Developing a Stochastic Model to Predict Cost Overrun due to the Causes of Projects Delay in Oil Fields Construction Industry

Abstract

The criticality of managing projects delay in oil fields construction projects has been exacerbated by the general complexities associated with the sources of projects delay and how varied the impact may be to various stakeholders such as client/owner, contractor, design/consultant and miscellaneous/external groups within the industry. While most of the previous research investigated the causes of project delay in general construction, very little research exists specifically on oil fields construction projects in terms of budget overrun. The main aim of the research is to establish a model that could be used to map the causes of projects delay to the cost overrun in oil fields construction projects and to investigate the variables that contribute to budget/cost overrun. In addition, the author used regression analysis and Monte Carlo simulation to develop a stochastic model to map the causes of projects delay to cost overrun. The research results revealed that poor contractor works was found to have the most significant positive impact in the delay experienced by the projects considered in this work. However, most of the causes of cost overrun were found to follow the Log logistic distribution – a family of distribution associated with projects where the risk increases at the initial phase of the project and decreases later on. Therefore, to minimize cost overrun, the study suggest that those involved in a project’s value chain must ensure a smooth execution of project in its early stage so as to limit the high risk associated with the project during in its initial phase. The main contribution of this work, is the research framework which can be used by businesses in oil fields construction industry to model their own factors and help in their decision-making, as opposed to using anecdotal evidence. Finally, the author recommended some areas of the current study that can be developed such as mapping the causes of projects delay to other impacts, such as time overrun success criteria and to increase the number of projects delay in the regression model.