Abstract: A method for determining and implementing a data collection program is disclosed. Data is typically collected in order to develop a subsurface model that can characterize a subsurface to assist in hydrocarbon management. However, it may be difficult to determine how much, or what type of data, to obtain so that the subsurface model is of sufficient certainty. In particular, parameters that define the model and outputs of the model (defined as quantities of interest (QoIs)) are subject to uncertainty. In order to reduce the uncertainty of the QoIs to an acceptable level, data collection programs are iteratively selected based on sequential subsurface uncertainty characterization. In this way, the data collection programs, when implemented, may collect a sufficient amount of data to reduce uncertainty of the subsurface model for subsequent use in hydrocarbon management.

Abstract: A hierarchical conditioning methodology for building and conditioning a geological model is disclosed. In particular, the hierarchical conditioning may include separate levels of conditioning of template instances using larger-scale data (such as conditioning using large-scale data and conditioning using medium-scale data) and using smaller-scale data (such as fine-scale data). Further, one or more templates, to be instantiated to generate the geological bodies in the model, may be selected from currently available templates and/or machine-learned templates. For example, the templates may be generated using unsupervised or supervised learning to re-parameterize the functional form parameters, or may be generated using statistical generative modeling.