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Determining the value of reservoir data by using nonlinear production optimization techniques

This paper describes a new method of analyzing the cost of uncertainty in reservoir data. The method is based on the use of nonlinear optimization techniques. Nonlinear optimization techniques are mathematical or heuristic methods of determining the set of decision variable values that maximizes an objective function value. Given the appropriate model, optimization techniques can be used to determine the best set of production engineering parameters for an oil or gas field. The optimization technique considered is the Genetic Algorithm (GA). However, the optimized engineering parameters depend largely upon the input reservoir rock and fluid properties. If the reservoir property values differ from the expected values, the engineering parameters selected as optimal by numerical optimization will probably be suboptimal. This leads to a new technique for assessing the cost of uncertainty in reservoir data. The cost of uncertainty in a reservoir parameter depends upon the discrepancy between the optimal net present value (NPV) for the actual parameter value and the NPV realized when optimization is performed for the expected value of the parameter. For a parameter, such as porosity or permeability, a probability distribution is determined. From this distribution, five representative values are chosen to discretize the distribution. The engineering parameters are optimized for the expected property value (the third representative value). The NPVs based on these engineering parameter values and the other four reservoir parameter values are determined. Then the optimal NPVs are determined for the four remaining reservoir parameter values. Combining the probability of each representative parameter value and the discrepancy between optimal and suboptimal NPVs for that parameter value yields the cost of uncertainty for that reservoir parameter. When the cost of uncertainty has been established for each reservoir parameter, the engineer will be able to decide which parameter should be further tested, and to what extent. This allows careful weighing of the cost of testing procedures, including seismic, logging, and transient testing, against the cost of uncertainty in the reservoir parameter values.