非常规/复杂油藏

深度学习框架预测二氧化碳矿化动态

本文开发了一种深度学习工作流程，可以预测盐水含水层中二氧化碳矿化随时间和空间的变化，与传统的基于物理的模拟相比，提供了一种更有效的方法。

MS 概念反应岩储层模型，填充了非均质渗透率值 (10 - 100 mD)，圆柱视图。
MS 概念反应岩储层模型，填充了非均质渗透率值 (10 - 100 mD)，圆柱视图。

反应性岩石在暴露于充有CO 2的水中时会发生一系列反应，从而形成稳定的碳酸盐。这些碳酸盐可以储存碳数千年。为了更好地了解这些反应地层中CO 2和盐水之间的相互作用，数值模拟是一种有用的工具。然而，模拟这些储层中的流体流动可能会带来巨大的计算挑战。

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Unconventional/complex reservoirs

Deep-Learning Framework Forecasts Dynamics of Carbon Dioxide Mineralization

This paper develops a deep-learning work flow that can predict the changes in carbon dioxide mineralization over time and space in saline aquifers, offering a more-efficient approach compared with traditional physics-based simulations.

MS Conceptual reactive rock reservoir model populated with the heterogeneous permeability values (10 – 100 mD), cylindrical view.
MS Conceptual reactive rock reservoir model populated with the heterogeneous permeability values (10 – 100 mD), cylindrical view.

Reactive rocks, when exposed to CO2-charged waters, can undergo a series of reactions leading to the formation of stable carbonates. These carbonates can store carbon for thousands of years. To better understand the interplay between CO2 and brine in these reactive formations, numerical simulations are a useful tool. However, simulating fluid flow in these reservoirs can pose significant computational challenges.

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