Machine Learning Techniques for Chemical Prediction during Carbon Capture and Storage

碳捕获与封存（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二氧化碳的流体注入地下深处，通常是在废弃的油藏中。一旦注入，富含二氧化碳的流体就会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，其中一些是负的，包括那些驱动pH变化，导致矿物溶解或极端矿物沉淀，影响次生孔隙演化的因素。特别是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而得到修正。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极大地改变流体化学。这种代谢影响pH值并产生溶解的无机碳（DIC），从而改变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。该学生将使用强化机器学习与地球化学反应-输运模型相结合来预测CCS的最佳流体组成。给定一个位于地球化学状态a的CCS地点，以及一个目标地球化学状态B（这可能更有利于成功/有效的碳储存），该模型将预测所需的最佳条件（在这种情况下，一系列地球化学修正，根据修正的化学成分、注入持续时间、注入位置等进行定义）。强化学习方法的优点之一是，模型一旦训练好，对目标状态是不可知的。目标条件可以根据储存的二氧化碳总质量、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反应传输模型进行训练。作为第一步，我们目前正在研究一个神经网络，它可以在给定初始状态的情况下预测CCS过程中的地球化学变化。这项调查是基于生成和询问数千种独特的地球化学情景，并将它们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，可以预测在CCS过程中给定一组随机初始条件的pH值。这种技术有扩展的空间，可以预测任何感兴趣的地球化学量。学生将开发这个潜在的预测模型来训练强化学习模型来预测地球化学修正策略，这将有望在CCS中得到工业应用，但也可以在次生孔隙演化感兴趣的任何问题上得到应用，以及我们希望开发策略来影响地球化学系统特定变化的任何其他应用。

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.