Abstract
Directional drilling decision making is complicated by the presence of multiple objectives and constraints. Existing directional drilling advisory methods find a balanced solution through optimization over a weighted sum of the objectives, but the weights are often chosen arbitrarily or based on rough estimates. In addition, existing methods only return a single solution to a multi-objective problem that likely does not have a single, best solution. The work presented here overcomes these limitations by identifying a set of feasible, diversely performing (i.e., Pareto optimal) solutions for the directional driller to choose from. In this novel approach to directional drilling steering optimization, the directional drilling steering problem is framed as a constrained optimal control problem over a finite horizon. A wellbore propagation model that is calibrated at set interval using field data was used to estimate system responses for a bent-sub mud motor drilling assembly. Pareto front approximation was used to solve the problem and a multi-objective evolutionary search method was leveraged to solve this optimization problem and present the directional driller with a set of Pareto optimal solutions that satisfy the problem constraints. The proposed Pareto front approximation approach was validated against a set of field test cases. These demonstrate that the proposed method is capable of finding feasible, high-performing solutions to these real-world problems sufficiently fast to be of use for real-time directional drilling advisory in the field.
NSTL
Elsevier