Machine Learning Techniques for Chemical Prediction during Carbon Capture and Storage

碳捕获和储存（CCS）是限制人为气候变化的重要组成部分。CCS的碳储存方面涉及将富含CO2的流体注入地下深处，通常是废弃的油藏。一旦注入，富含二氧化碳的流体可以以多种方式与岩石相互作用。其中一些是积极的，包括防止泄漏的碳矿化。然而，其中一些是负面的，包括那些驱动pH值的变化，导致矿物溶解或极端的矿物沉淀，影响次生孔隙度的演变。特别是在各种自然环境中的流体化学可以通过环境中微生物群落的作用来修正。特别是，微生物硫酸盐还原（MSR）和其他氧化还原敏感的微生物代谢可以大大改变流体化学。这种代谢影响pH值并产生溶解无机碳（DIC），这反过来又改变了系统内矿物质的饱和状态。最终，这些微生物代谢可以对地球化学系统的矿物学和整体孔隙度和渗透率结构产生很大影响。学生将使用强化机器学习结合地球化学反应性传输模型来预测CCS的最佳流体成分。如果一个CCS场址的地球化学状态为A，目标地球化学状态为B（可能更有利于成功/有效地储存碳），则该模型将预测最佳条件（在这种情况下，一系列地球化学修正物，根据修正物的化学成分、注入持续时间、注入位置等确定）。needed.强化学习方法的优点之一是，模型一旦训练好，就不知道目标状态。目标条件可以根据储存的CO2总质量、总碱度、矿物体积甚至同位素组成来指定。强化机器学习将使用多维同位素激活的反应传输模型进行训练。作为第一步，我们目前正在使用一个神经网络，该网络可以在给定初始状态的情况下预测CCS期间的地球化学变化。这项研究基于生成和询问数千个独特的地球化学场景，并将其直接输入神经网络进行训练和测试。目前，我们有一个回归模型，可以在CCS过程中给定一组随机初始条件来预测pH值。这种技术有扩展的余地，可以预测任何感兴趣的地球化学量。学生将开发这个基础的预测模型来训练强化学习模型来预测地球化学修正政策，这将有望在CCS中得到工业应用，但也可以解决次生孔隙度演化感兴趣的任何问题，以及我们希望制定策略以实现特定变化地球化学系统的任何其他应用。

Carbon capture and storage (CCS) is an essential part of limiting anthropogenic climate change. The carbon storage aspect of CCS involves the injection of CO2-rich fluid deep underground, typically in abandoned oil reservoirs. Once injected, the CO2-rich fluid can interact with the rocks in a number of ways. Some of these are positive, including the mineralisation of carbon which prevents leakage. However, some of these are negative, including those which drive changes in pH and lead to mineral dissolution or extreme mineral precipitation, influencing secondary porosity evolution. Fluid chemistry in various natural environments in particular can be amended through the action of microbial communities in the environment. In particular, microbial sulfate reduction (MSR) and other redox-sensitive microbial metabolisms can drastically alter fluid chemistry. This metabolism influences pH and generates dissolved inorganic carbon (DIC), which in turn changes the saturation state of minerals within the system. Ultimately, these microbial metabolisms can have a large effect on the mineralogy and overall porosity and permeability structure of the geochemical system.The student will use reinforcement machine-learning coupled to a geochemical reactive-transport model to predict the optimal fluid composition for CCS. Given a site for CCS in a geochemical state A, with a target geochemical state B (that might be more conducive to a successful/efficient storage of carbon) the model will predict the optimum conditions (in this case a series of geochemical amendments, defined in terms of chemical composition of amendment, duration of injection, location of injection etc.) needed. One of the advantages of the reinforcement learning approach is that the model, once trained, is agnostic as to the target state. Target conditions could be specified in terms of total mass of CO2 stored, total alkalinity, mineral volumes, or even isotope composition.The reinforcement machine-learning will be trained using a multi-dimensional isotope-enabled reactive-transport model. As a first step we currently work with a neural network that can predict geochemical changes during CCS, given an initial state. This investigation is based on generating and interrogating thousands of unique geochemical scenarios and feeding them directly into a neural network for training and testing. Currently we have a regression model that can predict pH given a random set of initial conditions during CCS. There is scope for expansion of this technique to predict any geochemical quantity of interest. The student will develop this underlying, predictive model to train the reinforcement learning model to predict geochemical amendment policy, which will hopefully have industrial applications in CCS, but also any problem where secondary porosity evolution is of interest, as well any other application where we wish to develop strategies to effect a specific changes geochemical systems.