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Discussion papers
https://doi.org/10.5194/se-2019-57
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-2019-57
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 27 Mar 2019

Research article | 27 Mar 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Solid Earth (SE).

Actors, actions and uncertainties: Optimizing decision making based on 3-D structural geological models

Fabian Antonio Stamm1, Miguel de la Varga1,2, and Florian Wellmann1 Fabian Antonio Stamm et al.
  • 1Computational Geoscience and Reservoir Engineering (CGRE), RWTH Aachen University, Germany
  • 2Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, Germany

Abstract. Uncertainties are common in geological models and have a considerable impact on model interpretations and subsequent decision making. This is of particular significance for high-risk, high-reward sectors, such as hydrocarbon exploration and production. Recent advances allows us to view geological modeling as a statistical problem that we can address with probabilistic methods. Using stochastic simulations and Bayesian inference, uncertainties can be quantified and reduced by incorporating additional geological information. In this work, we propose custom loss functions as a decision-making tool that builds upon such probabilistic approaches.

As an example, we devise a case in which the decision problem is one of estimating the uncertain volume of a structural hydrocarbon trap. We construct a synthetic 3-D model to represent a potential hydrocarbon system and develop algorithms for automatic trap volume calculation. Various volume probability distributions for different information scenarios are attained via Monte Carlo error propagation and Markov chain Monte Carlo sampling. For subsequent true value estimation, we design a case-specific loss function to reflect not only the decision-making environment, but also the preferences of differently risk-affine actors. Based on this function, optimizing for expected loss returns an actor's best estimate to base decision making on.

Our results show that the optimizing estimators shift according to the characteristics of the underlying value distribution. While overall spread leads to separation, risk-averse and risk-friendly decisions converge in the decision space and decrease in expected loss given narrower distributions. We thus consider the degree of decision convergence to be a measure for the state of knowledge and its inherent uncertainty at the moment of decision making. This decisive uncertainty does not change in alignment with model uncertainty but depends on alterations of critical parameters and respective interdependencies, in particular relating to seal reliability. Additionally, actors are affected differently by one set of information, depending on their risk affinity. It is therefore important to identify the model parameters which are most influential for the final decision in order to optimize the decision-making process.

Fabian Antonio Stamm et al.
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Status: open (until 20 Jun 2019)
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Fabian Antonio Stamm et al.
Data sets

Model input data F. A. Stamm https://doi.org/10.5281/zenodo.2595357

Model code and software

Model construction, analysis and simulation F. A. Stamm https://doi.org/10.5281/zenodo.2595357

Fabian Antonio Stamm et al.
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