The non-linear nature of historical oil and gas spot prices makes prediction very difficult. An evaluation of historical time series spot prices data with Fourier power spectrum analysis and autocorrelation function shows the likelihood of chaotic behavior. Characterization and identification of the data with the Lyapunov exponent suggest the existence of chaos. A chaos theory analysis is therefore used for the space phase reconstruction of the strange attractors in the oil and gas markets. The optimal embedding dimension, time delay and predictability are obtained with a spatial minimization of the root mean square error. The embedding dimension and time delay are then used as inputs in a fuzzy neural network model. The time series spot price data is embedded and divided into training and testing sets. A fuzzy neural network model is constructed using the training set and checked with the testing set. A good match is obtained between the predicted and historical time series data. The paper concludes that the chaotic behavior of the historical oil and gas spot prices prevents the long-term forecast of future spot prices and limits the short-term forecast to the embedding prediction horizon. Introduction A good estimate of future oil and gas prices is essential to the economic evaluation of oil and gas projects. It is also necessary for planning purposes in public institutions, national budgets of producing nations, financial institutions and future markets. In the past, linear models were used to forecast oil and gas prices. These models assumed monotonic increases based on historical trends. A sensitivity analysis was done with an optimistic case, most likely case (historical averages) and a pessimistic case1. Some of these models were based on price elasticity analysis. For example, Roberts2 argued that price elasticity and economic growth affected the direction of oil prices. He therefore developed a model with price elasticity and energy intensity as input variables. Inikori et al.3 also used price elasticity and supply demand balances as input variables. A linear regression model of lagged world/US drilling rig count was used for the forecasting. However, historical oil and gas prices are not constant nor do they increase monotonically. Price data show fluctuations and seems to be influenced by political events, supply, demand, technological changes, environmental concerns and inventories. Caldwell and Heather4 concluded that crude oil price has a stochastic nature and behaved as if it was normally distributed most of the time. However, it sometimes behaved in a nonlinear manner. Skov5 thought that large fluctuations in oil prices could occur because prices were not only determined by supply and demand but also by technology, culture, resource base, consumption patterns and population growth. Linear models were therefore most likely to give incorrect crude oil price forecasts. Dougherty6 advises that we should be concerned about the danger of linearity. He argued that the primary determinant of oil price was oil supply (the amount of oil offered for sale). He also focused on price elasticity because small changes in supply generated large changes in prices. He advocated a compressive analysis of production, reserves and cash flow in order to understand the impact of a price change. Linear models are based on the assumption that in a system, similar conditions generate similar responses when subjected to similar stimuli. Forecasting uses observed behavior of the system to predict future outcomes given similar conditions. Unfortunately, most linear forecast of commodity prices do not reflect their historical behavior. This is probably because economic systems have many autonomous variables including human agents who sometimes behave differently under similar stimuli. There is the probability of nonlinearity in the historical price data of many commodities.

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In the past economic models captured economic externalities such as sustainable economic development as additional investments that increased project operational costs. There is a need to internalize investment in sustainable economic development in project profitability models. This paper develops a model that captures economic returns from oil and gas investments from the different perspectives of the four major stakeholders in oil and gas operations. These four major stakeholders are the oil companies, the shareholders, the society and the host communities. The cost of sustainable economic development has different impacts on the financial conditions of each of the stakeholders. Each stakeholder therefore has its unique economic perspective defined by its minimum rate of return and its view of project profitability. The perspective of the oil companies use the cost of borrowing capital as its absolute minimum rate of return while that of the shareholder uses the bank saving rate of return. The absolute minimum rate of return for the society captures the replacement of oil and gas as wasting assets, while that for the host communities encompass the replacement of the income generating capacity lost due to environmental pollution as well as the cost of maintaining the economic regenerative capacity of the environment. The project must be profitable from the unique point of view of each of the major stakeholders for sustainable economic development to occur. Project profitability from the perspective of the host community is the sustainable economic development test. The paper concludes that oil and gas ventures can be screened with the four perspectives profitability model to ensure that they will enhance sustainable economic development understood from the perspective of each of the major stakeholders. Screened investment opportunities that are profitable from the four perspectives can then be ranked on the basis of profitability from the perspective of the oil companies. Introduction In the last few years, the sustainable economic development of host communities have become important to oil and gas investors. This is because of the rising level of disruption of oil and gas operations by inhabitants of host communities, especially in the developing nations. These disruptions have taken the form of demonstrations, protests, blockage of access roads, sabotage, kidnapping, hostage taking and armed struggles. For example, in Myanmar, opposition by the host communities have delayed the construction of the $1.2 billion Yadana natural gas pipeline project.1 Similarly, in Bolivia, host communities have protested the construction of a $2 billion 3200 km natural gas pipeline running from Santa Cruz, Bolivia to Sao Paulo, Brazil.2 In Nigeria, oil companies have suffered flowstations takeovers, sabotage and hostage taking by youths from host communities protesting against environmental pollution and economic underdevelopment.3 Shell Petroleum Development Corporation (SPDC) reported 108 incidents in 1993, 150 in 1997 and 325 in 1998. In March 2000, the Utorogu gas plant was occupied by armed youths from the host communities. Thirty SPDC workers and 4 soldiers were taken hostage.4 The youths demanded for sustainable economic development projects. The increasing capacity of the host communities to disrupt oil and gas operations and change the economics of oil and gas ventures forced the oil companies to evaluate its relationships with other major stakeholders.

Increased demand for natural gas in Venezuela has led to an intensified search for additional supply from mature oil fields in Eastern Venezuela. The search has involved the ranking of oil reservoirs that could be converted to gas producing reservoirs. Unreliable reservoir and historical production data introduced high uncertainties to the ranking based on gas reserves only. A fuzzy model was therefore constructed and used for ranking 45 reservoirs. Eight input variables were used to evaluate each of the reservoirs. The variables included remaining petroleum reserves, remaining gas reserves, gas cap volume, oil production per psi, gas production per psi, flow capacity, storage capacity and the distance to existing gas transmission lines. Membership functions were developed for each variable and 20 fuzzy rules were used to capture the non-linear relationship amongst the input and output variables. The output variable was represented by 5 membership functions and presented in a 0 - 1 range after defuzzification. The reservoirs were then ranked on the basis of their output range values. The results from the fuzzy model were compared to those obtained from a conventional methodology using a Decision Tree - Monte Carlo model. In this conventional model, the 8 input variables were normalized and represented as events in a decision tree. The variables were then weighted by using Monte Carlo simulation to generate the mean probability of each event occurring. A rolling netback calculation produced an output value for each reservoir. The output values were used to rank the 45 reservoirs. The paper concludes that although the two models handled the uncertainties of the impact of the 8 input variables on the output variable, the fuzzy model best capture the vagueness in the non-linear relationships between these variables. Introduction Venezuela has 147.5 TCF of gas reserves with 91% as associated gas and the remaining 9% as non-associated gas. In the Eastern part of the country, gas reserves are about 105 TCF with over 94% as associated gas. Gas demand is 1.92 BCF per day with a 7.5% annual growth rate. There is an on-going intensive search for gas to enhance the national gas supply. This search involved the conversion of mature oil reservoirs to gas producing reservoirs. Budget constraints limited the number of mature reservoirs that could be converted. Therefore, the reservoirs were ranked on the basis of Remaining Gas Reserves and the top ranked reservoirs were assumed to have the best gas opportunities. However, there was a high degree of uncertainty with the gas data. Furthermore, some of the reservoirs with large Remaining Gas Reserves had very low reservoir pressure and flow capacity. Others were located very far from any gas pipelines and surface infrastructure. There was therefore a need to use more sophisticated methods to rank the reservoirs. Conventional ranking techniques include the Net Present Value (NPV), the Internal Rate of Return (IRR), the Expected Monetary Value (EMV), Value of Information (VOI) and hydrocarbon volume such as the Remaining Gas Reserves. 1 Moore and Tucker 2 used NPV and the Economic Chance Factor to rank exploration opportunities. Zammerilli 3 used the EMV to rank horizontal well locations in tight, naturally fractured reservoirs. Variables such as reservoir pressure, net thickness, success ratio, well length, drainage area, azimuth, well radius and topography were fixed at the beginning of the project. Variables like gas price and drilling costs were allowed to vary. Kelkar 4 also used EMV to rank independent projects in the presence of uncertainties. Demirmen 5 used Decision Tree and the Value of Information (VOI) analyses for screening and ranking subsurface appraisal projects. VOI captures uncertainties arising from volumetric parameters and hydrocarbon quality parameters. Gottardi et al 6 gave a good summary of the new conventional tools for ranking R & D projects using multivariate analysis.

There are four major stakeholders in the oil and gas production process. These are the oil companies, the shareholders, the society and the host communities. The cost of protecting and reclaiming the environment has different impacts on the financial conditions of each of these stakeholders. Each stakeholder therefore has its unique economic perspective defined by its minimum rate of return and its view of project profitability. For a win-win situation for all stakeholders, the project must be profitable from the unique point of view of each of the stakeholders. In the past, emphasis was placed only on the perspectives of the oil companies and the shareholders. Economic models captured environmental costs as economic externalities that increased project operational and salvage costs, thus decreasing company profitability and shareholders’ dividend payments. But, the devastating impact of environmental pollution of the finances of the inhabitants of the host communities is now being recognized. The host communities need the regenerative capacity of the environment to maintain basic farming and fishing activities needed for their survival. The society, represented by the national government, suffer revenue loss in the face of oil pollution. This paper develops a model that captures project profitability from the four perspectives. The perspective of the oil companies uses the cost of borrowing capital as its absolute minimum rate for determining profitability while that of the shareholder uses the bank (saving) rate of return as an absolute minimum. For the society, the absolute minimum rate of return must capture the replacement of oil and gas as wasting assets. In the case of the host communities, this absolute minimum rate must encompass the replacement of income generating capacity lost due to environmental pollution as well as the cost of maintaining the economic regenerative capacity of the environment. The paper concludes that for a project to be profitable, it must meet the profitability criteria from these four perspectives. The profitability of projects from the perspectives of the society and host communities is the critical test for environmentally friendly oil and gas operations. Introduction Pollutants generated during oil and gas operations include produced formation water, drilling fluids, drilling cuttings, CO, SOx, NOx, particulates, oil leaks, oil spills, deck drainage, heat, noise, produced sands and all kinds of solid waste. These pollutants have a negative impact on the environment. Air, water and land have a lot of use value but little or no exchange value. In their natural state, they are often treated as free goods with no market value because of their abundance. Therefore, the costs of the pollution is often passed on to the society.1 The resulting outcry by environmental groups and non-governmental organizations encouraged many national governments to enact laws that protected the air, water and environmental quality as well as define and constrain the handling and disposal of hazardous waste.2 The oil companies treated the cost of meeting the new environmental laws as externalities and passed them on to the consumers. In 1990, externalities were estimated at 12%, 25% and 45% of the existing natural gas, oil and coal plants respectively.3 Some state regulatory commissions in USA and Canada now require that environmental externalities be incorporated into conventional resource selection, planning and decision making processes. This paper presents an approach of incorporating environmental impacts into conventional project economic models used in decision making in oil and gas operations. The new approach encompasses the costs and benefits to the major stakeholders in the process. The profitability of an investment opportunity is evaluated from the perspective of each major stakeholder. The opportunities that are profitable from all perspectives are screened out before they are ranked. This ensures that only the opportunities that are environmentally friendly are utilized.