Considering the importance of cost reduction in the petroleum industry, especially in drilling operations, this study focused on the minimization of the well-path length, for complex well designs, compares the performance of several metaheuristic evolutionary algorithms. Genetic, ant colony, artificial bee colony and harmony search algorithms are evaluated to seek the best performance among them with respect to minimizing well-path length and also minimizing computation time taken to converge toward global optima for two horizontal wellbore cases: (1) a real well offshore Iran; (2) a well-studied complex trajectory with several build and hold sections. A primary aim of the study is to derive less time-consuming algorithms that can be deployed to solve a range of complex well-path design challenges. This has been achieved by identifying flexible control parameters that can be successfully adjusted to tune each algorithm, leading to the most efficient performance (i.e., rapidly locating global optima while consuming minimum computational time), when applied to each well-path case evaluated. The comparative analysis of the results obtained for the two case studies suggests that genetic, artificial bee colony and harmony search algorithms can each be successively tuned with control parameters to achieve those objectives, whereas the ant colony algorithm cannot. Keywords Metaheuristic algorithms • Well-path designing • Well-path optimization • Genetic algorithm • Harmony search • Artificial bee colony List of symbols Definitions of wellbore trajectory variables (modified after Shokir et al. 2004) 1 , 2 , 3 First, second and third hold angles, °
Optimizing the trajectory of directional wellbores is essential to minimize drilling costs and the impacts of potential drilling problems. It poses multi-objective optimization challenges. Well-design optimization models initially focus on wellborelength minimization, but ideally also need to consider minimizing the surface torque during drilling and address, among other constraints, collision avoidance with offset wells. A novel trajectory-optimization model is described that computes the separation factor along the wellbore. It employs a genetic optimization algorithm with an objective function that maximizes the minimum separation factor along the entire length of a wellbore. Plausible well trajectories are identified within a feasible solution space defined by user-identified constraints. The simplicity and effectiveness of the proposed model are demonstrated using a case study involving real well data from the Reshadat oil field offshore southern Iran. In the case considered, a proposed well trajectory is identified as unsafe in terms of its minimum separation factor with an offset well and is re-planned with the proposed model to achieve a safer trajectory.
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