Adaptive Rank-Based Selection of Geological Realizations for Optimum Field Development Planning

Uncertainties are present in many decision-making processes. In field development planning, these uncertainties, typically represented by a set of geological realizations, need to be propagated in response to any proposed alternative (solution). Incorporation of the full set of realizations results in the oil and gas field development optimization problem-where either an algorithm iteratively tries to find the best solution from all the possible alternatives or the best solution must be selected from a set of predefined engineering judgment-driven development scenarios (i.e., set of either well control or well placement settings)-becoming computationally demanding. As such, realization subset selection techniques are required to reduce the computational overhead. We first introduce a reformulation of the subset selection problem to one that aims at ensuring consistent ranking of alternatives between those obtained by the full set and the selected subset. We argue that this should be the ultimate goal of any subset selection technique in such problems. In addition, we also propose a technique which selects a subset that minimizes the difference between the rankings obtained by the full set and subset, for a small batch of alternatives. The key idea, which we investigate thoroughly, is that there is a positive association between the goodness (in terms of ranking alternatives) of the subset selected using a small batch of alternatives and its fidelity in ranking other alternatives. Unlike previous methods, this technique does not depend on selecting subjective (static) properties to perform the subset selection nor does it rely only on flow-response vectors of a base-case scenario. In this work, the proposed technique is assessed using well placement and well control development alternatives to determine the applicability within field development planning. Additionally, the proposed subset selection technique is implemented in an adaptive scheme to solve a well placement optimization problem. The results are promising as the proposed technique consistently selects subsets that are able to rank development alternatives in a similar manner to the full set regardless of the type of development strategy (well control settings or well placement). Furthermore, the implementation of the proposed technique in an adaptive scheme is able to reduce the computational costs, on average, by a factor close to 9 without compromising the solution found for well placement optimization.