Probabilistic knowledge-based characterization of conceptual geological models

Abstract

The construction of conceptual geological models is an essential task in petroleum exploration, especially during the early stages of investment, when evidence about the subsurface is limited. In this task, geoscientists recreate the most likely geological scenarios that led to potential accumulation of reserves in a target block, based on past experience, historical analogues, and interpreted “signatures” that were left in the data by physical processes. Due to cognitive constraints, this task has traditionally focused on the single most likely conceptual scenario, or at most, a reduced set of scenarios chosen a priori via ad-hoc methods, which often lead to improper block valuation and severe money losses. In this work, we propose a probabilistic framework for reasoning about conceptual geological scenarios that helps domain experts maintain multiple hypotheses throughout the exploration program. The framework is extensible and can be instantiated automatically from simple knowledge templates, a form of “knowledge standard” in the company. We show how the acquired knowledge can be leveraged for uncertainty mitigation using concepts from information theory, and assess the framework qualitatively in a real case study.

Keywords

• Probabilistic graphical models
• Bayesian network
• Probabilistic reasoning
• Information gain
• Uncertainty quantification