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Abstract

Summary

This paper introduces Orison, a Python library designed to streamline subsurface data assimilation, particularly for geothermal applications. Orison integrates with various algorithms and libraries, allowing users to perform tasks such as optimization, sensitivity analysis, and metamodeling. It provides a flexible, user-friendly architecture that supports diverse numerical models and workflows without bias toward specific methods.

A synthetic geothermal case study demonstrates Orison’s capabilities. Using the ComPASS simulator, the study generates temperature data from a faulted, fractured reservoir and compares the performance of three optimization algorithms: Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution (DE), and Efficient Global Optimization (EGO). The results reveal that DE delivers better results than other methods.

The paper highlights Orison’s visualization tools for diagnostics, parameter exploration, and algorithm comparison. It emphasizes Orison’s dual parallelization capabilities for simulations and evaluations, ensuring computational efficiency. Future work aims to transition from synthetic to real-world geothermal datasets, expand to multi-source data assimilation problems, and incorporate additional algorithms.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202539035
2025-03-24
2026-02-11

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References

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Orison : An Integrated Environment for Subsurface Data Assimilation, a Geothermal Case Study
Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.202539035
/content/papers/10.3997/2214-4609.202539035
/content/papers/10.3997/2214-4609.202539035
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