Neural-Network Approach To Predict Well Performance Using Available Field Data

Abstract

Abstract Accurate prediction of future well performance is of great importance for petroleum reservoir management. This paper presents a practical neural network approach to predict existing and infill oil well performance using available filed data, such as well production history and well configuration information. It serves as a practical, cost-effective and robust tool for oilfield production and management. Well production, well spacing and the time-dependent information are used to train the neural network. The time-dependent information of wells are incorporated in a manner of time series for establishment of neural network. After the neural network is established, it is used to predict future performance of existing and infill wells. No reservoir data is currently used in the establishment of neural network, therefore it can predict well production performance in absence of reservoir data. Primary production of two data sets (each has 9 wells) in North Robertson Unit located in west Texas was tested using this approach. The results demonstrate that this approach is powerful in rapid projection of existing wells’ future performance, as well as the performance prediction of infill drilling wells.