A Closer Look At Differential Evolution For The Optimal Well Placement Problem

Carosio, Grazieli Luiza Costa; Humphries, Thomas; Haynes, Ronald D.; Farquharson, Colin G.

doi:10.1145/2739480.2754772

Cited by 8 publications

(6 citation statements)

References 43 publications

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“…DE algorithm outperforms many other optimization algorithms because of its excellent performance in convergence speed and robustness (Huang et al 2006). Several papers have applied DE to solve optimization problems including well placement optimization and well control optimization (Carosio et al 2015). Therefore, we use DE algorithm to optimize the well placement.…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…DE algorithm is an evolutionary algorithm with the iterative process of mutation, crossover and selection operators (Carosio et al 2015). A population of D-dimensional vectors inside the problem bounds is generated as the initial population firstly.…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…Then, the trial vector y p,q t?1 is calculated according to the target vector x p,q t and the mutation vector x p.q t?1 by crossover operator (Carosio et al 2015):…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…In order to solve well placement optimization problem, both gradient-based and derivativefree optimization algorithms are applied (Yeten et al 2002;Bangerth et al 2006;Taware et al 2012;Bouzarkouna et al 2012;Nwankwor et al 2013;Awotunde 2014;Isebor et al 2014;Humphries and Haynes 2015;Carosio et al 2015;Khademi and Karimaghaee 2016;Bagheri and Masihi 2016), such as finite difference gradient (FDC), simultaneous perturbation stochastic approximation (SPSA), genetic algorithm (GA), particle swarm optimization (PSO) algorithm, covariance matrix adaptation evolution strategy (CMA-ES) algorithm and differential evolution (DE) algorithm. Due to the simple metaheuristic mechanism, DE algorithm has shown good performance in solving the real parameter optimization problem and global optimization problem (Carosio et al 2015). It has been used to deal with well placement optimization problem in many researches (Awotunde 2014;Carosio et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Due to the simple metaheuristic mechanism, DE algorithm has shown good performance in solving the real parameter optimization problem and global optimization problem (Carosio et al 2015). It has been used to deal with well placement optimization problem in many researches (Awotunde 2014;Carosio et al 2015). In this paper, we apply DE algorithm to solve the well placement optimization problem in offshore oilfield.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Well placement optimization for offshore oilfield based on Theil index and differential evolution algorithm

Chen

Feng

Zhang

et al. 2017

J Petrol Explor Prod Technol

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Proper well placement should match the heterogeneity of reservoir and development schedule, in which case the displacement is balanced. A balanced displacement process can achieve a good development performance of reservoir. Present parameters to evaluate the displacement balanced degree are not fit for high water-cut oilfields and need a specific reservoir simulator. In this work, a well placement optimization method for offshore oilfield is established. A displacement balanced degree evaluation method is proposed based on Theil index. By using minimum Theil index as the objective function, well placement optimization model for offshore oilfield is built. In this model, constraints about the available infilling scope and minimum inter-well distance are considered to find the realizable optimal location. This optimization model is solved by a derivative-free optimization algorithm, differential evolution algorithm. This method is applied and validated in a semisynthetic offshore oilfield to find optimal well locations. Results demonstrate that this proposed method can find the optimal well placement for offshore oilfield to achieve a more balanced displacement and a higher oil recovery.

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Section: Optimization Algorithmmentioning

confidence: 99%

Section: Optimization Algorithmmentioning

confidence: 99%

“…Then, the trial vector y p,q t?1 is calculated according to the target vector x p,q t and the mutation vector x p.q t?1 by crossover operator (Carosio et al 2015):…”

Section: Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Well placement optimization for offshore oilfield based on Theil index and differential evolution algorithm

Chen

Feng

Zhang

et al. 2017

J Petrol Explor Prod Technol

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Well placement optimization subject to realistic field development constraints

Jesmani¹,

Bellout²,

Hanea

et al. 2016

Comput Geosci

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Well control optimization using derivative-free algorithms and a multiscale approach

Xiang

Haynes

Feng

2019

Computers & Chemical Engineering

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Smart well technologies, which allow remote control of well and production processes, make the problem of determining optimal control strategies a timely endeavour. In this paper, we use numerical optimization algorithms and a multiscale approach in order to find an optimal well management strategy over the life of the reservoir. Optimality is measured in terms of the values of the net present value objective function. The large number of well rates for each control step make the optimization problem more difficult and at a high risk of achieving a suboptimal solution. Moreover, the optimal number of adjustments is not known a priori. Adjusting well controls too frequently will increase unnecessary well management and operation cost, and an excessively low number of control adjustments may not be enough to obtain a good yield. We investigate three derivative-free optimization algorithms, chosen for their robust and parallel nature, to determine optimal well control strategies. The algorithms chosen include generalized pattern search (GPS), particle swarm optimiza-CMA-ES, within the multiscale approach are considered.Keywords Well Control · Production Optimization · Derivative-Free Algorithms · Multiscale Approach rithms can be broadly placed in two categories: derivativebased algorithms and derivative-free algorithms [25].Derivative-based or gradient-based algorithms, take advantage of the gradient information to guide their search. This type of algorithm, commonly used in well control optimization, includes steepest ascent [42], conjugate gradient [2], and sequential quadratic programming methods [25]. Gradients of the objective function may be calculated by using an adjoint equation. This is an invasive approach, requiring a detailed knowledge of mathematics inside the reservoir simulator [25,8]. Other ways to approximate the gradients include methods such as finite difference perturbation [2], or the simultaneous perturbation stochastic approximation [42]. These algorithms assume a certain degree of smoothness of the objective function with respect to the optimization variables. Derivative-based algorithms are potentially very quick to converge but sometimes fall into local optimal.Derivative-free algorithms can be subdivided into local search methods and global search methods. Local derivative-free algorithms include generalized pattern search (GPS) [23], mesh adaptive direct search (MADS) [25], Hooke-Jeeves direct search (HJDS) [25], ensemble-based optimization (EnOpt) [32], covariance matrix adaptation evolution strategy (CMA-ES) [7,31], and so on. These methods have strong ability to find accurate optima in a local space, but may face with some difficulties in finding global optima, especially when a good initial guess is not available. Global derivativefree algorithms search through the entire space and provide techniques to avoid being trapped in local optima. Examples of global search algorithms include genetic algorithms (GAs) [3], particle swarm optimization (PSO) [26], and differential evolution (DE)...

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A Closer Look At Differential Evolution For The Optimal Well Placement Problem

Cited by 8 publications

References 43 publications

Well placement optimization for offshore oilfield based on Theil index and differential evolution algorithm

Well placement optimization for offshore oilfield based on Theil index and differential evolution algorithm

Well placement optimization subject to realistic field development constraints

Well control optimization using derivative-free algorithms and a multiscale approach

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