用贝叶斯优化提升碳捕集项目经济性与可持续性
Bayesian Neural Network Surrogates for Bayesian Optimization of Carbon Capture and Storage Operations
- 用贝叶斯优化替代传统方法，结合多种随机模型加速决策变量寻优
- 在多目标、高维场景下，新模型比经典高斯过程更有效
- 首次将先进贝叶斯优化应用于油藏工程，助力绿色能源转型
碳捕集与封存（CCS）是实现可持续未来的关键技术。该过程通过将超临界CO₂注入地下储层，不仅减少碳排放应对气候变化，还能延长油气田生命周期，推动能源绿色转型。本文针对CCS项目开发中的决策变量优化问题，采用无需梯度的贝叶斯优化（BO）方法，系统比较了高斯过程（GP）以外的多种新型随机模型在复杂场景下的表现。研究聚焦于高维、多目标且量纲不一的情况，这些情形下传统GP常表现不佳。通过引入净现值（NPV）作为核心优化目标，新框架在提升经济可行性的同时保障技术可持续部署。本研究首次将前沿贝叶斯优化方法应用于油藏工程领域，验证了其在寻找更优随机模型方面的潜力，为能源行业可持续发展提供了高效优化路径。
原文摘要 · Abstract (English)
Carbon Capture and Storage (CCS) stands as a pivotal technology for fostering a sustainable future. The process, which involves injecting supercritical CO$_2$ into underground formations, a method already widely used for Enhanced Oil Recovery, serves a dual purpose: it not only curbs CO$_2$ emissions and addresses climate change but also extends the operational lifespan and sustainability of oil fields and platforms, easing the shift toward greener practices. This paper delivers a thorough comparative evaluation of strategies for optimizing decision variables in CCS project development, employing a derivative-free technique known as Bayesian Optimization. In addition to Gaussian Processes, which usually serve as the gold standard in BO, various novel stochastic models were examined and compared within a BO framework. This research investigates the effectiveness of utilizing more exotic stochastic models than GPs for BO in environments where GPs have been shown to underperform, such as in cases with a large number of decision variables or multiple objective functions that are not similarly scaled. By incorporating Net Present Value (NPV) as a key objective function, the proposed framework demonstrates its potential to improve economic viability while ensuring the sustainable deployment of CCS technologies. Ultimately, this study represents the first application in the reservoir engineering industry of the growing body of BO research, specifically in the search for more appropriate stochastic models, highlighting its potential as a preferred method for enhancing sustainability in the energy sector.
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