Assessing the Value of Information According to Attitudes Towards Downside Risk and Upside Potential

Santos, S.G.; Schiozer, D.J.

doi:10.3997/2214-4609.201701558

Cited by 3 publications

(2 citation statements)

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“…In hydrocarbon field developments, VOI was suggested as the evaluation method [81,82]. It was seen that determining the probabilities at the decision notes [83] and incorporating the risk attitude of the decision makers (e.g., [84]), risk hurdles [85] and performing sequential appraisals [86] are particularly difficult. Geothermal field development planning has thus far employed VOI mainly for individual costly data acquisition activities [87] or to compare a limited number of scenarios [88].…”

Section: Human Suggested Data Acquisitions Strategiesmentioning

confidence: 99%

Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

Clemens,

Chiotoroiu,

Corso

et al. 2024

Energies

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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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Section: Human Suggested Data Acquisitions Strategiesmentioning

confidence: 99%

Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

Clemens,

Chiotoroiu,

Corso

et al. 2024

Energies

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“…Recently, more applications have emerged-like those of Clemen (1996), Coopersmith and Cunningham (2002), Suslick and Schiozer (2004)-which enrich the process of assessing the VoI decision problem from a methodological perspective. Several papers, such as Walls (2005) and Vilela et al (2017), have discussed the use of utility theory in VoI assessment in the oil and gas industry; similarly, Santos and Schiozer (2017) discussed the impact of the risk attitude of the decision makers in VoI assessments; Kullawan et al (2017) developed a discretized--programming approach, based on value of information, to optimize stochastic-dynamic the geosteering operations; Steineder et al (2018) discussed the maximization of the VoI on a horizontal polymer pilot project; all these researchers used one or more crisp decision criteria to make decisions.…”

Section: Review Of Value Of Informationmentioning

confidence: 99%

A fuzzy inference system applied to value of information assessment for oil and gas industry

Vilela

Oluyemi

Petrovski

2019

Decis. Mak. Appl. Manag. Eng.

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Value of information is a widely accepted methodology for evaluating the need to acquire new data in the oil and gas industry. In the conventional approach to estimating the value of information, the outcomes of a project assessment relate to the decision reached following Boolean logic. However, human thinking logic is more complex and include the ability to process uncertainty. In addition, in value of information assessment, it is often desirable to make decisions based on multiple economic criteria, which, independently evaluated, may suggest opposite decisions. Artificial intelligence has been used successfully in several areas of knowledge, increasing and enhancing analytical capabilities. This paper aims to enrich the value of information methodology by integrating fuzzy logic into the decisionmaking process; this integration makes it possible to develop a human thinking assessment and coherently combine several economic criteria. To the authors' knowledge, this is the first use of a fuzzy inference system in the domain of value of information. The methodology is successfully applied to a case study of an oil and gas subsurface assessment where the results of the standard and fuzzy methodologies are compared, leading to a more robust and complete evaluation.

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Case Studies for the Value of Information and Flexibility in the Oil and Gas Industry

Vilela¹,

Oluyemi²

2021

Value of Information and Flexibility

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Assessing the Value of Information According to Attitudes Towards Downside Risk and Upside Potential

Cited by 3 publications

References 0 publications

Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

A fuzzy inference system applied to value of information assessment for oil and gas industry

Case Studies for the Value of Information and Flexibility in the Oil and Gas Industry

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