Optimization of Oil Field Development using a Surrogate Model: Case of Miscible Gas Injection

Simonov, Maksim; Shubin, Andrei; Penigin, Artem; Perets, D. S.; Белоногов, Е. В.; Margarit, A. S.

doi:10.2118/196639-ms

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Several studies exist in the literature that use optimization to improve field development, by finding the optimal configuration of parameters like well characteristics, drilling program, production and injection strategies, etc., to maximize the economic outcomes or the oil/gas recovery. Many of these studies (Jonsbråten 1998;Túpac et al 2007;Bellout et al 2012;Simonov et al 2019) depend on complex reservoir models to represent the production system performance, but in early phases of FDP, complex reservoir models are typically not available. In addition, by using reservoir models only, these studies are not considering the effect of the backpressure of wells and network systems on the sand face when computing production profiles and this can lead to significant errors.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, it is important to consider the effect of uncertainties when planning the future exploitation of a field. Some of the studies previously mentioned do not consider uncertainties when optimizing the field development process (Túpac et al 2007;Storvold 2012;Simonov et al 2019). On the other hand, Litvak et al (2007), Litvak and Angert (2009) and Litvak et al (2011) made use of BP's Top-Down Reservoir Modeling method (Williams et al 2004) to perform optimization during field development while including uncertainty analysis with uncertainty in the reservoir.…”

Section: Introductionmentioning

confidence: 99%

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Decision support method for early-phase design of offshore hydrocarbon fields using model-based optimization

González

Stanko

Hoffmann³

2019

J Petrol Explor Prod Technol

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This paper presents the development of a method to provide decision support in the feasibility studies and concept planning phases of oil and gas field development. The objective in developing the methodology was to provide an easy-to-use facility to integrate the production-governing elements of oil and gas fields that capture the integrated production and economic performance of the system. This in a modular and scalable manner includes numerical optimization and uncertainty analyses needed to support engineering decisions. The method follows a series of steps that allow determining the optimal field production profile, drilling schedule, type of offshore structure, pressure support method and selection of artificial lift. The first step consists of creating efficient (low running time) proxy models of the production performance of the field and the costs figures associated with the project. The proxy model of the production performance is based on curves of maximum production rates versus cumulative production and contains all relevant field design features and computation of the most relevant performance indicators to consider in the evaluation. The proxy model to estimate the costs associated with the project is based on linear equations function of production rates and number of wells. The second step is to perform numerical optimization to find optimal production profile and drilling schedule that maximize the net present value of the specific development strategies considered. For the last step, an evaluation of the effect of uncertainties on the results of the numerical optimization using probabilistic methods is performed. The method was applied in a synthetic production system based on public data of Wisting field (currently under development). The field is a remote low-energy oil reservoir located in the Barents Sea. Nine strategies, obtained from the combination of three recovery support methods and three processing facilities, were compared using the net present value as decision factor. The best strategy consists of using a tension leg platform as processing facility and multiphase boosting plus water injection as recovery support method. This strategy generated the highest production and required the lowest costs, resulting in the highest profitability. It was demonstrated that the methodology successfully finds optimal field design features while quantifying the effect of uncertainties.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decision support method for early-phase design of offshore hydrocarbon fields using model-based optimization

González

Stanko

Hoffmann³

2019

J Petrol Explor Prod Technol

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Analysis of different objective functions in petroleum field development optimization

Rostamian,

de Sousa Miranda,

Mirzaei-Paiaman

et al. 2024

J Petrol Explor Prod Technol

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Oilfield development optimization plays a vital role in maximizing the potential of hydrocarbon reservoirs. Decision-making in this complex domain can rely on various objective functions, including net present value (NPV), expected monetary value (EMV), cumulative oil production (COP), cumulative gas production (CGP), cumulative water production (CWP), project costs, and risks. However, EMV is often the main function when optimization is performed under uncertainty. The behavior and performance of different objective functions has been investigated in this paper, when EMV is the primary criterion for optimization under reservoir and economic uncertainty. One of the goals of this study is to provide insights into the advantages and limitations of employing EMV as the sole objective function in oil field development decision-making. The designed optimization problem included sequential optimization of design variables including well positions, well quantity, well type, platform capacity, and internal control valve placements. A comparative analysis is presented, contrasting the outcomes obtained from optimizing the EMV-based objective function against traditional objective functions. The study underscores the importance of incorporating multiple objective functions alongside EMV to guide decision-making in oilfield development. Potential benefits in minimizing CGP and CWP are revealed, aiding in the mitigation of environmental impact and optimization of resource utilization. A strong correlation between EMV and COP is identified, highlighting EMV’s role in improving COP and RF.

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Historical Window-Enhanced Transfer Gaussian Process for Production Optimization

et al. 2022

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Summary As a crucial step of reservoir management, production optimization aims to make the optimal scheme for maximal economic benefit measured by net present value (NPV) according to reservoir states. Despite the remarkable success, more advanced methods that can get higher NPV with less time consumed are still in urgent need. One main reason for limiting the optimization performance of existing methods is that historical data cannot be fully used. For a practical reservoir, production optimization is generally implemented in multiple stages, and substantial historical data are accumulated. These hard-won data obtained with lots of time encapsulate beneficial optimization experience and in-depth knowledge of the reservoir. However, when encountered with an unsolved optimization task in new stages, most methods discard these historical data, optimize from scratch, and gradually regain the knowledge of the reservoir with massive time for “trial and error” to find the right optimization direction, which is time-consuming and affects their practical application. Motivated by this, a novel method named historical window-enhanced transfer Gaussian process (HWTGP) for production optimization is proposed in this paper. Each optimization stage is regarded as a time window, and the data in historical windows are adopted as a part of training data to construct the transfer Gaussian process (TGP), which guides the whole optimization process. To solve the high-dimensional feature of practical problems, the prescreening framework based on a dimension-reduction method named Sammon mapping is introduced. The main innovation of HWTGP is that like experienced engineers, it can extract beneficial reservoir knowledge from historical data and transfer it to the target production-optimization problem, avoiding massive time for “trial and error” and getting superior performance. Besides, HWTGP has a self-adaptive mechanism to avoid harmful and ineffective experience transfer when tasks in historical and current windows are unrelated. To verify the effectiveness of HWTGP, two reservoir models are tested 10 times independently and results are compared with those obtained by differential evolution (DE) and a surrogate-based method. Experimental results show that HWTGP can achieve the optimal well controls that can get the highest NPV, and has significantly enhanced convergence speed with excellent stability, proving the effectiveness of transferring historical data.

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Optimization of Oil Field Development using a Surrogate Model: Case of Miscible Gas Injection

Cited by 5 publications

References 2 publications

Decision support method for early-phase design of offshore hydrocarbon fields using model-based optimization

Decision support method for early-phase design of offshore hydrocarbon fields using model-based optimization

Analysis of different objective functions in petroleum field development optimization

Historical Window-Enhanced Transfer Gaussian Process for Production Optimization

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