Petroleum reservoirs are geologically large and complex. In order to technically and economically optimize the exploitation of these hydrocarbon reserves, multimillion dollar investments, reliable numerical reservoir simulation models should be constructed to predict the reservoir performance and response under different production scenarios. Reservoir numerical simulation models can be only trusted after good calibration with actual historical data. The model is considered to be calibrated if it is able to reproduce the historical data of the reservoir it represents. This calibration process is called history matching, and this is the most time consuming phase in any numerical reservoir simulation study. Traditional history matching is carried out through a trial and error approach of adjusting model parameters until a satisfactory match is obtained. The biggest challenge that faces the simulation engineer during this critical phase is that, several combinations of reservoir history matching parameters might be satisfactorily matching the past dynamic behavior of the system which makes the process ill-posed due to the non-uniqueness solution issues. Traditional history matching is accordingly a time-consuming, expensive, and often frustrating procedure and as a consequence, the assisted history matching technique has been arisen.

In assisted history matching technique, the simulated data is compared to the historical data by means of a misfit function, objective function. The history matching problem is translated into an optimization problem in which the misfit function is an objective function bounded by the model constraints. The objective function is minimized using appropriate optimization algorithm and thus the results are the model parameters that best approximate the fluid rates and pressure data recorded during the reservoir life.

The objectives of this paper are to clarify the idea behind assisted history matching process and discuss its different aspects, experimental design, proxy models, and optimization algorithms, and show how these aspects are integrated to overcome the frustrating nature of the history matching problem. Finally, guide lines are provided to enhance the design of the assisted history matching process.

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