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Lincoln, United Kingdom

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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.


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.


Bahadori A.,Southern Cross University of Australia | Wood D.A.,DWA Energy Ltd
Journal of Natural Gas Science and Engineering | Year: 2013

Australia's gas resources are large enough to support projected domestic and export market growth beyond 2030 and are expected to grow further. Most (around 92 per cent) of Australia's conventional gas resources are located in the Carnarvon, Browse and Bonaparte basins off the north-west coast.Large coal seam gas (CSG) resources exist in the coal basins of Queensland and New South Wales. Tight gas accumulations are located in onshore Western Australia and South Australia, while potential shale gas resources are located in the Northern Territory, Western Australia and South Australia. © 2012 Elsevier B.V.


Optimizing the routing of resources to multiple remote sites is a complex issue confronting many sectors of the gas an oil industries with significant cost implications. When significant numbers of sites are involved the optimum solutions become difficult to find and require complex algorithms to do so. Memetic algorithms (MA), combining multiple metaheuristics and heuristics that can be easily activated or deactivated offer a potentially effective and flexible approach to complex routing optimization problems. By combining MAs with the recently proposed tool of metaheuristic profiling (MHP) it is possible to establish and monitor the contributions of the component metaheuristics and heuristics in finding the lowest-distance tour solutions for routing problems. MHP also facilitates the identification of synergies between specific metaheuristics, potential conflicts or duplication among others, and computational time consumption issues with certain combinations. Applying MHP as a monitoring tool during the development of MAs helps to develop balanced algorithms combining multiple metaheuristics focused on specific tasks, such as exploring the global solution space and/or exploiting the space locally around specific solutions. Memetic algorithms make it possible to consider the classic evolutionary algorithms and other well-known heuristics each as components in a “toolbox” of metaheuristics/heuristics available to be combined and configured to form flexible, fit-for-purpose optimization tools. A routing memetic algorithm is described in detail and tested using well-studied examples of the travelling salesman problem (TSP). MHP is applied, using the Excel-VBA platform, to reveal the relative contribution of the nine metaheuristics involved in the routing MA developed here, which incorporates some of the metaheuristics derived from bat-flight principles. The study identifies how these metaheuristics function together with integrational synergies. The MHP information is displayed in graphic and tabular form, alongside the optimum values obtained from multiple executions of the algorithm to illustrate the guidance and level of insight that can be provided by the MHP technique. Memetic algorithms typically involve multiple control variables that can be (and often need to be) tuned to improve their efficiency in finding the optima of specific problems. This makes them flexible, but potentially time consuming to setup and operate. The successful application of the MA to the TSP routing problem suggests scope for its development to address more complex routing problems. © 2016 Elsevier B.V.


A hybrid bat-flight optimization (BFO) algorithm is described and developed in comparison with the original bat-inspired algorithm (Yang, 2010). The changes made remove the need to evaluate and store velocities from previous iterations to calculate new solutions, thereby reducing the computational requirements without negatively impacting the performance of the algorithm as an efficient optimizer. The hybrid BFO consists of six metaheuristic components that complement each other in their contributions to global and local search of solution spaces. The hybrid BFO algorithm is applied to a well-reported complex wellbore trajectory optimization problem previously used to evaluate the performance of evolutionary optimization algorithms. The hybrid BFO is shown to work effectively and efficiently in finding the optimum solution space, requiring significantly less iterations to do so than a hybrid genetic algorithm applied to the same problem, both developed in VBA code.The performance of the hybrid BFO algorithm is further evaluated by a novel technique of metaheuristic profiling introduced in this work. By recording the origin of each solution generated in each iteration of the algorithm, in terms of which metaheuristic component is responsible for producing it, a profile of the origin of the ten highest-ranking solutions in each iteration is constructed. This profile reveals that the metaheuristic components driven by the frequency, loudness and pulse rate (i.e., the bat-echolocation-inspired metrics used to drive the algorithm) contribute to the solutions derived in complementary, but varying ways as the iterations of the algorithm progress. Metaheuristic profiling is considered to be a promising technique for design, performance comparison, improvement and customization of evolutionary algorithms. © 2016 Elsevier B.V.


Wood D.A.,DWA Energy Ltd
Journal of Natural Gas Science and Engineering | Year: 2012

The evolution of global and regional LNG trade over the past twenty years has been a story of rapid growth, diversification and increased flexibility in LNG cargo movements. Asia continues to dominate global LNG trade, but the European LNG market has evolved significantly in the past decade and seems destined for sustained growth and diversification over the next decade or so. Despite the LNG import market in North America being overwhelmed by unconventional gas developments in the past few years, future sustained growth of LNG demand in Asia and Europe are underpinned by firm new project commitments. A number of North American LNG export projects are progressing with a view to supplying this growing market demand in Europe and Asia. New gas discoveries in deepwater offshore East Africa and Eastern Mediterranean are also likely to compete for LNG market share in growing European and Asian gas markets Country and regional statistics presented illustrate how significantly the global LNG industry has changed in the past decade. These statistics reveal the complexity of commercial, political and technical drivers at play, particularly in the case of Europe, and how these drivers are conspiring to boost future demand for LNG. © 2012 Elsevier B.V.


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.


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.


Metaheuristic profiling is proposed as an effective technique with which to evaluate the relative contributions of the metaheuristic components of hybrid evolutionary optimization algorithms in progressing searches of feasible solution spaces to locate global optimum values of their objective functions. Although many useful evolutionary algorithms have been successfully proposed and tested to solve a wide range of complex mathematical optimization problems, when applied to real-world optimization tasks their performance can often be improved by hybridization with other metaheuristics. A case is made here that in developing optimization algorithms for specific practical applications it is better to treat the available evolutionary algorithms as part of a "toolbox" of metaheuristic components that can be configured in various hybridized combinations. The technique of metaheuristic profiling is evaluated as means of identifying the relative contributions of individual metaheuristic components in contributing to the discovery of optimum solutions over multiple iterations of hybrid algorithms. The metaheuristic profiling technique of a toolbox of metaheuristic components is evaluated in terms of applying seven hybrid evolutionary algorithms to optimize a previously studied complex well-bore trajectory optimization problem. The seven hybrid evolutionary algorithms developed with multiple metaheuristics are built upon standard: genetic; particle swarm; bee colony; ant colony; harmony search, cuckoo search and bat flight algorithms. Pseudocode for each of the hybrid algorithms studied are provided in an appendix. These codes identify the metaheuristics included and the sequence in which they are applied in the hybrid algorithms. All seven hybrid algorithms are coded in VBA based in Microsoft Excel with the assistance of the metaheuristic profiling technique, to provide reliably reproducible solutions to well-bore trajectory design optimization. Analysis of metaheuristic performance also confirms the benefits of fat-tailed distributions, sampled chaotically, in a novel way, to drive certain metaheuristics. © 2016 Elsevier B.V.


Wood D.A.,DWA Energy Ltd
Journal of Natural Gas Science and Engineering | Year: 2016

Applying optimization algorithm to identify high-performing portfolios is an essential component of gas and oil portfolio analysis and decision making. As oil and gas companies wish to strategically optimize their performance with respect of multiple valuation and non-financial key performance indicators (KPIs) optimization algorithms that facilitate multi-objective optimization are desirable. The benefits of applying a suite of optimizers to mean versus semi-standard deviation stochastic multi-year cash flow and income models are explored with the aid of the hypothetical Portfolio X consisting of eleven upstream assets (with a summary dataset of mean values of KPI simulation distributions plus some general simulation input assumptions applied to the portfolio included as Appendix A). The methods and results of three distinct optimizer algorithms are compared, i.e., rank and cut, linear (Simplex) and evolutionary (genetic) algorithms in terms of a multi-KPI function test score, which is also used as the objective function for the genetic algorithm. The calculations described using the Portfolio X dataset are produced using Excel workbooks driven by VBA macros, and can be scaled up for use with larger asset sets. The results of variously constrained optimization runs using the three optimizers are further evaluated in terms of feasible-envelope and efficient-frontier concepts for value metrics, and for chances of achieving specified KPI targets on an annual basis. A risked portfolio value metric including a risk aversion factor is also derived in order to capture portfolio value and risk in a single metric. An overall portfolio analysis and valuation framework into which a suit of optimizers can meaningfully be deployed is described. © 2016 Elsevier B.V..

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