Machine learning-based uncertainty models for reservoir property prediction and forecasting
In subsurface data analytics and machine learning, advances enable new methods and workflows for spatio-temporal, geoscience, and engineering property estimation and forecasting. These advances allow new and detailed models that contribute to field development planning cycles, such as reservoir modeling, volumetric assessment, pre-drill uncertainty, and production allocation.
Uncertainty is caused by incomplete information and a lack of knowledge about continuous or discrete features. The presence of data uncertainty resulting from measurement errors, recording, data processing, sampling bias, and sample heterogeneity further exacerbates the need for reliable and interpretable uncertainty models, which are essential for effective subsurface prediction and forecasting. Therefore, developing robust, accurate, and precise models that provide the best possible estimates and account for the associated uncertainties is critical to improve decision-making.
This research aims to develop innovative workflows to validate uncertainty models, predict properties with uncertainty, and create interpretable forecasting models. This research presents the following subjects: (1) The evaluation of machine learning-based uncertainty models, (2) the use of a deep convolutional encoder-decoder network to generate ensemble predictions and evaluate the uncertainty based on development parameters and geological information, (3) the use of virtual ensembles in gradient-boosted decision trees for well-log imputation, and (4) model interpretability in well performance forecasting with temporal fusion transformers. The developed workflows offer a reliable and understandable approach to uncertainty models critical for subsurface resource modeling and forecasting.