الفهرس يوجد فقط 14 صفحة متاحة للعرض العام
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المستخلص
Oil and gas reservoirs are characterized by qualitative and quantitative values using pressure transient analysis. The well test is conducted by creating a flow disturbance in the well and recording the related response of the bottom-hole pressure. Well test analysis consists of two main phases: (1) the recognition of the entire reservoir model, and (2) the model parameter estimation. The objective of this study is to apply the Artificial Neural Network (ANN) technology to identify the reservoir model. A multilayer neural network had been used with back propagation optimization algorithm for the recognition process. The required training and test datasets have been generated by using the analytical solutions of commonly used reservoir models. Nine networks have been constructed; each one differentiates among six boundary models. Most commonly found reservoir models of different inner, outer boundary and reservoir medium are included (e.g. vertical, fracture and horizontal wells; homogenous, dual porosity and radial composite reservoirs; and infinite, one sealing fault, two sealing faults, rectangle and circle boundaries)