Optimizing Reservoir Performance Under Uncertainty with Application to Well Location

Aanonsen, Sigurd Ivar; Eide, Asle; Holden, Lars; Aasen, J.O.

doi:10.2523/30710-ms

Cited by 19 publications

(4 citation statements)

References 2 publications

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“…This cheap proxy can then be used to replace the actual evaluation function during the large number of iterations of the optimization process. Earlier researchers have looked into using proxies for numerical models [4][5][6][7] . Such a proxy method requires an initial investment of numerical simulations that will be used to calibrate the proxy in order to make it as accurate as possible.…”

Section: Helper Methodsmentioning

confidence: 99%

“…Kriging: Kriging can be used to create a proxy. Previously kriging has been used in a similar fashion by Pan and Horne 5 and also Aanonsen et al 4 . Kriging is an algorithm based on the theory of regionalized variables and can be used as a multidimensional interpolation and extrapolation algorithm.…”

Section: Helper Methodsmentioning

confidence: 99%

“…Bittencourt and Horne 2 investigated optimization of well placement using a hybrid of JHQHWLF DOJRULWKP *$ DQG SRO\WRSH PHWKRG * \DJ OHU DQG * PUDK 3 optimized production rates for a gas storage field using GAs. Aanonsen et al 4 coupled a CPU efficient reservoir simulator with an optimization algorithm and made use of a kriging proxy to find optimum well locations. Pan and Horne 5 also used kriging to decrease the necessary number of simulations required to optimize well location.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimization of Well Placement in a Gulf of Mexico Waterflooding Project

Güyagüler¹,

Horne²,

Rogers

et al. 2002

SPE Reservoir Evaluation &Amp; Engineering

101

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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.

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Section: Helper Methodsmentioning

confidence: 99%

Section: Helper Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of Well Placement in a Gulf of Mexico Waterflooding Project

Güyagüler¹,

Horne²,

Rogers

et al. 2002

SPE Reservoir Evaluation &Amp; Engineering

101

View full text Add to dashboard Cite

show abstract

“…Work on FDO problems started with the placement or scheduling of a limited number of wells (for early studies see, e.g., Bittencourt [7]; Beckner and Song [5]; Aanonsen et al [1]; Bittencourt and Horne [8]; Guyaguler et al [16] Badru and Kabir [3]; Bangerth et al [4] and references therein). In most of these studies, some simplifications were typically made, such as consideration of vertical wells only, or of straight well segments only, or of single-well trajectories only.…”

Section: Introductionmentioning

confidence: 99%

Ensemble-based well trajectory and drilling schedule optimization—application to the Olympus benchmark model

2020

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The general field development optimization problem is complex due to the potentially large number of controls of mixed type and discontinuities in the objective function related to varying numbers and types of wells being placed in a discretized grid. This may make the problem challenging or even unsuitable for certain types of optimization methods that rely on, e.g., the availability of (adjoint) gradients. It is not yet clear which alternative approaches will be most useful. Here we investigate the application of stochastic gradient-based optimization techniques to field development optimization. Since their initial application to large-scale well rate and pressure control problems, such techniques have been shown to produce useful results of practical value also for other types of reservoir optimization problems such vertical well placement, well drilling scheduling, and water-alternating-gas strategy optimization. Here we introduce an efficient parameterization for well trajectory optimization and discuss a simple way to handle the number of wells that is placed. The full field development problem is split into subproblems that are addressed sequentially. The sequential workflow is applied to the Olympus benchmark model which represents a complex green field development optimization challenge. Initial experiments show that the proposed approach based on stochastic gradient methods is able to find much improved development strategies, as defined by the number and trajectories of wells, a platform location and a drilling sequence, at relatively low computational cost. We additionally identify a number of possible improvements to the applied workflow that are expected to make it applicable to other field cases of intermediate complexity.

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Optimization of a reservoir development plan using a parallel genetic algorithm

Carter

Matthews

2008

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A parallel genetic algorithm has been applied successfully to design a production plan that is substantially superior to that obtained using a conventional engineering approach. The reservoir, a dipping structure, was expected to yield optimum production using a rolling line drive from downdip to updip positions. The simulation allowed for 3800 positions for each of 11 wells, giving a total of 1.3×10 31 options. The genetic algorithm sampled 1650 of these and was able to identify seven solutions that would increase production by over 30% compared with the rolling line drive. In contrast, a random search using 850 samples managed to find only two plans that improved production; in each of these cases the improvement was less than 1%.

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Optimizing Reservoir Performance Under Uncertainty with Application to Well Location

Cited by 19 publications

References 2 publications

Optimization of Well Placement in a Gulf of Mexico Waterflooding Project

Optimization of Well Placement in a Gulf of Mexico Waterflooding Project

Ensemble-based well trajectory and drilling schedule optimization—application to the Olympus benchmark model

Optimization of a reservoir development plan using a parallel genetic algorithm

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