Updating joint uncertainty in trend and depositional scenario for reservoir exploration and early appraisalr

Computationally efficient updating of reservoir models with new production data has received considerable attention recently. In this paper however, we focus on the challenges of updating reservoir models prior to production, in particular when new exploration wells are drilled. At this stage, uncertainty in the depositional model is highly impactful in terms of risk and decision making. Mathematically, such uncertainty is often decomposed into uncertainty of lithological trends in facies proportions which is typically informed by seismic data, and sub-seismic variability often modeled geostatistically by means of training images. While uncertainty in the training image has received considerable attention, uncertainty in the trend/facies proportion receives little to no consideration. In many practical applications, with either poor geophysical data or little well information, the trend is often as uncertain as the training image, yet is often fixed, leading to unrealistic uncertainty models. The problem is addressed through a hierarchical model of probability. Total model uncertainty is divided into first uncertainty in the training image, then uncertainty in the trend given the uncertain training image. Our methodology relies on an efficient Bayesian updating of these model parameters (trend and training image) by modeling forward-simulated well facies profiles in low-dimensional metric space. We apply this methodology to a real field case study involving wells drilled sequentially in the subsurface, where as more data becomes available, uncertainty in both training image and trend require updating to improve characterization of the facies.