History matching for reservoir simulation by use of semi-automatic iteration sample method

Reservoir history matching is a complicated inverse problem in the oil industry. The goal of history matching is to minimize the difference between simulated data and historical data, so oil companies can use this model to predict the future. The quality of history matching can dramatically influence the economic decision of a petroleum project. Therefore, improving the accuracy and efficiency of history matching is essential in the petroleum industry. Traditionally, history matching is often done using the trial-and-error method, which requires engineers to try different parameters until a good match is obtained. History matching usually requires running hundreds of models to get a good match, and the procedure is onerous and time-consuming. Oil companies are always looking for new methods and algorithms to improve the history matching procedure. In this paper, we develop a semi-automatic history matching method by using the idea of the hill-climbing algorithm and randomly sampling method. The goal of this research is to prove the method is effective. The method is evaluated using two 3D synthetic reservoir models that use production data as a condition: for model 1, was provided with a list of 8 parameters to change to get a match; Model 2 was a variant of Model 1, with significant changes in the number of wells and other properties. For this model, a list of 40 possible parameters to change for this match was provided. We wrote a Python program to achieve the semi-automatic procedure. The hill-climbing algorithm’s most significant issue is that it is very easy to be trapped in a local minimum. This paper shows that using the randomly sampling method and hill climbing algorithm together can significantly reduce the chance that the algorithm is trapped by a local minimum. The results of this research demonstrate that the method can improve the efficiency of the history matching procedure and give a relatively good result. It can be used as a powerful tool to perform reservoir history matching. The semi-automatic iteration sample procedure can easily handle hundreds of runs at the same time, and it allows us to work more efficiently with complicated reservoir models.