Life-cycle production optimization of an oil field with an adjoint-based gradient approach

Forouzanfar, Fahim; Rossa, Ernesto Della; Russo, Roberta; Reynolds, Albert C.

doi:10.1016/j.petrol.2013.11.024

Cited by 36 publications

(13 citation statements)

References 19 publications

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“…Recently, there has been an increased focus on the application of different model-based optimization techniques for optimal control to achieve improved reservoir-management strategies. Most of these studies have used relatively simple models, whereas a limited number of studies such as Bailey et al (2005), Sarma et al (2008), Alhuthali et al (2009), Chaudhri et al (2009), Forouzanfar et al (2013), van Essen et al (2010, and Raniolo et al (2013) have used realistic field-scale or sector models. Most of the studies that use realistic real field models concerned single-objective optimization on a single geological realization, with the exception of Alhuthali et al (2009) and Raniolo et al (2013), who performed single-objective optimization by use of an ensemble of geological realizations.…”

Section: Introductionmentioning

confidence: 99%

Ensemble-Based Multiobjective Optimization of On/Off Control Devices Under Geological Uncertainty

Fonseca

Leeuwenburgh²,

Rossa

et al. 2015

SPE Reservoir Evaluation &Amp; Engineering

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We consider robust ensemble-based (EnOpt) multiobjective production optimization of on/off inflow-control devices (ICDs) for a sector model inspired by a real-field case. The use of on/off valves as optimization variables leads to a discrete control problem. We propose a reparameterization of such discrete controls in terms of switching times (i.e., we optimize the time at which a particular valve is either open or closed). This transforms the discrete control problem into a continuous control problem that can be efficiently handled with the EnOpt method. In addition, this leads to a significant reduction in the number of controls that is expected to be beneficial for gradient quality when using approximate gradients. We consider an ensemble of sector models where the uncertainty is described by different permeability, porosity, net/ gross ratios, and initial water-saturation fields. The controls are the ICD settings over time in the three horizontal injection wells, with approximately 15 ICDs per well. Different optimized strategies resulting from different initial strategies were compared. We achieved a mean 4.2% increase in expected net present value (NPV) at a 10% discount rate compared with a traditional pressure-maintenance strategy. Next, we performed a sequential biobjective optimization and achieved an increase of 9.2% in the secondary objective (25% discounted NPV to emphasize shortterm production gains) for a minimal decrease of 1% in the primary objective (0% discounted NPV to emphasize long-term recovery gains), as averaged over the 100 geological realizations. The work flow was repeated for alternative numbers of ICDs, showing that having fewer control options lowers the expected value for this particular case. The results demonstrate that ensemble-based optimization work flows are able to produce improved robust recovery strategies for realistic field-sector models against acceptable computational cost.

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Section: Introductionmentioning

confidence: 99%

Ensemble-Based Multiobjective Optimization of On/Off Control Devices Under Geological Uncertainty

Fonseca

Leeuwenburgh²,

Rossa

et al. 2015

SPE Reservoir Evaluation &Amp; Engineering

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“…In addition to robust optimization studies there is a large number of papers describing deterministic well control optimization over the life cycle of the reservoir both with and without the use of gradients; see, e.g., Sudaryanto and Yortsos (2000), , Sarma et al (2008b), van Essen et al (2010), Forouzanfar et al (2013a, Kourounis et al (2014) or, for overviews, Jansen (2011) andEcheverrıa Ciaurri et al (2011). Moreover, all of the CLRM studies referred to in Section 3 involve robust or deterministic well control and/or well location optimization.…”

Section: Application Case Reservoir Engineering -Long-term Reservoir mentioning

confidence: 99%

Recent Developments in Closed-Loop Approaches for Real-Time Mining and Petroleum Extraction

Benndorf

2017

Math Geosci

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Advanced data acquisition and process modelling technology provide 'real-time' data and decision support capacity for different aspects of the resource extraction process. Closed-loop approaches have recently been applied to utilize information extracted from these data in combination with advanced computing technology for improved production control in mineral resource extraction. Similar techniques have been developed in the petroleum industry combining computer-assisted model updating with model-based production optimization. This contribution reviews recent developments and methods applied, highlights differences and assesses the potential value added for both application domains. The focus here is on the two main constituents of closed-loop concepts, data assimilation and optimization. Technological readiness of the constituents is assessed, and gaps for further technology development are identified. The value added is illustrated by means of selected cases. Key Wordsclosed-loop reservoir management, real-time mining, data assimilation, optimization Published as Benndorf, J. and Jansen, J.D., 2017: Recent developments in closed-loop approaches for realtime mining and petroleum extraction. Mathematical Geosciences 49 (3) 277-306.

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“…On the other hand, gradient-based optimization methods require continuous parameters, and because the well position in (mainly commercial) reservoir simulators is usually defined in terms of its discrete coordinate in the numerical grid, the usage of derivative-based optimization algorithms is hampered. This can be overcome by re-parameterization [12,29,30]. Nevertheless, derivative-free optimization algorithms [25] are often applied to well placement studies [13].…”

Section: Introductionmentioning

confidence: 99%

Stochastic optimization strategies applied to the OLYMPUS benchmark

et al. 2019

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In this work, we discuss the application of stochastic optimization approaches to the OLYMPUS case, a benchmark challenge which seeks the evaluation of different techniques applied to well control and field development optimization. For that matter, three exercises have been proposed, namely, (i) well control optimization; (ii) field development optimization; and (iii) joint optimization. All applications were performed considering the so-called OLYMPUS case, a synthetic reservoir model with geological uncertainty provided by TNO (Fonseca 2018). Firstly, in the well control exercise, we successfully applied an ensemble-based approximate gradient method in a robust optimization formulation. Secondly, we solve the field development exercise using a genetic algorithm framework designed with special features for the problem of interest. Finally, in order to evaluate further gains, a sequential optimization approach was employed, in which we run one more well control optimization based on the optimal well locations. Even though we utilize relatively well-known techniques in our studies, we describe the necessary adaptations to the algorithms that enable their successful applications to real-life scenarios. Significant gains in the expected net present value are obtained: in exercise (i) a gain of 7% with respect to reactive control; for exercise (ii) a gain of 660% with respect to a initial well placement based on an engineering approach; and for (iii) an extra gain of 3% due to an additional well control optimization after the well placement optimization. All these gains are obtained with an affordable computational cost via the extensive utilization of high-performance computing (HPC) infrastructure. We also apply a scenario reduction technique to exercise (i), with similar gains obtained in the full ensemble optimization, however, with substantially inferior computational cost. In conclusion, we demonstrate how the state-of-the-art optimization technology available in the model-based reservoir management literature can be successfully applied to field development optimization via the conscious utilization of HPC facilities. Keywords Field development optimization • Well control optimization • Joint optimization • Ensemble-based optimization • Genetic algorithm • OLYMPUS challenge • Uncertainty quantification Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Life-cycle production optimization of an oil field with an adjoint-based gradient approach

Cited by 36 publications

References 19 publications

Ensemble-Based Multiobjective Optimization of On/Off Control Devices Under Geological Uncertainty

Ensemble-Based Multiobjective Optimization of On/Off Control Devices Under Geological Uncertainty

Recent Developments in Closed-Loop Approaches for Real-Time Mining and Petroleum Extraction

Stochastic optimization strategies applied to the OLYMPUS benchmark

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