Option #2: Gas injection project in Oman
Running head: THE GAS INJECTION PROJECT CASE 1
THE GAS INJECTION PROJECT CASE 6
Option #2: The Gas Injection Project Case
Woodrow Rowell
Colorado State University-Global Campus
3/27/20
The Gas Injection Project Case
Gas Injection projects have been associated with managerial difficulties as a result of the involved complexities and many processes, which lead to project failure or inability to complete the projects within the scheduled time frame. Projects consist of numerous activities, and each has to be completed at the right time, for overall success to be realized. The paper will address the Gas Injection Project that took place in an Oman based, Oil & Gas Company. Key factors to be addressed include; the risk modification methods prescribed by the PMBOK Guide, 6th edition, a description of the Monte Carlo method used in the case study and associated benefits in the perspectives of quantifying and analyzing the risks, and likelihood or possibility of them completing the project in defined time frames.
Risk Quantification Methods Based- PMBOK Guide,6th Edition
According to the PMBOK Guide,6th edition, the following methods can be used in quantifying risks or contingencies within projects. First, one can use heuristic methods, which mainly rely on the experience or advice from an expert. Models considered under this method include; Percentage of Total Values, Predetermined Guidelines, Case-based Reasoning Model and the Controlled Interval and Memory (Meyer, 2015).
Secondly, there are the Expected Value Methods. Under this method, the risk value is obtained by the risk exposure to the assigned probabilities. Methods used under this category include; Method of Moments and the Expected value of individual risks. Third, we have Probability Distribution Methods. Risks are calculated based on statistical distributions, which have been predefined. Applicable methods under this category include; Monte Carlo Simulation and the Range Estimating (Meyer, 2015).
Mathematical modelling is the fourth possible method used. Hypothetical mathematical models, utilizing either linear or non-linear equations are used in the process of determining the values of the risks. Methods under this category include; Fuzzy Sets and the Artificial Neural Networks. Fifth, there is Interdependency Models. These types of modes are known to determine the risk values using the logical and resource-controlled dependencies. Examples include; Influence Diagrams, Analytical Hierarchy Process and Influence Diagrams (Meyer, 2015).
The last listed method is known as Benchmarking, also referred to as Empirical methods. In this case, historical or past projects are used in determining the factors leading to risks. Shared risks are determined by comparing the projects’ specific characteristics. Commonly used methods here include; Factor Rating and the Regression (Meyer, 2015).
Monte Carlo Simulation
The Monte Carlo simulation falls under the Probability Distribution Methods, used in risk quantification. The risk values are determined through probability distribution, where a range of values, from factors exhibiting uncertainties, are substituted. Using the probability functions, the risk values are calculated repeatedly, apply different sets of random numbers of values (recalculations). Benefits associated with the monte Carlo simulation in the case study are that;
· The possible events and probability of occurring are specified,
· Data is presented graphically,
· Supports sensitivity analysis and identifies variables with high impacts,
· Clearly projects the probability of project success and
· Facilitates modelling of dependency occurring between the user input variables (Wilson, 2014).
Based on its benefits and unique features, the method was found fit in the case and facilitated the quantification of the costs and schedule of the project.
On-Time Project Completion
The project could be completed on time only if the costs did not exceed the planned ones, and all activities completed on time as scheduled. The Monte Carlo simulation ran several calculations and assigned various risk values, which were then averaged to determine the possible levels of the risks. The method was coupled up with sensitivity analysis, to offer the final results used by the project team, in quantifying the risks.
Costs that could affect the project completion were found to be; late order placement, rework and schedule delay and AFC package delay in probabilities of 48.3%, 28.2% and 27.9% respectfully. The project duration was influenced by risks like reworking and delay rescheduling at 39.9%, package delay (24.3%), material delay (10.6%), package vendors complexity (10.1%), late commissioning and installation (7.9%), and failed acceptance tests (7.1%).
Based on the costs and schedule delays, the chances are that the project could not be completed on time. Averaging the cost increase of the project to 8%, the probability of having the project complete within the specified time will be 72%. In that, the project would require two years more than it was initially anticipated. The project managers wanted the project to be ready in the year 2013, but the identified risks pushed it to 2015. Failing to identify the risks and mitigate or address them on time suggested project management failure (Khadem, Piya, S., & Shamsuzzoha, 2018).
Project managers are required to carry out effective risk assessments and quantify the risks using the most appropriate risk quantification methods, considering that we have got a wide range of the same. The PMBOK Guide,6th edition has provided a wide range of possible risk quantification methods. Monte Carlo simulation proved to be of great importance to the project, based on its benefits such as; specifying events to occur and the respective probability of occurrence, graphical presentation of data, compatibility with sensitivity analysis, predicting the likelihood of project success, and determining dependency input variables. It the responsibility of project managers to identify, list, and quantify all risks relating to a project, especially those affecting costs and scheduling period, and undertake effective remedial actions to address the issue. Failure to do so, chances of completing the project on the scheduled time, with the planned resources, will decline (Wilson, 2014).
References
Khadem, M. M. R. K., Piya, S., & Shamsuzzoha, A. (2018). Quantitative risk management in gas injection project: a case study from Oman oil and gas industry. Journal of Industrial Engineering International, 14(3), 637-654. Retrieved from https://doi.org/10.1007/s40092-017-0237-3
Meyer, W. G. (2015). Quantifying risk: measuring the invisible. Paper presented at PMI® Global Congress 2015—EMEA, London, England. Newtown Square, PA: Project Management Institute. Retrieved from; https://www.pmi.org/learning/library/quantitative-risk-assessment-methods-9929
Wilson, R. (2014). Mastering Risk and Procurement in Project Management: A Guide to Planning, Controlling, and Resolving Unexpected Problems. Pearson Education. Retrieved from https://csuglobal.instructure.com/courses/18935/external_tools/218