Viscosity and gas/oil ratio curves estimation using advances to neural networks

Abstract

In oil and gas industry, prior prediction of certain properties is needed ahead of exploration and facility design. Viscosity and gas/oil ratio (GOR), are among those properties described through curves with their values varying over a specific range of reservoir pressures. However, the usual prediction approach could result into curves that are not consistent, exhibiting scattered behaviour as compared to the real curves. In this paper two advances to artificial neural networks are implemented to solve the problem. These are Support Vector Regressors and Functional Networks. Statistical error measures have been used and showed the high performance of the proposed techniques. Moreover, the predicted curves are consistent with the actual curves.