Applying a Genetic Neuro-Model Reference Adaptive Controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between 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 an “auto-regressive with extra input signals,” or ARX model and on a Genetic Algorithm (GA) to control the ROP. INTRODUCTION Operational costs associated with petroleum offshore drilling have risen significantly in the past few years. Deepwater reservoirs, whose exploration had been previously considered economically unfeasible, have become the target of the oil industry, pushing drilling rigs to increasing water depths.