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

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 per...

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Hauptverfasser: Fonseca, T.C., Mendes, J.R.P., Serapiao, A.B.S., Guilherme, I.R.
Format: Tagungsbericht
Sprache:eng
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Zusammenfassung: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.
DOI:10.1109/CIMCA.2006.8