Optimization for Drilling Schedule of Wells in the Development of Heavy Oil Reservoirs

Lamas, L. F.; Botechia, Vinícius Eduardo; Schiozer, Denis José; Delshad, Mojdeh

doi:10.5419/bjpg2017-0014

Cited by 6 publications

(1 citation statement)

References 2 publications

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“…The cost of finding a strong and admissible heuristic for drilling-problem can be prohibitive since the evaluation of heuristic function requires simulations. In this paper, we use a heuristic function in which all remaining wells are drilled simultaneously at the next step [28,40], as this estimate can be obtained inexpensively and generally provides an overestimate of the NPV. Such a heuristic is guaranteed to find the true optimal drilling order.…”

Section: Learned Heuristic Searchmentioning

confidence: 99%

Fast robust optimization using bias correction applied to the mean model

Wang

Oliver

2020

Comput Geosci

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Ensemble methods are remarkably powerful for quantifying geological uncertainty. However, the use of the ensemble of reservoir models for robust optimization (RO) can be computationally demanding. The straightforward computation of the expected net present value (NPV) requires many expensive simulations. To reduce the computational burden without sacrificing accuracy, we present a fast and effective approach that requires only simulation of the mean reservoir model with a bias correction factor. Information from distinct controls and model realizations can be used to estimate bias for different controls. The effectiveness of various bias-correction methods and a linear or quadratic approximation is illustrated by two applications: flow optimization in a one-dimensional model and the drilling-order problem in a synthetic field model. The results show that the approximation of the expected NPV from the mean model is significantly improved by estimating the bias correction factor, and that RO with mean model bias correction is superior to both RO performed using a Taylor series representation of uncertainty and deterministic optimization from a single realization. Use of the bias-corrected mean model to account for model uncertainty allows the application of fairly general optimization methods. In this paper, we apply a nonparametric online learning methodology (learned heuristic search) for efficiently computing an optimal or near-optimal robust drilling sequence on the REEK Field example. This methodology can be used either to optimize a complete drilling sequence or to optimize only the first few wells at a reduced cost by limiting the search depths.

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Section: Learned Heuristic Searchmentioning

confidence: 99%

Fast robust optimization using bias correction applied to the mean model

Wang

Oliver

2020

Comput Geosci

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Influence of polymer properties on selection of production strategy for a heavy oil field

Lamas

Botechia

Correia

et al. 2018

Journal of Petroleum Science and Engineering

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Efficient Optimization of Well Drilling Sequence with Learned Heuristics

Wang

Oliver

2019

Day 1 Tue, May 14, 2019

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When preparing a field development plan, the forecast value of the development can be sensitive to the order in which the wells are drilled. Determining the optimal drilling sequence generally requires many simulation runs. In this paper, we formulate the sequential decision problem of drilling schedule as one of finding a path in a decision tree that is most likely to generate the highest net-present-value (NPV). A non-parametric online learning methodology is developed to efficiently compute the sequence of drilling wells that is optimal or near optimal. The main ideas behind the approach are that heuristics from relaxed problems can be used to estimate the maximum value of complete drilling sequences constrained to previous wells, and that multiple online learning techniques can be used to improve the accuracy of the estimated values. The performance of various heuristic methods is studied in a model for which uncertainty in properties is neglected. The initial heuristic utilized in this work generates a higher estimated NPV than the actual maximum NPV. Although such a heuristic is guaranteed to find the true optimal drilling order when used in A* search, the cost of the search can be prohibitive unless the initial heuristic is highly accurate. For the variants of heuristic search methods with weighting parameters, the results show that it may not be possible to identify parameters that can be used to find a solution quickly without sacrificing the accuracy of the estimated NPV in this drilling sequence problem. In contrast, the online learned heuristics based on the observations from previous drilling steps are demonstrated to outperform the other variants of heuristic methods in terms of running time, accuracy of the estimated value and solution quality. Multi-learned heuristic search with space reduction is an efficient and fast method to find a solution with high value. Continuing the search with space restoration is guaranteed to improve the solution quality or find the same solution as the multi-learned heuristic search without any space reduction.

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Optimization for Drilling Schedule of Wells in the Development of Heavy Oil Reservoirs

Cited by 6 publications

References 2 publications

Fast robust optimization using bias correction applied to the mean model

Fast robust optimization using bias correction applied to the mean model

Influence of polymer properties on selection of production strategy for a heavy oil field

Efficient Optimization of Well Drilling Sequence with Learned Heuristics

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