A Genetic Neuro-Model Reference Adaptive Controller for Petroleum Wells Drilling Operations

Authors:
Tiago C. Fonseca;Jose Ricardo P. Mendes;Adriane B. S. Serapiao;Ivan R. Guilherme
Affiliations:
State University of Campinas, Brazil;State University of Campinas, Brazil;Sao Paulo State University, Brazil;Sao Paulo State University, Brazil
Venue:
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Year:
2006

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Abstract

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP.Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided.