为多相流问题中基于机器学习的代用模型建立巴洛孪生降阶模型

IF 4.6 1区 地球科学 Q2 ENVIRONMENTAL SCIENCES
T. Kadeethum, V. L. S. Silva, P. Salinas, C. C. Pain, H. Yoon
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To evaluate the performance of BBT-ROM, we conduct three three-dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. 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引用次数: 0

摘要

我们提出了一种名为 "提升巴洛双减阶建模(BBT-ROM)"的创新方法,用于提高多相流问题机器学习代用模型的可靠性。BBT-ROM 建立在巴罗双减阶建模的基础上,利用自监督学习,通过构建结构良好的输入参数和输出量潜空间,有效处理线性和非线性流形。为了应对多相流问题中由于注水井和断层造成的高对比度数据的挑战,我们在 BBT-ROM 中采用了提升算法。该算法顺序训练一组弱模型(即不准确模型),通过集合学习提高预测精度。为了评估 BBT-ROM 的性能,我们使用不同数量的输入参数和模型域特征,处理了三个三维多相流问题,包括注水和地质碳储存(GCS)。结果表明,BBT-ROM 在预测非湿相饱和度(如石油或 CO2 的饱和度)和流体压力方面表现出色,平均相对误差在 0.5% 到 3% 之间。重要的是,BBT-ROM 在 GCS 测试过程中面对有限的输入参数空间时表现出很强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Boosting Barlow Twins Reduced Order Modeling for Machine Learning-Based Surrogate Models in Multiphase Flow Problems
We present an innovative approach called boosting Barlow Twins reduced order modeling (BBT-ROM) to enhance the reliability of machine learning surrogate models for multiphase flow problems. BBT-ROM builds upon Barlow Twins reduced order modeling that leverages self-supervised learning to effectively handle linear and nonlinear manifolds by constructing well-structured latent spaces of input parameters and output quantities. To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT-ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. To evaluate the performance of BBT-ROM, we conduct three three-dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. The results demonstrate that BBT-ROM excels at predicting non-wetting phase saturation (e.g., oil or CO2$\mathrm{C}{\mathrm{O}}_{\mathrm{2}}$ saturation) and fluid pressure, with average relative errors ranging from 0.5% to 3%. Importantly, BBT-ROM showcases robustness when faced with limited input parameter space during GCS testing.
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来源期刊
Water Resources Research
Water Resources Research 环境科学-湖沼学
CiteScore
8.80
自引率
13.00%
发文量
599
审稿时长
3.5 months
期刊介绍: Water Resources Research (WRR) is an interdisciplinary journal that focuses on hydrology and water resources. It publishes original research in the natural and social sciences of water. It emphasizes the role of water in the Earth system, including physical, chemical, biological, and ecological processes in water resources research and management, including social, policy, and public health implications. It encompasses observational, experimental, theoretical, analytical, numerical, and data-driven approaches that advance the science of water and its management. Submissions are evaluated for their novelty, accuracy, significance, and broader implications of the findings.
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