Beschreibung
The purpose of this research is to develop a mathematical model as a reservoir estimation tool for naturally fractured reservoirs with dual lateral well configurations. The tool proposed in this study includes a forward artificial neural network (ANN) with the ability to predict production data via known reservoir and well design parameters. The proposed tool also includes an inverse ANN component that can be used to predict the permeability and porosity of matrix and fracture, as well as fracture spacing and reservoir thickness. By means of the proposed tool, the user would be able to analyze instantaneously predicted reservoir or production data with less cost and time. The software involved in developing the tool were MATLAB, EXCEL, and a commercial modeling software. The procedures are introduced and discussed in the following chapters including training data generation, selecting training data sets, training forward and inverse ANN models. Moreover, a graphical user interface (GUI) is developed and assembled for each ANN, which allows the user to view results in numerical and graphical formats.
Autorenporträt
Jia Lu was born in Suzhou, China. He started his college education in 2010 and was accepted later into Integrated Graduate and Undergraduate Program in the College of Earth and Mineral Sciences at the Pennsylvania State University. He successfully graduated with both B.S. and M.S. degrees in May 2015, along with the accomplishment of this research.
