Sam Wamuziri

The Fuzzy Analytic Hierarchy Process in the Investment Appraisal of Drilling Methods

Abstract The robust appraisal of exploration drilling concepts is essential for establishing the economic viability of a prospective recovery field. This study evaluates the different concept selection methods that were considered for drilling operations at the Trym field in Norway. The construction of drilling rigs is a capital-intensive process, and it involves high levels of economic risk. These risks can be broadly categorised as aleatoric (i.e. those related to chance) and epistemic (i.e. those related to knowledge). Evaluating risks in the investment appraisal process tends to be a complicated process. Project risks are evaluated using Monte Carlo simulation (MCS) and are based on the fuzzy analytic hierarchy process (AHP). MCS provides a useful means of evaluating variabilities (i.e. aleatoric risks) in oil drilling operations. However, many of the economic risks in oil drilling processes are unanticipated, and, in some cases, are not readily expressible in quantitative values. The fuzzy AHP is therefore used to appraise the qualitatively defined indirect revenues comprising risks that affect future flexibilities, schedule certainty and health and safety performance. Both the Monte Carlo technique and the fuzzy AHP technique found that a cumulative revenue variation of up to 30% is possible in any of the considered drilling options. The fuzzy AHP technique estimates that the chances of profitability being less than NOK 1 billion over a five-year period is 0.5%, while the Monte Carlo technique estimates suggest a more conservative proportion of 10%. Overall, the fuzzy AHP technique is easy to use and flexible, and it demonstrates increased robustness and improved predictability.

Cost analysis error? Exploring issues relating to whole-life cost estimation in sustainable housing

Purpose – The main purpose of the study is to promote consideration of the issues and approaches available for costing sustainable buildings with a view to minimising cost overruns, occasioned by conservative whole-life cost estimates. The paper primarily looks at the impact of adopting continuity in whole-life cost models for zero carbon houses. Design/methodology/approach – The study embraces a mathematically based risk procedure based on the binomial theorem for analysing the cost implication of the Lighthouse zero-carbon house project. A practical application of the continuous whole-life cost model is developed and results are compared with existing whole-life cost techniques using finite element methods and Monte Carlo analysis. Findings – With standard whole-life costing, discounted present-value analysis tends to underestimate the cost of a project. Adopting continuity in whole-life cost models presents a clearer picture and profile of the economic realities and decision-choices confronting clients...

Mapping Relational Efficiency in Neuro-Fuzzy Hybrid Cost Models

Significant improvements are achievable in the accuracy of cost estimates if cost models adequately incorporate issues of flexibility and uncertainty. This study evaluates the relational efficiencies of the fuzzy composition operators “ the max-min and max-product, in establishing the final cost of water infrastructure projects. Cost and project data was collected on 1600 water infrastructure projects completed in Scotland between 2000 and 2011. Neural network is first used to develop relative weightings of relevant cost predictors. These were then standardized into fuzzy sets to establish a consistent effect of each variable on the overall target cost. The strength and degree of relationship of the normalized cost predictor weightings and the fuzzified project attributes were combined using the max-min and max-product composition operators to obtain project cost predictions. The predictions from the two composition operators are compared with the actual cost figures. Results show comparable performance in the efficiency of the composition operators. Based on statistical correlations, the max-product composition operator achieved on average a deviation of 1.71% while the max-min composition had an average deviation of 1.86%. Improvements in the relational efficiency of neuro-fuzzy hybrid cost models could assist in developing a robust framework for realistic cost targets on construction projects.