Oil Well Performance Prediction and Diagnosis System Using Computational Intelligent Models

Description

Computational Intelligent systems are the automated detection of meaningful patterns in data, uncertainty recognition and optimization. The complex oil production performance prediction and diagnosis require a novel approach to forecasting, handling of uncertainty/vagueness and optimization in place of, or supplementing the traditional numerical mathematical modeling existing in the industry. There is often an abundance of different types of geological, petro-physical, well and production data that can be linked and utilized to match production history and to obtain reliable forecasts very quickly with intelligent systems. Computational Intelligence (CI) includes four main areas: Evolutionary Computation (genetic algorithms and genetic programming), Swarm Intelligence, Fuzzy Systems and Neural Networks. These models are of immense applications in the petroleum engineering systems. Neural Network as well as Support Vector Machines are based on the concept of machine learning. Fuzzy Logic is used to model vague, imprecise and uncertain situations which are prevalent in the oil and gas systems.