This paper proposes an assisted history matching (AHM) and uncertainty analysis workflow that was applied to facilitate the history matching of a giant carbonate reservoir in Middle East. The objective was to identify and quantify reservoir uncertainties and assess their impact on the field performance. In addition, to create a sufficient number of realizations to allow combinations of all uncertainties to capture a combined effect.

A real field case is represented by a consistent workflow that iteratively updates the ranges and number of reservoir uncertainties constrained by the actual measurements. The process has the following steps: definition of global uncertainty, sensitivity analysis, exclusion of less influential parameters, experimental design, revision of uncertainty matrix, and run optimization algorithms. The approach was firstly implemented at a global level and then continued to a regional level. The primary objective function is consisted of oil and water production mismatches, and the plan is to upgrade the objective function to include more parameters for further model HM enhancements.

Initially, the workflow was based on five uncertainty parameters. Ten sensitivity analysis cases were performed and tornado chart analysis suggested excluding some parameters that have less impact on the match quality, hence the objective function. Next, experimental design using Latin Hypercube was performed which allows seeing a combined effect of uncertainty parameters. During several experimental design iterations, the uncertainty parameter matrix was revised and a total number of uncertainty parameters was increased from 5 to 17. Finally, a total number of 260 experimental cases were completed, however, no good history match case was obtained. Therefore, a transition from the global level to a regional level was performed. The most sensitive identified uncertainties at global level were absolute permeability, vertical permeability anisotropy, pore volume and fault transmissibility. At the regional level, additional permeability multipliers for well regions were added to the uncertainty matrix. After that, a good quality matched cases were obtained.

Field scale and complexity were the main drive to implement AHM workflow. In a giant carbonate reservoir with long history and complex geology, a classical history matching method with unique solution cannot assure an accurate model predictability. The key advantages of this approach were the facilitating of the HM process and reducing of the total calculation time.

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