TITLE

Managing Marginal Oilfield Risks with Fuzzy Logic and Monte Carlo Simulation for Economics Optimization

AUTHOR(S)
Alaneme, Charles Ezemonye; Igboanugo, Anthony Clement
PUB. DATE
August 2012
SOURCE
Journal of Emerging Trends in Engineering & Applied Sciences;Aug2012, Vol. 3 Issue 4, p660
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Investors in marginal oilfield exploitation need cost effective risk analysis process to enhance the quality of the decision making. The risks associated with marginal oilfields represents an economic opportunity and a clear understanding of the risks helps to correlate and stratify the expected net returns through efficient planning for and allocation of right resources as well as selecting an optimum alternative. This paper provides illumination and deep insight about the application of Fuzzy Logic technique in analysing risk and the use of Monte Carlo Simulation in optimizing investment analysis. The paper discusses their wider implications using a case study, and gives justification for their analytical importance to marginal oilfields exploitation. An iterative Delphi technique was employed to define six risk attributes in a given Isiekenesi Marginal Oilfield from where fifty-three risk variables were identified for evaluation. A questionnaire designed with Rensis Likerts 5-point attitudinal scale was subsequently administered to 42 respondents to provide linguistic expression of the level of risk probabilities and consequences. Thereafter, a computed Kendall Coefficient of Concordance of W= 0.75, and chi-squared value (χ² ) of 546 which is greater than 27.69 recorded in the statistical table, implying an index of agreement among the judges in ranking the variables, hence, a null hypothesis of disconcordance among the judges was rejected at a p-value of 0.01. The Fuzzy logic analysis was able to establish that an investment risk level of 0.71 on a scale of 0 to 1 is associated with Isiekenesi Field in the Nigeria Niger Delta, with optimistic, probable, and pessimistic fuzzy value of 0.35, 0.75, and 0.96. The optimization of the net present value with Monte Carlo simulation confirmed this approach as a risk management alternative to address limitations of conventional methods. The importance of the study is straddled with the simple extrapolation that managing the inherent risks and uncertainties will lead to an optimized exploitation of the marginal oilfields to increase oil reserves and subsequently increase the economic revenue of the country.
ACCESSION #
80534643

 

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