Oil Well Performance Diagnosis System Using Fuzzy Logic Inference Models

This paper considers an approach to design a fuzzy logic controller to detect incipient abnormal production of wells in a reservoir. The daily operation of an oil and gas production system requires many decisions, which may affect the volumes of oil produced and the cost of production oil. These decisions are taken at different levels in the organization, but eventually they will reach the physical production system. One of the decisions may require controlling oil and gas production by changing the bean (choke) size. When a production rate is defined and if for some reasons the production is low, the management may consider the need to increase production by increasing the bean size and conversely. The fuzzy logic controller, which is a method of a rule-based decision making based on human knowledge, was developed for the oil well performance diagnosis. Simulation results demonstrate how the designed fuzzy logic controller performs well production fault detection. The purpose of this paper is to illustrate how Fuzzy logic inference system which is an automatic method of generating fuzzy rules, can predict the flow rate, as a vital parameter in determining the choke size.