Abstract Repurposing of oil and gas wells for geothermal energy involves several critical steps, including assessing the well’s structural integrity, evaluating the reservoir’s thermal properties, and modeling the potential energy output. The development of geothermal systems is naturally uncertain due to sub-state conditions, thermal conductivity, and inequalities in fluid flow behavior. Reliable performance forecasting and optimal well design depend on this rigorous modeling and simulation. As a result, global interest in sustainable energy has accelerated the use of abandoned oil and gas wells as viable resources for geothermal energy production. This study investigates the feasibility of repurposing abandoned oil wells in the Volve oil field in the North Sea as geothermal energy sources. This research shows the effects of petrophysical properties (i.e., permeability and porosity) on enthalpy production derived from reservoir modeling, and examines the uncertainty in these properties to achieve more accurate reservoir characterization and modeling. The following methodological framework was employed: First, reliable and coherent three-dimensional models of petrophysical properties were developed using geostatistical sequential simulations to improve well log data and spatial patterns as revealed by spatial covariances and variograms. Next, these models were updated to align with historical production data. After validation, the models were utilized to forecast enthalpy production. Based on the results, the Volve reservoir can produce an average of 2,094 MWh of geothermal energy each year, yielding a total energy output of about 41,877 MWh over a 20-year operating period.

Abstract Structural geological modeling is aimed at finding a representation of geological units. This is a complex ill-posed problem, and the data may be sparse and of varying quality, leading to multiple geological models consistent with them. Despite continuous advances for decades in geological modeling, recent studies still show some unresolved industrial challenges. Consequently, this work aims to improve geological modeling by proposing a novel approach with a focus on improving uncertainty management. A stochastic approach is developed based on deep generative methods, namely generative adversarial networks (GANs). Thanks to a synthetic dataset, a GAN is trained to generate two-dimensional unconditional geomodels that are plausible. Subsequently, a Bayesian inversion is performed with a Metropolis-adjusted Langevin algorithm (MALA) to produce geomodels consistent with field data. The proposed approach is validated on different conditioning data. For each case, the approach is able to successfully produce a variety of geomodels that can be linked to different geological settings. The uncertainties on geological units are measured by Shannon entropy on the generated models.