Combining data, concepts and models to create actionable insights chasing remaining reserves in a mature oil field

Reservoir simulation models are essential for predicting reservoir behavior and managing production. These models are relying on a variety of input parameters. Some of these parameters are measured in wells, others inferred from geological, historical, and seismic data. Ensemble modeling helps maintain uncertainties in property and production forecasting. This implies that a reservoir model is realized by up to several hundred of realizations. Each realization is used for predictions of well performance and forecasts, resulting in a range of outcome which reflects the uncertainties in the input parameter sets.4D seismic data has proved to be useful in identifying drilling opportunities and optimizing production, while integrating 4D seismic attributes into reservoir models to include dynamic data beyond well production remains complex.The oil and gas industry's digital shift promises more efficient cross-discipline interactions and automation, allowing for targeted studies to assess investment premises and recovery opportunities. A Grane field case study, is used to exemplify how a small team, utilizing cloud-based tools and 4D seismic data to conduct a rapid analysis to assess reservoir models and to identify well targets. The intention is to demonstrate the benefits of automation, access to computing resources and the need for effective interdisciplinary collaboration in reservoir modelling studies.