How Does the Definition of the Objective Function Influence the Outcome of History Matching?

Abstract

Summary In this work we investigate how the form of the objective function can influence the results and the speed of history matching (HM). The objective function definition depends on the production variables included in the objective and their weighting factors. These choices may impact, for instance, the speed of assisted history matching. We demonstrate how the choice of the suitable form for the objective function used in HM should depend on the particular reservoir development problem at stake. The work presents a comparative study between different objective function formulations used in history matching a synthetic reservoir example. An industry standard stochastic optimization algorithm - evolution strategy was chosen for the comparative benchmarking of the impact of the objective function choice on history matching. The synthetic model represents waterflooding case with 3 production, 3 injection wells, 7 years of simulated history and 8 parameters of reservoir uncertainty. The findings from the comparative study are not limited to a particular assisted HM algorithms applied. Processing and analysis of the experimental results confirmed that the formulation of the objective function is important, since its value allows the algorithm to accelerate towards finding better HM solutions. The study demonstrates how different objective function formulations lead to different computational costs to reach the history matched solution. This means an optimal objective function formulation for each particular problem should provide the fastest convergence. Novelty of the work is in demonstrating how the different objective function formulation can help to history match a reservoir model with minimized computational cost when solving different production problems. We show that the objective function should not be defined in the same way for any history matching process but rather adjusted to the particular application allowing to reach required history match at minimum computational cost. This will give more chances to history match real complex hydrocarbon field models within a reasonable time.