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Abstract

Summary

The initial stages of oil and gas exploration are delineated by the reduced and sparse presence of well-log data or its absence. The reservoir characterization, at this phase, is mainly done with geophysics and information about the region’s geology. The lack of well-log data information generates a higher uncertainty in decision-making, such as choosing the most favorable places for oil and gas. A framework is proposed to create conceptual geological scenarios incorporating the principles of geostatistical seismic inversion and machine learning techniques to reduce the dimensionality and identify which parameters create a higher and lower uncertainty in the early stages of oil and gas exploration. The case study demonstrates the proposed framework, where different simulations based on a scale factor to their variograms were generated. The comparison in the MDS Space showed how closer and farther the synthetic images were to a target. The farthest images and their parameters can be falsified, and the closest image can be sampled for newer realizations using co-simulation and deep-learning generative techniques. The further results can be inverted to reservoir properties and quantified their uncertainty.

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2023-11-27
2026-04-13

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/content/papers/10.3997/2214-4609.202335074
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Framework for Generating a Space of Uncertainty in Early Stages of Oil and Gas Exploration
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202335074
/content/papers/10.3997/2214-4609.202335074
/content/papers/10.3997/2214-4609.202335074
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