Joint optimization of number of wells, well locations and controls using a gradient-based algorithm

Forouzanfar, Fahim; Reynolds, Albert C.

doi:10.1016/j.cherd.2013.11.006

Cited by 59 publications

(22 citation statements)

References 12 publications

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“…As a result we choose to use the number of simulation runs as our performance indicator to compare the optimization strategies. This is a widely used measure in the well placement and control optimization literature (Isebor et al, 2014a;Forouzanfar and Reynolds, 2014;Brouwer and Jansen, 2004;Humphries et al, 2013). It is also worth mentioning that, GPS and CMA-ES need an initial guess to start the optimization processes.…”

Section: Parametermentioning

confidence: 99%

“…For some problems, particularly well placement, the optimization surface can be very rough, which results in discontinuous gradients (Ciaurri et al, 2011). However, the gradient-based methods are often the most efficient methods especially for the optimal well control problem (Zhao et al, 2013;Handels et al, 2007;Vlemmix et al, 2009;Wang et al, 2007;Forouzanfar and Reynolds, 2014).…”

Section: Introductionmentioning

confidence: 99%

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A multilevel coordinate search algorithm for well placement, control and joint optimization

Xiang

Haynes

Feng

2016

Computers & Chemical Engineering

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Determining optimal well placements and controls are two important tasks in oil field development. These problems are computationally expensive, nonconvex, and contain multiple optima. The practical solution of these problems require efficient and robust algorithms. In this paper, the multilevel coordinate search (MCS) algorithm is applied for well placement and control optimization problems. MCS is a derivative-free algorithm that combines global and local search. Both synthetic and real oil fields are considered. The performance of MCS is compared to generalized pattern search (GPS), particle swarm optimization (PSO), and covariance matrix adaptive evolution strategy (CMA-ES) algorithms. Results show that the MCS algorithm is strongly competitive, and outperforms for the joint optimization problem and with a limited computational budget. The effect of parameter settings for MCS are compared for the test examples. For the joint optimization problem we compare the performance of the simultaneous and sequential procedures and show the utility of the latter.

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Section: Parametermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multilevel coordinate search algorithm for well placement, control and joint optimization

Xiang

Haynes

Feng

2016

Computers & Chemical Engineering

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“…However the CMA algorithm, like many other randomized search algorithms, is designed to be robust even when such discontinuities exist in the objective function. In Forouzanfar and Reynolds (2014) a method for obtaining a relatively smooth NPV function by distributing the rate of a well among its neighbouring blocks is presented.…”

Section: Well Trajectory Parametrizationmentioning

confidence: 99%

“…On the other hand, the superior computational efficiency of some local search optimization algorithms compared to the heuristic optimization methods motivated their application to the well placement optimization problem. For example, gradient-based algorithms using adjoint gradient (Handels et al, 2007;Wang et al, 2007;Vlemmix et al, 2009;Forouzanfar and Reynolds, 2013), methods based on the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm (Bangerth et al, 2006;Li and Jafarpour, 2012), derivative-free methods (Forouzanfar and Reynolds, 2014;Isebor et al, 2013) and direct search algorithms such as HookeJeeves direct search (HJDS) and generalized pattern search (GPS) (Bellout et al, 2012) have all been applied for the optimal well placement problem.…”

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confidence: 99%

A Covariance Matrix Adaptation Algorithm for Simultaneous Estimation of Optimal Placement and Control of Production and Water Injection Wells

Forouzanfar

Poquioma

Reynolds

2015

SPE Reservoir Simulation Symposium

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In this paper, we present both simultaneous and sequential algorithms for the joint optimization of well trajectories and their lifecycle controls. The trajectory of a well is parameterized in terms of six variables which define a straight line in 3D. In the simultaneous joint optimization algorithm, the set of controls of a well throughout the life-cycle of the reservoir is parameterized in terms of the singular vectors corresponding to the largest singular values of a pre-specified temporal covariance matrix which imposes a temporal correlation on the controls at each well. In this approach, well controls are parameterized in terms of a few optimization parameters which significantly reduces the dimension of the joint optimization problem. Moreover, the imposed smoothness on the well controls will result in temporally smooth well controls. We use an implementation of the Covariance Matrix Adaptation -Evolution Strategy (CMA-ES) optimization algorithm to solve the defined optimization problem. In the sequential optimization algorithm, first the trajectories of the wells are optimized using CMA-ES optimization algorithm while the controls of the wells are pre-specified. Once the optimum trajectories of the wells are obtained, the life cycle production optimization step is performed in order to find the optimal well controls for the specified well trajectories. For the production optimization step, we compare the performance of three production optimization algorithms which are: the standard EnOpt algorithm, the standard CMA-ES algorithm for production optimization and, a variant of the CMA-ES algorithm for production optimization in which we set the initial covariance matrix equal to a pre-specified covariance which imposes a temporal correlation on the controls of each well. The performance of the proposed algorithms is tested for the joint optimization of well trajectories and controls of injectors and producers for the PUNQ reservoir model. The proposed simultaneous well placement-well control optimization algorithm obtained slightly better results than the sequential optimization framework. CMA-ES algorithm showed good performance for both well placement and production optimization purposes. Moreover, the CMA algorithm with a pre-specified covariance that imposes a temporal correlation on the well controls showed a superior performance compared to EnOpt for the life cycle production optimization step of the sequential framework. General well placement optimization problemWell placement optimization is the key element in the decision making process for preparing the optimal development plan for a reservoir. To solve a "general well placement optimization" problem, the number of wells, their trajectory and controls should be optimized in order to maximize a prescribed objective function, which is typically the net present value (NPV) of the hydrocarbon production from the reservoir. However, very few papers have addressed this general problem as it is too computationally expensive. Yeten et al. (2002) proposed ...

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“…There is also an increasing number of studies involving the joint optimization of well controls and locations (e.g. Bailey et al 2005, Isebor et al 2014bForouzanfar and Reynolds, 2014;Humphries and Haynes, 2015;Forouzanfar et al 2016). Finally there are some studies that address comprehensive field development planning optimization including, e.g., surface facilities, artificial lift options and drilling schedules (e.g.…”

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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Joint optimization of number of wells, well locations and controls using a gradient-based algorithm

Cited by 59 publications

References 12 publications

A multilevel coordinate search algorithm for well placement, control and joint optimization

A multilevel coordinate search algorithm for well placement, control and joint optimization

A Covariance Matrix Adaptation Algorithm for Simultaneous Estimation of Optimal Placement and Control of Production and Water Injection Wells

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

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