Expected value, downside risk and upside potential as decision criteria in production strategy selection for petroleum field development

Santos, Susana M.G.; Botechia, Vinícius Eduardo; Schiozer, Denis José; Gaspar, Ana Teresa

doi:10.1016/j.petrol.2017.07.002

Cited by 18 publications

(16 citation statements)

References 42 publications

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“…To determine the value of production strategies with and without information, considering different attitudes towards upsides and downsides, we apply Equation 3 (Santos et al, 2017). 3where: ε -production strategy value adjusted to the decision maker's attitude; EMV -expected monetary value; S B-2 and S B+ 2 -lower and upper semi-variance (squared semi-deviation) from the benchmark B; C draversion coefficient to downside risk; C up -expectation coefficient to upside potential; τ dr and τ up -tolerance level to downside risk and to upside potential, expressed in the same units of the distribution and taking strictly positive values.…”

Section: Methodsmentioning

confidence: 99%

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

Santos

Schiozer

2017

SPE Europec Featured at 79th EAGE Conference and Exhibition

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The Value of Information (VoI) analysis typically assesses information opportunities to manage uncertainty. The VoI is traditionally estimated using the expected monetary value (EMV), which overlooks the decision maker's (DM) attitude towards upsides and downsides. In this study, we assess the VoI for DMs with different attitudes, under different levels of information reliability. We define attitude to be as: neutrality to upsides and downsides, aversion to downside risk, and willingness to exploit upside potential. We applied a simple and flexible formula, which incorporates EMV with lower and upper semi-deviations from a benchmark, to quantify downside risk and upside potential. We determined VoI using many uncertain scenarios to maintain interactions between parameters instead of deterministically isolating the uncertainty under analysis. To accelerate analysis and reduce computational costs, we used a set of candidate production strategies optimized for extremely different scenarios. Our case study was the UNISIM-I-D, a benchmark reservoir model with a key structural uncertainty affecting production strategy selection. We used an appraisal well as an information source, and four hypothetical DMs with different attitudes. Our results showed that these DMs value information differently, that one DM may decide to acquire information while another may not, for the same situation. In our case study, information reduced downside risk but did not increase upside potential, meaning that information was more valuable to risk-averse DMs (which was up to 20 times higher), and less valuable to DMs exclusively focused on maximizing upsides.

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Section: Methodsmentioning

confidence: 99%

“…To do so, we apply a straightforward formula previously proposed by Santos et al (2017), which assesses the value of production strategies incorporating the DM's attitude. We use this formula to calculate VoI for different hypothetical DMs, and considering different levels of information reliability.…”

Section: Objectivementioning

confidence: 99%

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

Santos

Schiozer

2017

SPE Europec Featured at 79th EAGE Conference and Exhibition

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“…including reservoir, economic, and other uncertainties. A Robust Optimization procedure (Silva et al, 2016) can be used, or a risk-return analysis (Santos et al, 2017a) to select the best strategy from the candidates obtained in Step 9. If the simulation runtime for the number of scenarios is unfeasible, the RMs can be used to represent them.…”

Section: Production Strategy Selection Under Uncertaintymentioning

confidence: 99%

“…12). Santos et al (2017a) combined the expected monetary value, downside risk, and upside potential (eq. (2)) to determine the economic value of a production strategy adjusted to the decision maker's attitude, ɛ(NPV), while maintaining the same units and dimension as the NPV:…”

Section: The Robust Emr Consists Ofmentioning

confidence: 99%

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Model-based decision analysis applied to petroleum field development and management

Schiozer

Santos

et al. 2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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This work describes a new methodology for integrated decision analysis in the development and management of petroleum fields considering reservoir simulation, risk analysis, history matching, uncertainty reduction, representative models, and production strategy selection under uncertainty. Based on the concept of closed-loop reservoir management, we establish 12 steps to assist engineers in model updating and production optimization under uncertainty. The methodology is applied to UNISIM-I-D, a benchmark case based on the Namorado field in the Campos Basin, Brazil. The results show that the method is suitable for use in practical applications of complex reservoirs in different field stages (development and management). First, uncertainty is characterized in detail and then scenarios are generated using an efficient sampling technique, which reduces the number of evaluations and is suitable for use with numerical reservoir simulation. We then perform multi-objective history-matching procedures, integrating static data (geostatistical realizations generated using reservoir information) and dynamic data (well production and pressure) to reduce uncertainty and thus provide a set of matched models for production forecasts. We select a small set of Representative Models (RMs) for decision risk analysis, integrating reservoir, economic and other uncertainties to base decisions on risk-return techniques. We optimize the production strategies for (1) each individual RM to obtain different specialized solutions for field development and (2) all RMs simultaneously in a probabilistic procedure to obtain a robust strategy. While the second approach ensures the best performance under uncertainty, the first provides valuable insights for the expected value of information and flexibility analyses. Finally, we integrate reservoir and production systems to ensure realistic production forecasts. This methodology uses reservoir simulations, not proxy models, to reliably predict field performance. The proposed methodology is efficient, easy-to-use and compatible with real-time operations, even in complex cases where the computational time is restrictive.

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Estimation of the chance of success of a four-dimensional seismic project for a developed oil field

et al. 2022

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Expected value, downside risk and upside potential as decision criteria in production strategy selection for petroleum field development

Cited by 18 publications

References 42 publications

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

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

Model-based decision analysis applied to petroleum field development and management

Estimation of the chance of success of a four-dimensional seismic project for a developed oil field

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