This paper presents the novel approach of the Norm-dist Monte-Carlo fuzzy analytic hierarchy process (NMCFAHP) to incorporate probabilistic and epistemic uncertainty due to human's judgment vagueness in multicriteria decision analysis. Normal distribution is applied as the most appropriate distribution model to approximate the probability distribution function of the criteria and alternatives within Monte-Carlo simulation. To test the applicability of the proposed NMCFAHP, the case study of non-destructive test (NDT) technology selection is performed in the Petroleum Company in Borneo, Indonesia. When compared with the conventional triangular fuzzy-AHP, the proposed NMCFAHP method reduces the standard error of mean values by 90.4-99.8% at the final evaluation scores. This means that the proposed NMCFAHP significantly involves fewer errors when dealing with fuzzy uncertainty and stochastic randomness. The proposed NMCFAHP delivers reliable performance to overcome probabilistic uncertainty and epistemic vagueness in the group decision making process.

This paper presents a techno-economic analysis for oil and gas production sharing contract (PSC) which is subjected to uncertainty from fluctuation of natural gas prices and production reservoir capacity. Net present value (NPV) is calculated based on a 10-year contract duration considering capital-operational expenditure, production sharing contract bidding value, and salvage value. The Monte Carlo method is embedded in the NPV analysis to quantify the probability of the production sharing contract's profit and loss. The result of this probability is utilized as input for determining the decision to acquire the PSC. This paper confirms that investment in the oil and gas industry is high risk. This type of investment is only suitable for companies with strong equity or financial power.

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Learning from events is a crucial measure to reduce the number of catastrophic incidents on board the oil and gas industry. It is critical to have a better understanding of the root causes behind the incidents. A better understanding of the incident causal factors can be accomplished by investigating thoroughly previous incident reports and determine the most notable factors in contributing to the incidents. This paper mainly elaborates and analyzes incident reports produced by the International Oil and Gas Producer Association’s (IOGP’s) reports from 2010 until 2018 located in the worldwide operation. The most contributing factors lead to the oil and gas incidents are examined in this paper utilizing Gaussian fuzzy analytic hierarchy process, an improved methodology of fuzzy analytic hierarchy process (FAHP) by embedding Gaussian fuzzy number within the evaluation process. Gaussian fuzzy number is obtained by random simulation corresponding to the Gaussian probability density function. The result of this paper reveals the more accurate and realistic representation of incident causal factors determination. This point of view helps in better explaining oil and gas incidents causal factors, where precaution measures should be directed and efficiently managed.