A Computational Model for Wells’ Performance Analysis

The ability to identify underperforming wells and recover the remaining oil in place is a cornerstone for effective reservoir management and field development strategies. As advancement in computing programming capabilities continuous to grow, Python has become an attractive method to build complicated statistical models that predicts, diagnose or analyze well performance, efficiently and accurately. The aim of this study is to develop a computational model that will allows us to diagnose and analyze well performance using nodal analysis with the help of python. In this study, python was used to compute Nodal analysis method using Darcy and Vogel Equations. A case study was carried out using the data obtained from a field operating in the Niger Delta. Again, sensitivity of tubing size was conducted using python. The results obtained showed that a computational model with python has the ability to visualize, model and analyze wells performances. This technique will petroleum engineers to better monitor evaluate and enhance their production operation without the need for expensive softwares. This will reduce operating cost increases revenue.