Optimization of nonconventional wells under uncertainty using statistical proxies

Artus, Vincent; Durlofsky, Louis J.; Onwunalu, Jerome Emeka; Aziz, Khalid

doi:10.1007/s10596-006-9031-9

Cited by 88 publications

(46 citation statements)

References 17 publications

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“…In practice, the limit is estimated via the following expression: where n e is the number of unemployed bees; c is a constant coefficient with a recommended value of 0.5 or 1 [10]. The new solution is generated within the entire search space.…”

Section: Abc Algorithmmentioning

confidence: 99%

Algorithms for the Optimization of Well Placements—A Comparative Study

Udoeyop¹,

Oboh²,

Afiakinye³

2018

ACES

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The Artificial Bee Colony (ABC) is one of the numerous stochastic algorithms for optimization that has been written for solving constrained and unconstrained optimization problems. This novel optimization algorithm is very efficient and as promising as it is; it can be favourably compared to other optimization algorithms and in some cases, it has been proven to be better than some known algorithms (like Particle Swarm Optimization (PSO)), especially when used in Well placement optimization problems that can be encountered in the Petroleum industry. In this paper, the ABC algorithm has been modified to improve its speed and convergence in finding the optimum solution to a well placement optimization problem. The effects of variations of the control parameters for both algorithms were studied, as well as the algorithms' performances in the cases studied. The modified ABC (MABC) algorithm gave better results than the Artificial Bee Colony algorithm. It was noticed that the performance of the ABC algorithm increased with increase in the number of its optimization agents for both algorithms studied. The modified ABC algorithm overcame the challenge posed by the use of uniformly generated random numbers with very rough NPV surface. This new modified ABC algorithm proposed in this work will be a great tool in optimization for the Petroleum industry as it involves Well placements for optimum oil production.

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Section: Abc Algorithmmentioning

confidence: 99%

Algorithms for the Optimization of Well Placements—A Comparative Study

Udoeyop¹,

Oboh²,

Afiakinye³

2018

ACES

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“…Under the risk neutrality assumption, the objective reduces to maximizing the expectation of the monetary value with respect to probability distributions of uncertain geological parameters 1), 2) . Now suppose that some information-gathering activities can be conducted to reduce some degree of geological uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Value of Information in Optimizing Reservoir Development under Geological Uncertainty

Goda

Ohno

Satô

2017

J. Jpn. Petrol. Inst.

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The value of information (VOI) analysis has been recognized as a useful tool for measuring how much the expected monetary value can be increased as a result of an information-gathering activity. The VOI analysis is usually applied in the context of decision making under geological uncertainty, where only a relatively small number of decision alternatives are taken into account. In this paper we discuss possible applications of the VOI analysis in optimization of reservoir development under geological uncertainty. More precisely, by means of the VOI analysis, we evaluate how much an information-gathering activity provides an increase of the maximized expected monetary value, where the maximization is considered in a probabilistic sense to account for geological uncertainty. In this application we often need to deal with a quite large or even infinite number of candidate solutions within an optimization problem, which may cause trouble for an efficient implementation of the VOI analysis. After introducing a general methodology to estimate the VOI in our context, we validate our methodology through a toy problem, and moreover apply to a simple waterflooding problem. We find out that the VOI analysis can be conducted efficiently even in optimization under geological uncertainty by specializing our methodology properly.

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“…Most previous GA implementations have involved bGA, though a cGA implementation was recently presented [18]. GA-based procedures have been applied to optimize the locations of both vertical wells [3,4,22] and nonconventional wells [2,31,38,39]. A number of surrogate modeling procedures have also been applied in conjunction with GAs, as discussed in the papers cited above.…”

Section: Introductionmentioning

confidence: 99%

Application of a particle swarm optimization algorithm for determining optimum well location and type

2009

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Determining the optimum type and location of new wells is an essential component in the efficient development of oil and gas fields. The optimization problem is, however, demanding due to the potentially high dimension of the search space and the computational requirements associated with function evaluations, which, in this case, entail full reservoir simulations. In this paper, the particle swarm optimization (PSO) algorithm is applied for the determination of optimal well type and location. The PSO algorithm is a stochastic procedure that uses a population of solutions, called particles, which move in the search space. Particle positions are updated iteratively according to particle fitness (objective function value) and position relative to other particles. The general PSO procedure is first discussed, and then the particular variant implemented for well optimization is described. Four example cases are considered. These involve vertical, deviated, and dual-lateral wells and optimization over single and multiple reservoir realizations. For each case, both the PSO algorithm and the widely used genetic algorithm (GA) are applied to maximize net present value. Multiple runs of both algorithms are performed and the results are averaged in order to achieve meaningful comparisons. It is shown that, on average, PSO outperforms GA in all cases considered, though the relative advantages of PSO vary from case to case. Taken in total, these findings are very promising and demonstrate the applicability of PSO for this challenging problem.

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Optimization of nonconventional wells under uncertainty using statistical proxies

Cited by 88 publications

References 17 publications

Algorithms for the Optimization of Well Placements—A Comparative Study

Algorithms for the Optimization of Well Placements—A Comparative Study

Value of Information in Optimizing Reservoir Development under Geological Uncertainty

Application of a particle swarm optimization algorithm for determining optimum well location and type

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