Drivers to the workover rig problem

Abstract When onshore oil wells reduce the production due to a malfunction, workover rigs are used to restore productivity. Given a set of wells needing maintenance and a set of workover rigs, the workover rig problem (WRP) consists of finding rig routes for minimizing the total production loss of the wells within a finite time horizon. The wells have different loss rates and require different services such as cleaning, reinstatement and stimulation. The rigs can be at different positions in the oil field and can have different equipment. This problem shares some of the same characteristics of the vehicle routing problems. Using a literature review, we offer some new ways to consider and approach the problem in its actual context.

[1]  Nicolas Jozefowiez,et al.  From Single-Objective to Multi-Objective Vehicle Routing Problems: Motivations, Case Studies, and Methods , 2008 .

[2]  Celso C. Ribeiro,et al.  Scheduling workover rigs for onshore oil production , 2006, Discret. Appl. Math..

[3]  Steve Sorrell,et al.  Shaping the global oil peak: A review of the evidence on field sizes, reserve growth, decline rates and depletion rates , 2012 .

[4]  Ralph Simon,et al.  Enhanced oil recovery: Definitions, fundamentals, applications, and research frontiers , 1981 .

[5]  Gilbert Laporte,et al.  Fifty Years of Vehicle Routing , 2009, Transp. Sci..

[6]  Guy Desaulniers,et al.  Tabu Search, Partial Elementarity, and Generalized k-Path Inequalities for the Vehicle Routing Problem with Time Windows , 2006, Transp. Sci..

[7]  David Pisinger,et al.  Subset-Row Inequalities Applied to the Vehicle-Routing Problem with Time Windows , 2008, Oper. Res..

[8]  Steve Sorrell,et al.  Oil futures: A comparison of global supply forecasts , 2010 .

[9]  Christos D. Tarantilis,et al.  Dynamic Vehicle Routing Problems , 2014, Vehicle Routing.

[10]  Glaydston Mattos Ribeiro,et al.  Efficient heuristics for the workover rig routing problem with a heterogeneous fleet and a finite horizon , 2014, J. Heuristics.

[11]  Shahin Gelareh,et al.  Solution methods for scheduling of heterogeneous parallel machines applied to the workover rig problem , 2015, Expert Syst. Appl..

[12]  Gilbert Laporte,et al.  The dynamic multi-period vehicle routing problem , 2010, Comput. Oper. Res..

[13]  Nicolas Jozefowiez,et al.  Multi-objective vehicle routing problems , 2008, Eur. J. Oper. Res..

[14]  John M. Gowdy,et al.  Technology and Petroleum Exhaustion: Evidence from Two Mega-Oilfields , 2007 .

[15]  S. M. Farouq Ali,et al.  Heavy oil—evermore mobile , 2003 .

[16]  C. Cipolla,et al.  Reservoir Modeling in Shale-Gas Reservoirs , 2010 .

[17]  A. Rahil Drilling Performance Management System , 2007 .

[18]  Paolo Toth,et al.  Recent advances in vehicle routing exact algorithms , 2007, 4OR.

[19]  Robert Clayton Bachman,et al.  Case Study: Evaluation of Horizontal Well Multistage Fracturing in the Viking Oil Formation , 2012 .

[20]  Burak Eksioglu,et al.  The vehicle routing problem: A taxonomic review , 2009, Comput. Ind. Eng..

[21]  Bruce L. Golden,et al.  The vehicle routing problem : latest advances and new challenges , 2008 .

[22]  Glaydston Mattos Ribeiro,et al.  A comparison of three metaheuristics for the workover rig routing problem , 2012, Eur. J. Oper. Res..

[23]  Julio Ortega Lopera,et al.  A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows , 2013, Comput. Ind. Eng..

[24]  Glaydston Mattos Ribeiro,et al.  A Grasp with Path-Relinking for the Workover Rig Scheduling Problem , 2010, Int. J. Nat. Comput. Res..

[25]  T. D. Mueller,et al.  A Mathematical Model of Reservoir Response During the Cyclic Injection of Steam , 1967 .

[26]  Gilbert Laporte,et al.  Solving a Dynamic and Stochastic Vehicle Routing Problem with a Sample Scenario Hedging Heuristic , 2006, Transp. Sci..

[27]  Christophe Duhamel,et al.  Models and hybrid methods for the onshore wells maintenance problem , 2012, Comput. Oper. Res..

[28]  Y. B. Adeboye,et al.  Prediction of reservoir performance in multi-well systems using modified hyperbolic model , 2011 .

[29]  Abel García-Nájera,et al.  An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows , 2011, Comput. Oper. Res..

[30]  Michel Gendreau,et al.  A review of dynamic vehicle routing problems , 2013, Eur. J. Oper. Res..

[31]  Glaydston Mattos Ribeiro,et al.  A simple and robust Simulated Annealing algorithm for scheduling workover rigs on onshore oil fields , 2011, Comput. Ind. Eng..

[32]  H. F. Spoerker,et al.  Performance Drilling Onshore Iran - Introducing New Concepts to a Mature Area , 2005 .

[33]  Munir Ahmad,et al.  Evaluation of operational performance of workover rigs activities in oilfields , 2013 .

[34]  J. J. Brennan,et al.  Scheduling a Backlog of Oilwell Workovers , 1977 .

[35]  Michel Gendreau,et al.  Heuristics for multi-attribute vehicle routing problems: A survey and synthesis , 2013, Eur. J. Oper. Res..

[36]  George V. Chilingarian,et al.  Surface operations in petroleum production , 1991 .

[37]  J. E. Cochrane Rig Performance Monitoring and Measurement: Can It Again Be Useful? , 1989 .

[38]  L. Bodin ROUTING AND SCHEDULING OF VEHICLES AND CREWS–THE STATE OF THE ART , 1983 .

[39]  Laura Bahiense,et al.  Planning and scheduling a fleet of rigs using simulation-optimization , 2012, Comput. Ind. Eng..

[40]  Roberto Roberti,et al.  An exact solution framework for a broad class of vehicle routing problems , 2010, Comput. Manag. Sci..

[41]  Glaydston Mattos Ribeiro,et al.  A branch-price-and-cut algorithm for the workover rig routing problem , 2011, Comput. Oper. Res..