Applying a robust solution based on expert systems and GA evolutionary algorithm for prognosticating residual gas saturation in water drive gas reservoirs

Abstract

In strong water drive gas reservoirs (WDGRs), the water encroachment in the gas zone has adverse effects on the gas mobility and causes considerable volume of gas to be trapped behind water front; therefore estimation of residual gas saturation after water influx is an important parameter in estimation of gas reservoirs with strong aquifer support. It is difficult to achieve a thorough and exact understanding of water drive gas reservoirs. It depends on several parameters of petrophysical and operational features. In majority of the previous studies about residual gas saturation, the correlations were depended on petrophysical properties such as porosity, permeability, and initial gas saturation. Most of these correlations are well applied on limited dataset that they are constructed based on, but they are not applicable to dataset from other references. In other words, they are not capable of generalization. One reason for this might be different experimental methods to determine the residual gas saturation. In the present study, the prediction of residual gas saturation is presented utilizing Committee Machine Intelligent Technique and some well-known correlations are used for comparison. The reviewed correlations in this study generally do not provide good results and some of them that exhibit reasonable results demand some experimental parameters that are usually unavailable. In this study, two different intelligent models are proposed for spontaneous imbibition and force flood. The suggested models provide good results for the two cases; however, the prediction for force flood is not as exact as the results for the spontaneous imbibition. At the end, an outlier approach detection based on Leverage method was applied to investigate the applicability domain of the proposed models as well as possible outlier data.