Identification of non-Darcian flow effect in double-porosity fractured aquifer based on multi-well pumping test

Abstract

Well testing in double-porosity fractured aquifers or oil and gas reservoirs is one of the long-lasting research problems in subsurface hydrology and petroleum engineering, where the double-porosity implies that the media of concern can be approximated as a two interrelated continuums (fracture network and rock matrix) with two distinctively different porosities. However, most of those studies in double-porosity fractured aquifers only concern Darcian flow in the fractured continuum. In this study, we will expand such studies from Darcian flow regime to non-Darcian flow regime, which is most likely to be the case for the field well testing site reported in this investigation. New solutions based on power law and a linearization approximation are obtained for hydraulic heads in Laplace domain and subsequently inverted to give spatiotemporal distributions in real-time domain. The non-Darcian flow model in single porosity media is obtained by setting the leakage coefficient (C) to 0 or the storage coefficient of the matrix (Sm) to 0, where C is the rate of fluid transfer between the fracture and rock matrix. Parameter analysis is conducted using dimensionless formats. A larger dimensionless quasi conductivity coefficient KqD, a larger drawdown in the early stage, and a smaller drawdown in the late stage. The dimensionless parameters CD and ϕ influence the drawdown in transitional state, and the values get larger, the drawdown in transitional state drop smaller. A pumping test conducted in Huangtun, Anhui province of China has been applied to test the non-Darcian flow effect in a double-porosity aquifer, in which a particle swarm optimization (PSO) algorithm will be applied here to seek the optimal hydraulic parameters. As the result shows, the observed drawdown data fit well with the new model in the pumping stage. The predicted recovery drawdown curve with the calculated parameters of the new model also performs well with the field drawdown data, which supports the applicability of the new model to interpret the field data.