An Experimental Study of an Evolutionary Tool for Scheduling in Oil Wells

Data-Driven Joint Chance-Constrained Optimization for the Workover Rig Scheduling Problem

“…With this assumption, McKechnie et al (2002) presented a management system for workover operations that allows control of the rig schedule and the required equipment. Later, a different problem was introduced by Pandolfi et al (2010). The authors described a system called PAE (evolutionary algorithm for planning), applied in an extension of the WRRSP considering other resources such as crews and equipment used to service onshore wells.…”

“…The authors described a system called PAE (evolutionary algorithm for planning), applied in an extension of the WRRSP considering other resources such as crews and equipment used to service onshore wells. Later, Villagra et al (2013) adapted the model proposed by Pandolfi et al (2010) to consider penalty functions and repair algorithms to transform infeasible solutions into feasible ones in a constrained version of the problem. Achkar et al (2019a,b) proposed a MILP model for this extended WRRSP, considering a heterogeneous rig fleet, precedence constraints, crew shifts, failure risks, and minimizing production loss and costs.…”