The Application of Success Probabilities, Success Driven Project Management/SDPM, and Some Critical Chain Concepts to the Oil & Gas Industry in Brazil

This paper presents the practical application of range estimates for activity durations and/or resource requirements to produce success probability trends, which is a basic advance included in Success Driven Project Management/SPDM. SPDM has been used in Russian speaking countries for the past 15 years and includes concepts equal to certain critical chain features; all concepts underlying SPDM will be described briefly. Recent experience in applying SPDM to projects in the oil and gas industry in Brazil will be described, and open discussion of all aspects of SPDM will be encouraged. THE NEED FOR MANAGING UNCERTAINTIES, RISKS, AND RESOURCE RESTRICTIONS IN PROJECTS Most complex projects and programs today involve many uncertainties and risks, plus many restrictions on availability of money, skilled people, materials, equipment, and other key resources; projects in the oil and gas sector in Brazil are no exception to this statement. The need is to be able to plan, schedule, monitor and control these projects while reflecting these uncertainties, risks, and restrictions in a practical and realistic manner in the project plans, schedules, execution methods, and progress reports. The concepts and approaches described here have been developed and applied over the past 15 years, primarily in Russia and former Soviet Union countries, to satisfy this need. They are now being applied successfully in western European countries, Brazil, and elsewhere. The term Success Driven Project Management/SDPM (Liberzon and Archibald 2003), (Liberzon 1996, 2000, 2001) refers to an integrated approach that incorporates 1) range estimates of work scope, activity duration, resource requirements; 2) calculation of activity durations based on work scope and resource productivity information; 3) calculation of the Resource Critical Path/RCP by taking into consideration all schedule constraints including resource and financing constraints in both the forward and backward passes of network analysis; and 4) predictions of the probabilities of achieving user defined target schedules and costs. We will first describe the underlying principles and methods upon which SDPM has been constructed and then provide a short history related to its development and emphasis on resource planning and schedule optimization. SDPM will be compared briefly with the basic concepts of the Critical Chain method, and present a comparison of the use of Monte Carlo methods with using a range of three estimates to predict success probabilities. Finally we will describe our experience in the field in applied SDPM to projects in the oil and gas industry in Brazil. A few basic conclusions from this experience will then be presented.