History Matching of Field Production using Design of Experiments

Abstract

Abstract An important aspect of reserves estimates is to quantify the contribution of uncertainties in underlying parameters such as structure, Sand extent, Fault Seal, Aquifer support, etc. If field production history is available, it can be incorporated to generate reliable measures of uncertainties for these parameters and identify the most likely field models. History Matching (HM) can be solved successfully using the Design of Experiment (DOE) method to define modeled scenarios. In the past decade DOE methodology has been used to compute estimated reserves while minimizing the simulation effort. This paper describes its extension to History Matching. The process of HM using DOE is a four stage process. The first step is to identify an uncertainty frame-work listing the uncertain parameters (X) and history matching parameters (Y). The second stage is to generate a design (the specified model combinations of the reservoir parameters) capable of estimating the effect and interactions of all parameters. Thirdly, the specified models are made and simulated and the response surface y(x) calculated, relating reservoir simulator input x and each output y. The last step is to predict the history matching parameter values for all possible model scenarios and measure goodness of history match for each model. The History Match can be quantified by the D-Score, a measure of the difference between the realized and predicted value of the history matching parameter standardized across modeling error. The D-score based probabilistic measures are flexible and allow the user to incorporate prior information. The above methodology for History Match will be presented for a field, which has over 11 years of oil production and 7 years of water-cut. We will demonstrate that History Matching using DOE is reliable, fast, efficient, and can quantify the probability of scenario occurrence.