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Abstract

Summary

Subsurface flow model calibration is often formulated to adhere to a given prior geologic scenario for describing th econtinuity in rock property distributions while minimizing the mismatch between predicted and observed flow responses. In probabilistic model calibration, the prior geologic continuity model is given either through parametric distributions with known parameters (e.g. Gaussian priors with known covariances) or through empirical distributions with sample realizations that share the same statistical attributes or spatial patterns (e.g., a training image). The conventional assumption is to use the prior model as a geologic constraint to maintain consistency with descriptions provided by the geologist. However, geologists often deal with various sources of uncertainty that complicate the construction of prior models to describe the variability in the spatial distribution of subsurface properties. A natural question to ask is whether dynamic data can help geologists to constrain or narrow down the number of possible scenarios. The purpose of this paper is to evaluate the feasibility of using dynamic data to accept or reject prior geologic scenarios using a pattern-based approach. We develop a twostage calibration process, where in the first stage dynamic data is used to identify plausible geologic scenarios using approximate parametric solutions while in the second stage geologic feasibility is ensured through a pattern-based mapping with a supervised machine learning technique. A series of model calibration problems are used to evaluate the performance of the proposed formulation and to discuss its properties. These examples show the value of incorporating dynamic data in selecting consistent geologic scenarios prior to performing full model calibration and uncertainty quantification.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.201802201
2018-09-03
2024-04-28

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A Pattern-Based Approach For Geologic Scenario Identification And Reservoir Model Calibration
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201802201
/content/papers/10.3997/2214-4609.201802201
/content/papers/10.3997/2214-4609.201802201
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