Optimization of Well Placement in a Gulf of Mexico Waterflooding Project

Abstract

Determination of the location of new wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, and economic criteria. Various approaches have been proposed for this problem. Among those, direct optimization using the simulator as the evaluation function, although accurate, is in most cases infeasible due to the number of simulations required. In this study a hybrid optimization technique based on the genetic algorithm (GA), polytope algorithm, kriging algorithm and neural networks is proposed. Hybridization of the GA with these helper methods introduces hill-climbing into the stochastic search and also makes use of proxies created on the fly. Performance of the technique was investigated on a set of exhaustive simulations for the single well placement problem and it was observed that the number of simulations required was reduced significantly. This reduction in the number of simulations reduced the computation time, enabling the use of full-scale simulation for optimization even for this full-scale field problem. It was also seen that the optimization technique was able to avoid convergence to local maxima due to its stochastic nature. Optimal placement of up to four water injection wells was studied for Pompano, an offshore field in the Gulf of Mexico. Injection rate was also optimized. The net present value of the waterflooding project was used as the objective function. Profits and costs during the time period of the project were taken into consideration.

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

“…Although some of them are adjoint-based (Wang et al 2007;Zandvliet et al 2008a;Sarma and Chen 2008;Vlemmix et al 2009), the most effective techniques use 'non-classical' methods such as genetic algorithms, particle swarm optimization or evolutionary strategies (e.g. Güyagüler et al 2002;Yeten et al 2003;Bangerth, 2006;Owunalu and Durlofsky, 2010, Bouzarkouna et al 2012Forouzanfar et al 2013b, Jesmani et al 2016. There is also an increasing number of studies involving the joint optimization of well controls and locations (e.g.…”

In-situ recovery of heavy-oil from fractured carbonate reservoirs: Optimization of steam-over-solvent injection method

“…This framework integrates genetic algorithm with orthogonal arrays for experimental design and response surface model as an objective function proxy. Genetic Algorithm is a population-based search technique which applies the concept of "survival of the fittest," commonly used in the genes science (Guyaguler et al 2002;Chen et al 2010). An initial population or genotype is typically constructed by random sampling of the solution space or by utilizing different experimental design strategies such as orthogonal arrays.…”

Well placement optimization: A survey with special focus on application for gas/gas-condensate reservoirs

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