Integration of spatial data context into machine learning models

The oil and gas industry, over its long history, has accumulated a large volume of spatial data from various resources like seismic surveys, well logs and production information, which provide a huge potential for data analytics and machine learning application to assist physics-based models. The ongoing digitalization transformation in the oil and gas industry also emphasizes this opportunity. In addition, the nature of unconventional resources poses the challenges of high uncertainty among the data measurements and less well-understood production mechanisms. The challenges bring data-driven solutions like machine learning to our attention to support reservoir modeling and decision-making. However, the data-driven tools tend to ignore essential spatial contexts when they are directly used for subsurface data, which may cause unrealistic results and high-cost risk. The spatial context mentioned here refers to the spatial continuity, heterogeneity, sampling bias, data sparsity relative to the reservoir scale, etc. Also, in traditional workflows, machine learning especially deep learning are used as a black-box tool that is hard for domain experts to interrogate and the data sparsity renders the models with a large number of parameters prone to overfit. Due to the high value of subsurface development decisions, models must be understandable to be used to support decision making. Therefore, it is critical to customize the current machine learning and data analytics techniques to account for the spatial, multivariate, and multiscale information among the data as well as bring domain knowledge and physics constraints into the workflow, providing model interpretability. This dissertation bridges the gap between the widely applied data-driven workflows and complex spatial context among subsurface data. This dissertation presents interpretable, data-driven workflows specifically designed for spatial, multivariate, multiscale data with domain expertise and physics constraints for more accurate subsurface predictions with greater confidence to decision-support. The proposed workflows improve classical data-driven predictions in subsurface applications by integrating the spatial context, physics constraints and domain expertise with innovative use of geostatistics and graph neural networks. The novel, comprehensive workflows cover spatial data analytics steps from data pre-processing, including sampling bias mitigation, spatial feature engineering, anomaly segmentation, to production forecasting to support decision-making.Petroleum and Geosystems Engineerin