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The oil and gas industry contains significant inherent risks that greatly affect a company's ability to achieve predicted performance. In an attempt to better manage risk and uncertainty during the decision making process, the efficient frontier concept has been proposed as a method for portfolio optimization. This paper analyzes the application of the efficient frontier concept to a yearly budget allocation process considering unconventional resource play opportunities. Economic and optimization models were developed with assumptions specific to resource plays. The models show that the expected risk, uncertainty, and return on investment change according to the level of investment, project selection, the number of planned wells, and to the grouping of wells for budgeting purposes. The budget dependency of the risk and return cause an oddly shaped efficient frontier resulting in mean-variance efficient portfolios that may not be optimal when based on a set level of risk. In addition, the results show that conventional rank and cut methods for portfolio selection may suffice for resource play budget allocation. However, even though the efficient frontier does not often offer definitive solutions for portfolio optimization, illustrating the interactions among portfolio properties and providing insight into inefficient budget allocations could make the efficient frontier optimization process invaluable. Introduction In Markowitz's seminal article of 1952, he introduced the investment community to the idea of the Efficient Frontier (EF) and mean-variance efficient portfolios. An efficient portfolio maximizes expected value for a given level of risk (or uncertainty) and a set capital investment (Figure 1). Many articles have analyzed the possibility of using EF concepts for oil and gas development decisions (Adekunle 2006; Edwards and Hewett 1993; Erdogan et al. 2005; Faya et al. 2007; Haskett et al. 2004; Merritt 2000; Orman and Duggan 1999; Schuyler 2003; Simpson 2002) and a review of these articles is suggested for more elaborate elucidation on the topic. The goal of the project was to explore the potential for the application of the EF for budget allocation among resource play opportunities. Can the EF provide insight for the decision making process? Ideally, a decision framework could provide a concise answer to a problem. However, most studies suggest that the EF should only be used to gain clarity on the interactions among a large number of correlated and uncorrelated investments, and to illustrate which portfolios are not efficient and thus should be avoided (Adekunle 2006; Bratvold 2003; Edwards and Hewett 1993; Erdogan et al. 2005; Faya et al. 2007; Simpson 2002). In contrast, the more conventional rank and cut method based on an economic metric for budget allocation may not be perceived as the best method for risk reduction, but it does provide a specific answer, and it may in fact be an acceptable risk-mitigating method for resource play budget allocation.
The oil and gas industry contains significant inherent risks that greatly affect a company's ability to achieve predicted performance. In an attempt to better manage risk and uncertainty during the decision making process, the efficient frontier concept has been proposed as a method for portfolio optimization. This paper analyzes the application of the efficient frontier concept to a yearly budget allocation process considering unconventional resource play opportunities. Economic and optimization models were developed with assumptions specific to resource plays. The models show that the expected risk, uncertainty, and return on investment change according to the level of investment, project selection, the number of planned wells, and to the grouping of wells for budgeting purposes. The budget dependency of the risk and return cause an oddly shaped efficient frontier resulting in mean-variance efficient portfolios that may not be optimal when based on a set level of risk. In addition, the results show that conventional rank and cut methods for portfolio selection may suffice for resource play budget allocation. However, even though the efficient frontier does not often offer definitive solutions for portfolio optimization, illustrating the interactions among portfolio properties and providing insight into inefficient budget allocations could make the efficient frontier optimization process invaluable. Introduction In Markowitz's seminal article of 1952, he introduced the investment community to the idea of the Efficient Frontier (EF) and mean-variance efficient portfolios. An efficient portfolio maximizes expected value for a given level of risk (or uncertainty) and a set capital investment (Figure 1). Many articles have analyzed the possibility of using EF concepts for oil and gas development decisions (Adekunle 2006; Edwards and Hewett 1993; Erdogan et al. 2005; Faya et al. 2007; Haskett et al. 2004; Merritt 2000; Orman and Duggan 1999; Schuyler 2003; Simpson 2002) and a review of these articles is suggested for more elaborate elucidation on the topic. The goal of the project was to explore the potential for the application of the EF for budget allocation among resource play opportunities. Can the EF provide insight for the decision making process? Ideally, a decision framework could provide a concise answer to a problem. However, most studies suggest that the EF should only be used to gain clarity on the interactions among a large number of correlated and uncorrelated investments, and to illustrate which portfolios are not efficient and thus should be avoided (Adekunle 2006; Bratvold 2003; Edwards and Hewett 1993; Erdogan et al. 2005; Faya et al. 2007; Simpson 2002). In contrast, the more conventional rank and cut method based on an economic metric for budget allocation may not be perceived as the best method for risk reduction, but it does provide a specific answer, and it may in fact be an acceptable risk-mitigating method for resource play budget allocation.
Low-enthalpy geothermal energy can make a major contribution towards reducing CO2 emissions. However, the development of geothermal reservoirs is costly and time intensive. In particular, high capital expenditures, data acquisition costs, and long periods of time from identifying a geothermal resource to geothermal heat extraction make geothermal field developments challenging. Conventional geothermal field development planning follows a linear approach starting with numerical model calibrations of the existing subsurface data, simulations of forecasts for geothermal heat production, and cost estimations. Next, data acquisition actions are evaluated and performed, and then the models are changed by integrating the new data before being finally used for forecasting and economics. There are several challenges when using this approach and the duration of model rebuilding with the availability of new data is time consuming. Furthermore, the approach does not address sequential decision making under uncertainty as it focuses on individual data acquisition actions. An artificial intelligence (AI)-centric approach to field development planning substantially improves cycle times and the expected rewards from geothermal projects. The reason for this is that various methods such as machine learning in data conditioning and distance-based generalized sensitivity analysis assess the uncertainty and quantify its potential impact on the final value. The use of AI for sequential decision making under uncertainty results in an optimized data acquisition strategy, a recommendation of a specific development scenario, or advice against further investment. This approach is illustrated by applying AI-centric geothermal field development planning to an Austrian low-enthalpy geothermal case. The results show an increase in the expected value of over 27% and a reduction in data acquisition costs by more than 35% when compared with conventional field development planning strategies. Furthermore, the results are used in systematic trade-off assessments of various key performance indicators.
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