Rule-based and machine learning hybrid reservoir modeling for improved forecasting

Reservoir characterization becomes challenging in deepwater depositional systems where high exploration costs and complicated geological structures often limit data collection. Rule-based geostatistical subsurface modeling can overcome this data-gap challenge and produce geological representations for these reservoirs using geological rules constrained by even sparse data sets. The rule-based model simulates sediment dynamics through depositional rules in generating reservoir architecture and the associated rock properties distributions. As a result, rule-based models integrate conceptual information, including temporal deposition sequence and consequent compensational stacking patterns. However, selecting realistic rule parameters (e.g., stacking patterns and geometry of depositional elements) and integrating quantitative data (e.g., well logs and fluid production history) remain as obstacles to the broad application of rule-based subsurface models. In this research, I develop a robust rule-based modeling method for deepwater reservoir and machine learning-assisted data conditioning methods for various data (i.e., well data, stratigraphy, and production history) in the rule-based models. In this regard, I study the following subjects: (1) investigation of the realistic rule-parameters and removal of the numerical artifacts by comparing the models with geological observation, (2) solution to the long-standing conditioning challenge of rule-based models through the novel application of machine learning approach, and (3) examination of machine learning applications for a superior model inference model in standard geostatistical methods. The developed workflow enables reservoir characterization in deepwater reservoirs by reproducing their realistic geological heterogeneity and integrating various observed data into the models, resulting in accurate production forecasts. Moreover, the flexibility of the workflow can broaden its applications to different depositional settings, such as fluvial or deltaic reservoirs.Petroleum and Geosystems Engineerin