反演(地质)
人工神经网络
多孔性
石油工程
饱和(图论)
水准点(测量)
生成语法
过程(计算)
人工智能
地质学
计算机科学
机器学习
地球物理学
岩土工程
地震学
数学
构造学
大地测量学
组合数学
操作系统
标识
DOI:10.1016/j.geoen.2023.212229
摘要
Geological CO2 storage is aiming to inject the carbon dioxide into subsurface formations, and geophysical measurements are then commonly used to monitor the fluid long-term and safe storage for risk assessment during and after the injection. In this process, the rock physical inversion is an essential part for determining the reservoir parameters such as porosity or fluid saturation for potential storage calculation or monitoring fluid migration. We propose a deep learning approach to invert reservoir parameters based on rock properties or seismically inverted results. The rock physics equations are incorporated into the learning process, leading the neural networks as physics informed. We choose the generative adversarial networks to obtain ensemble predictions by varying the input latent vectors, from which the uncertainty analysis is performed. The proposed approach is applied to the Sleipner 2019 Benchmark Model for inverting reservoir porosity and CO2 saturation with rock properties in terms of velocities and bulk density as inputs. The supervised learning and physics-informed neural network are also applied for a comparison; however, both of them cannot access the prediction uncertainty that is important for risk reduction by decision makers.
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