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

Abstract

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.

Constraining stochastic 3-D structural geological models with topology information using approximate Bayesian computation in GemPy 2.1

“…The simulation experiments for our two case studies demonstrated that we are able to approximate posterior distributions to obtain probabilistic geomodel ensembles that honor both our prior parameter knowledge and qualitative geological knowledge. If the applied topological information is meaningful, then the constrained stochastic geomodel ensemble will see a meaningful reduction in uncertainty and will subsequently allow for more precise model-based estimates and decision-making (Stamm et al, 2019). More importantly, the (approximate) Bayesian approach requires the explicit statement of the geological knowledge (here the topology information) used in the probabilistic geomodel, increasing the transparency of assumptions made during the geomodeling process and any subsequent decisions.…”

Constraining stochastic 3-D structural geological models with topology information using Approximate Bayesian Computation using GemPy 2.1

“…both our prior parameter knowledge and qualitative geological knowledge. If the applied topological information is meaningful, then the constrained stochastic geomodel ensemble will see a meaningful reduction in uncertainty, and will subsequently allow for more precise model-based estimates and decision-making (Stamm et al, 2019). More importantly, the (approximate)…”

Quantification of uncertainty in 3-D seismic interpretation: implications for deterministic and stochastic geomodeling and machine learning

“…Such hybrid approaches may provide the best solutions when the time and computational costs of full stochastic modeling are too high and/or when elements of the uncertainty in the geomodel are not best represented as simple stochastic functions, such as different conceptual models (e.g., for fault network topologies). Stamm et al (2019) have recently explored how both fault throw and fault sealing uncertainty can be incorporated into stochastic geological modeling workflows, and studies like ours can help inform stochastic parameterization with how fault throw uncertainties can change along strike depending on changes in the seismic data quality.…”

“…Our work is thus concerned with quantifying the scope of uncertainties in seismic interpretation, which represents inevitably biased human judgement under uncertainty (Tversky and Kahneman, 1974). This "subjective" uncertainty is in contrast to more "objective" uncertainty related to the geophysical acquisition of the data themselves (Tannert et al, 2007;Bond, 2015). Previous work has shown that significant conceptual uncertainties and biases are encountered during the interpretation process of 2-D seismic lines (Bond et al, 2007(Bond et al, , 2011Macrae, 2013;Bond, 2015;Alcalde et al, 2017a, c), as well as the impact of seismic image quality on the interpretation (Alcalde et al, 2017b).…”