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Wood D.A.,DWA Energy Ltd | Towler B.F.,University of Wyoming
Journal of Natural Gas Science and Engineering | Year: 2012

Gas-to-liquids (GTL) has emerged as a commercially-viable industry over the past thirty years offering market diversification to remote natural gas resource holders. Several technologies are now available through a series of patented processes to provide liquid products that can be more easily transported than natural gas, and directed into high value transportation fuel and other petroleum product and petrochemical markets. Recent low natural gas prices prevailing in North America are stimulating interest in GTL as a means to better monetise isolated shale gas resources. This article reviews the various GTL technologies, the commercial plants in operation, development and planning, and the range of market opportunities for GTL products. The Fischer-Tropsch (F-T) technologies dominate both large-scale and small-scale projects targeting middle distillate liquid transportation fuel markets. The large technology providers have followed strategies to scale-up plants over the past decade to provide commercial economies of scale, which to date have proved to be more costly than originally forecast. On the other hand, some small-scale technology providers are now targeting GTL at efforts to eliminate associated gas flaring in remote producing oil fields. Also, potential exists on various scales for GTL to supply liquid fuels in land-locked gas-rich regions. Technology routes from natural gas to gasoline via olefins are more complex and have so far proved difficult and costly to scale-up commercially. Producing dimethyl ether (DME) from coal and gas are growing markets in Asia, particularly China, Korea and Japan as LPG substitutes, and plans to scale-up one-step process technologies avoiding methanol production could see an expansion of DME supply chains. The GTL industry faces a number of challenges and risks, including: high capital costs; efficiency and reliability of complex process sequences; volatile natural gas, crude oil and petroleum product markets; integration of upstream and downstream projects; access to technology. This review article considers the GTL industry in the context of available opportunities and the challenges faced by project developers. © 2012 Elsevier B.V. Source


Generic supplier selection from the perspective of multi-criteria decision making (MCDM) methodologies including crisp, fuzzy and intuitionistic fuzzy analysis of decision matrices has received much attention, but less so specifically for the gas and oil industry, and in terms of comparing performance of a number of available techniques. A set of 30 criteria are identified for assessing supplier selection for facilities and field development projects across the petroleum industry. Bidders are assessed in terms of these criteria, with varying degrees of uncertainty and subjectivity, using linguistic scoring terms that are then transformed into crisp and fuzzy numerical sets. Eight MCDM scoring methods are described mathematically and applied to a facilities-procurement scenario in order to analyze a linguistic-assessment matrix for five alternative bidders using the 30 recommended criteria. These scoring methods are: linear; non-linear; the order of preference by similarity to an ideal solution (TOPSIS); Fuzzy TOPSIS (with and without entropy weighting); and, intuitionistic fuzzy TOPSIS (IFT) with three alternative methods for calculating entropy weighting (We). Performance of the eight methods is assessed by comparing calculated rankings for the five bidders in relation to the defined supplier selection scenario for a base case and ten sensitivity cases. The results of the analysis suggest that entropy weightings applied to fuzzy sets provide more consistent bidder selection, and led to the proposal of a new intuitionistic-fuzzy-TOPSIS-method-with-flexible-entropy-weighting method that enables the entropy weighting scale to be tuned to suit the circumstances of specific scenarios using equation 30 to flexibly normalize the entropy weighting scale. © 2015 Elsevier B.V. Source


Asset portfolio modelling and optimization are critical activities for upstream (exploration and production) gas and oil companies in order for decision makers to establish the combined value of their assets and to select assets for further development, divestment and/or acquisition. However, it is an activity that is typically not conducted in a standardized and systematic way, with many companies relying on simple deterministic discounted cash flow asset-value-roll-up analysis, but missing vital insight to the subtle, but significant characteristics of their portfolios. A more systematic, multi-stage stochastic methodology is proposed to reveal detailed characterization of gas and oil asset portfolios in terms of value, risk and timing. The non-linear nature of risk is taken into account in an approach to risk analysis that begins at the asset level and progresses through to the pre-corporate rolled-up asset portfolio to post-tax portfolio factoring in the corporate financial dimension. The proposed methodology emphasizes the importance of considering financial and non-financial metrics (i.e. production, reserves and timing) over each year of a planning horizon. In addition, those same metrics summed over all the years of a planning horizon, expressed in terms of risked value and downside risk of the portfolio failing to achieve certain strategic targets identifies feasible envelopes for possible asset combinations. The downside risk measures apply important modifications to standard risk-variance analysis, introducing flexibility into the approach to suit diverse strategic objectives of potential portfolio holders. Further analysis of those risk versus risked value feasible envelopes reveals the efficient frontiers representing the asset combinations that achieve the highest value for specific levels of downside risk. Characterizing a portfolio of gas and oil assets with such a methodology helps to frame multi-objective optimization algorithms tailored to suit the unique characteristics of each asset portfolio. Excel spreadsheets driven by visual basic for applications (VBA) macros offer the advantages of flexibility, transparency and customization to characterize asset portfolios with the methodology proposed. A small portfolio involving eleven exploration, appraisal, development and production gas and oil assets (Portfolio X) is presented to illustrate the benefits of the proposed approach to gas and oil asset portfolio characterization. The diversity in character of conventional and unconventional upstream gas assets makes a portfolio approach to their understanding extremely worthwhile. © 2016 Elsevier B.V. Source


Nwaoha C.,Chulalongkorn University | Wood D.A.,DWA Energy Ltd
Journal of Natural Gas Science and Engineering | Year: 2014

Natural gas is destined to become a larger part of Nigerian energy mix as the country seeks to guarantee the sustainability of its energy supply and benefit from greater energy efficiency and reduce energy-related costs. However, this continues to be a relatively slow process with large quantities of associated gas still being flared, as it has been since the 1950s. Natural gas' availability, versatility, accessibility, and more importantly its clean-burning characteristics when compared to other fossil fuels, is a substantial driver for its further utilisation in country. Nigeria is endowed with some 182 trillion cubic feet (tcf) of proven gas reserves, and that is mostly located in the Niger Delta. Nigeria's government is keen to develop local utilization of gas employing a range of available technologies. These technologies include gas to power using gas fed by transmission and distribution pipeline networks to supply combined cycle gas turbines (CCGT), compressed natural gas (CNG), gas to liquids (GTL) to supply transportation fuels, gas to fertilizer (GTF) and petrochemicals to support domestic industries, and export options involving liquefied natural gas (LNG), the West African Gas Pipeline (WAGP), and, in the future, other potentially large-scale export routes (e.g. to Europe through a Trans Saharan Gas Pipeline (TSGP). This paper reviews these gas utilization options, export potential, and government's policies that are stimulating gas investments in Nigeria. © 2014 Elsevier B.V. All rights reserved. Source


Hybrid cuckoo search optimization (hCSO) algorithms are described and developed in comparison with the standard cuckoo search algorithm (Yang & Deb, 2009). The hCSO involves potentially eight metaheuristic components that complement each other in their search contributions and operate as a "tool box" of modules such that six of them can be easily switched on or off. The metaheuristics are coordinated to progress through five distinct steps that constitute hCSO: (1) initialization; (2) exchange (3) modification; (4) replacement; (5) metaheuristic labelling, ranking and carry forward. Key amendments introduced to hCSO involve replacing Levy flight solution space sampling with stochastic random sampling of simpler fat-tailed distributions with dynamic sampling windows that move through the distribution as iterations of the algorithm advance. The randomly-extracted samples are further adjusted with scaled-chaotic sequences to provide more flexibility and control over the granularity of the sampling of the solution space. In addition other metaheuristics are added to the standard CSO that improve the balance of the algorithm between local and global searching. Three of the metaheuristics include chaotic adjustments to dynamic stochastic sampling of search metrics distributions (fat-tailed and other, highly non-linear, stepped ranges). Several configurations of the metaheuristics available in the hCSO algorithm are applied to a well-reported complex wellbore trajectory optimization problem. Their performance is compared with the aid of metaheuristic profiling and statistical analysis of the minimum total measured depth (TMD) found in multiple sequential runs. Several configurations of the hCSO are shown to work efficiently in locating the global optimum, avoiding being trapped by the many local optima within the solution space. They do so requiring less computational time than six other evolutionary algorithms evaluated with the same number of iterations and population of generated solutions and similarly developed in Excel VBA code to facilitate metaheuristic profiling. © 2016 Elsevier B.V. Source

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