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付强,周守为,李清平
《中国工程科学》 2015年 第17卷 第9期 页码 123-132
天然气水合物是甲烷等烃类气体与水在高压低温条件下形成的笼形化合物，俗称可燃冰，将有望成为继页岩气、致密气、煤层气、油砂等之后的储量最为巨大的接替能源，主要分布在北极冻土带和沿海大陆架300~3000
高树琴,赵霞,方精云
《中国工程科学》 2016年 第18卷 第1期 页码 73-79 doi: 10.15302/J-SSCAE-2016.01.010
本文通过综述当前我国草地碳库的研究成果，并利用1982—2011年的遥感影像，估算出我国草地生态系统碳库约为31.2 PgC，其中96 %储存于土壤中。由于我国草地类型多样，分布地域广阔，造成草地植被碳密度分布的空间异质性很高。内蒙古是草地植被碳库最大的省份，其次是西藏和青海，六大牧区的植被碳库占全国草地植被碳库总量的71 %。然而，我国90 %的天然草地发生不同程度的退化，采取有效的人工管理措施和实施重大的生态建设工程，均对草地碳库的恢复具有明显的作用，说明我国草地有很大的碳汇潜力。
Xueying Lu, Kirk E. Jordan, Mary F. Wheeler, Edward O. Pyzer-Knapp, Matthew Benatan
《工程（英文）》 2022年 第18卷 第11期 页码 96-104 doi: 10.1016/j.eng.2022.06.011
We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization (BO) for injection well scheduling optimization in geological carbon sequestration. This work represents one of the first at tempts to apply BO and high-fidelity physics models to geological carbon storage. The implicit parallel accurate reservoir simulator (IPARS) is utilized to accurately capture the underlying physical processes during CO2 sequestration. IPARS provides a framework for several flow and mechanics models and thus supports both stand-alone and coupled simulations. In this work, we use the compositional flow module to simulate the geological carbon storage process. The compositional flow model, which includes a hysteretic three-phase relative permeability model, accounts for three major CO2 trapping mechanisms: structural trapping, residual gas trapping, and solubility trapping. Furthermore, IPARS is coupled to the International Business Machines (IBM) Corporation Bayesian Optimization Accelerator (BOA) for parallel optimizations of CO2 injection strategies during field-scale CO2 sequestration. BO builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm—the Gaussian process regression, and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample. The IBM BOA addresses the three weaknesses of standard BO that limits its scalability in that IBM BOA supports parallel (batch) executions, scales better for high-dimensional problems, and is more robust to initializations. We demonstrate these merits by applying the algorithm in the optimization of the CO2 injection schedule in the Cranfield site in Mississippi, USA, using field data. The optimized injection schedule achieves 16% more gas storage volume and 56% less water/surfactant usage compared with the baseline. The performance of BO is compared with that of a genetic algorithm (GA) and a covariance matrix adaptation (CMA)-evolution strategy (ES). The results demonstrate the superior performance of BO, in that it achieves a competitive objective function value with over 60% fewer forward model evaluations.
孙思琦,陈永喆,王聪,胡庆芳,吕一河
《中国工程科学》 2022年 第24卷 第5期 页码 97-106 doi: 10.15302/J-SSCAE-2022.05.012
标题 作者 时间 类型 操作
基于样地调查的地质碳储量的贝叶斯优化
Xueying Lu, Kirk E. Jordan, Mary F. Wheeler, Edward O. Pyzer-Knapp, Matthew Benatan
期刊论文
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