220,869 US9921338B2 (en) 2013-06-06 2014-03-20 Selecting and optimizing oil field controls for production plateau
US14/220,869 2014-03-20

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WO2014197637A1 true WO2014197637A1 (fr) 2014-12-11
WO2014197637A4 WO2014197637A4 (fr) 2015-01-22

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113240161A (zh) * 2021-04-27 2021-08-10 中国石油天然气股份有限公司 净现值预测模型建立方法、装置、存储介质及电子设备
CN114841390A (zh) * 2021-02-01 2022-08-02 中国石油天然气股份有限公司 层系重组方法、装置、计算机设备及存储介质
US11634980B2 (en) 2019-06-19 2023-04-25 OspreyData, Inc. Downhole and near wellbore reservoir state inference through automated inverse wellbore flow modeling

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070265815A1 (en) * 2006-05-15 2007-11-15 Benoit Couet Method for optimal gridding in reservoir simulation
US20100332442A1 (en) * 2008-04-21 2010-12-30 Vikas Goel Stochastic programming-based decision support tool for reservoir development planning
US20110011595A1 (en) * 2008-05-13 2011-01-20 Hao Huang Modeling of Hydrocarbon Reservoirs Using Design of Experiments Methods
US20110308792A1 (en) * 2010-06-22 2011-12-22 Le Ravalec Mickaele Method for operating an oil pool based on a reservoir model gradually deformed by means of cosimulations

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070265815A1 (en) * 2006-05-15 2007-11-15 Benoit Couet Method for optimal gridding in reservoir simulation
US20100332442A1 (en) * 2008-04-21 2010-12-30 Vikas Goel Stochastic programming-based decision support tool for reservoir development planning
US20110011595A1 (en) * 2008-05-13 2011-01-20 Hao Huang Modeling of Hydrocarbon Reservoirs Using Design of Experiments Methods
US20110308792A1 (en) * 2010-06-22 2011-12-22 Le Ravalec Mickaele Method for operating an oil pool based on a reservoir model gradually deformed by means of cosimulations

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11634980B2 (en) 2019-06-19 2023-04-25 OspreyData, Inc. Downhole and near wellbore reservoir state inference through automated inverse wellbore flow modeling
CN114841390A (zh) * 2021-02-01 2022-08-02 中国石油天然气股份有限公司 层系重组方法、装置、计算机设备及存储介质
CN113240161A (zh) * 2021-04-27 2021-08-10 中国石油天然气股份有限公司 净现值预测模型建立方法、装置、存储介质及电子设备

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