Benefits and Limitations of Assisted History Matching

Abstract

Some years ago, a new methodology called automatic history matching was approached by the scientific community. The idea consisted in treating history matching as an optimization process, i.e. defining a cost function representative of the discrepancy between measured (real) and simulated data, and in minimizing the cost function. The minimization of the cost function can be obtained by applying a suitable optimization algorithm. Optimization and non-linear programming were not new methodologies in the field of applied mathematics. However, the selection of the most adequate optimization algorithm among those available in the technical literature is not trivial, and the number of independent variables involved in complex reservoir simulation does not make the solution of the optimization problem a standard procedure. In fact, the main criticalities of a history matching process change for each analyzed reservoir. As a consequence, the identification of an optimization methodology appropriate for a wide variety of reservoirs is quite impossible. Therefore, automatic history matching remains a dream and, more realistically, assisted history matching can be the target. The concept of assisted history matching is that reservoir engineers are still in charge of reservoir model calibration, but they can rely on reliable optimization tools to better explore the parameter space and to speed up the convergence to one or more solutions. The aim of this paper consists in discussing benefits, limitations and drawbacks of assisted history matching by applying new techniques, based on multi objective optimization and heuristic strategies. Attention is focused on the possibility offered by these methodologies of obtaining a number of calibrated reservoir models. Each of these models can then be used for field performance prediction so as to obtain a representative evaluation of the risks associated to any reservoir development scenario.

Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting

“…Although multi-objective optimization can be determined, a perfect history matching model leads to poor prediction [17]. Thus, the history matching approach is formidable and requires a long time, which renders a lot of work [18].…”

System Identification Based Proxy Model of a Reservoir under Water Injection

“…Decline curve analysis (DCA) and numerical reservoir simulation are classical methods that are frequently employed to forecast reservoir performance [2][3][4][5][6]. Each of these methods has strengths, limitations, and restrictions [7].…”

“…Thus, reservoir property data used in modeling and forecasting are only best estimates and will not be exact representatives of the reservoir at large. Besides, the historymatching stage by which the inferred reservoir properties are adjusted until the numerical simulation model reasonably mimics the actual reservoir is a formidable task and has substantial limitations [3,5].…”

“…The so-called best realization could also produce a geologically inconsistent reservoir model [11]. The reader is advised to refer to [3] which has critically summarized the limitations of assisted history matching.…”

Determine the Inflow Performance Relationship of Water Producing Gas Well Using Multiobjective Optimization Method

“…Han et al [7] presented a multiobjective evolutionary algorithm applied to history matching of water flooding projects, that is to search a feasible set of geological properties showing the reliable future performance. Cancelliere et al [8] discussed benefits, limitations and drawbacks of assisted history matching, based on multi objective optimization and heuristic strategies. Attention was focused on the possibility offered by these methodologies of obtaining a number of calibrated reservoir models.…”