A key challenge in developing brown fields is identifying a strategy that enables placement of horizontal wells in a field riddled with existing, depleted wells. These wells have drained multiple reservoirs in proximity to current target intervals, resulting in altered in-situ pressures that may impose additional technical and economic drilling risks. This work presents a new technique which optimizes well path design by combining hybrid nature-based metaheuristics with spline curvature to navigate around depleted zones. The proposed method is validated by testing on synthetic and actual well cases. The nature-based metaheuristic method employed is a modified firefly algorithm with hybrid implementations of mutation and annealing. It considers a potential well’s starting coordinates, target coordinates, possible obstructions, subsurface stress distribution, an RSS tool’s dogleg limitation range, kick off depth limitation, and required length of lateral section to optimize overall wellbore length, all of which can directly be linked to the economics behind drilling a well. The functionality of the designed algorithm is examined with both synthetic data and publically available field data. Further complexity is added in the model by including geomechanical stresses in the model when available. Comparisons of overall wellbore length, wellbore orientation, and wellbore profile energy are also provided for a case in the Wattenberg basin derived from public data. Using sparse information, the algorithm was able to automatically design entire well paths in a relatively short period for all cases and the final solutions resembled industry solutions based on minimum design constraint. The uniqueness of the work is highlighted by the algorithm’s ability to converge towards optimal solutions which can help the operator shift work load from well design to more critical tasks.
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