Duc H. Le

Optimal choice of a surveillance operation using information theory

We consider the problem of choosing among a suite of potential reservoir surveillance operations. We frame the problem in terms of two questions: (1) Which surveillance operation is the most useful? (2) What is the expected value of the reduction in uncertainty in the reservoir variable J (e.g., cumulative oil production) that would be achieved if we were to conduct each surveillance operation to collect and history match the data obtained? Note that the objective is to answer these questions with an uncertain reservoir description and without any actual measurements. We propose a procedure based on information theory to answer these questions. We apply the proposed method to two simple problems, a nonlinear toy problem and a simple waterflooding problem. The results are verified by an exhaustive history-matching procedure, which is reasonably rigorous but very computationally demanding. We find that the mutual information approach is a fast and reliable alternative to the history-matching approach.

Optimal Choice of a Surveillance Operation Using Information Theory

We consider the problem of choosing among a suite of potential reservoir surveillance operations. We frame the problem in terms of two questions: (1.) Which surveillance operation is the most useful? (2.) What is the expected value of the reduction in uncertainty in the reservoir variable J (e.g. cumulative oil production) that would be achieved if we were to conduct each surveillance operation to collect and history-match the data obtained? Note that the objective is to answer these questions with an uncertain reservoir description and without any actual measurements. We propose a procedure based on information theory to answer these questions. Question 1 is answered by calculating the mutual information between J and the vector of observed data. Question 2 is answered by estimating the expected value of the standard deviation (or P90-P10) of J in the posterior model from the conditional entropy of J. We apply the proposed method to two simple problems, a nonlinear toy problem and a simple water flooding problem. The results are verified by an exhaustive history matching procedure, which is reasonably rigorous but very computationally demanding. We find that the mutual information approach is a fast and reliable alternative to the history matching approach.