Characterization of Partially Sealing Faults from Pressure Transient Data: An Artificial Neural Network Approach

Well testing is an effective and convenient tool for characterizing the hydrocarbon reservoirs. Although, the forward solution part of the well testing theory is well advanced, the corresponding inverse solution protocols especially for the complex domains are not well established. In this paper, a new inverse solution methodology, which utilizes the artificial neural network technology in analyzing the pressure transient data, is presented. The proposed methodology is applied towards analyzing the pressure transient data collected from an anisotropic faulted reservoir. The network development begins with a simple system and the level of complexity of the system is increased as the investigation progresses. The final goals of the analysis include determination of the principal permeability values, porosity of the reservoir, distance to the fault, orientation of the fault with respect to the flow directions and the sealing characteristics of the fault. The primary goal of this work is to test the capability of the ANN methodology as an engineering tool in pressure transient analysis applications.