Enhancement of unconventional oil and gas production forecasting using mechanistic-statistical modeling

Abstract

Unconventional oil and gas basins have rapidly become expansive and critical energy resource systems. However, accurately predicting highly variable well production rates remains challenging, given the typically poor subsurface characterization and complex flow behavior involved. This creates uncertainty about future resource availability, undermining reliable economic assessments and good stewardship of the resource. Production, drilling, and hydraulic fracturing datasets from thousands of wells offer insight into patterns of productivity but are noisy and incomplete. Fully exploiting this information is only possible by leveraging contextual knowledge to structure observations. This thesis provides a novel framework for combining machine learning and probabilistic modeling with domain knowledge and physics to understand and predict well productivity. Technology is a constantly evolving driver of productivity that must be captured in forecasts. This thesis shows that the immense geological heterogeneity of unconventional basins can lead to overestimating the role of technology when the best areas are increasingly targeted alongside design improvements. This conflation is remedied using spatial structure to infer geological productivity as a latent variable. A regression-kriging technique is shown to effectively disentangle technology from geology--which play roughly equal roles--and reduce error in initial well productivity predictions by more than a third compared to established methods. Long-term production dynamics for unconventional wells are unpredictable and current forecasting approaches have considerable limitations. Fitted production curve models are ill-posed and unreliable, but aggregated type-well curves ignore important differences between wells. This thesis introduces Tikhonov regularization as a way of effectively sharing information across wells, cutting error in the earliest long-term productivity forecasts in half. Additionally, a spatiotemporal hierarchical Bayesian approach is developed that incorporates physical relationships to enhance predictions and interpretability while quantifying and reducing uncertainty. Sampling from this high dimensional model is enabled by designing a unique Metropolis-Hastings within Gibbs scheme to take advantage of the model's structure. This novel mechanistic-statistical approach is able to learn and generalize physical relationships across ensembles of wells with vastly different properties--realistic scenarios where current techniques generate two to five times as much error--providing an important and practical advance in better understanding and managing these resources.