Quantifying Uncertainty in Downscaling of Seismic Data to High-Resolution 3-D Lithological Models

Building high-resolution lithological models using seismic data can facilitate decision-makings for earth resources development, but they are subject to considerable uncertainties due to the limited seismic resolution. Traditionally, high-resolution lithological models are built deterministically or stochastic simulation is performed using seismic as "soft data" in geostatistical contexts. Many approaches have been developed to do the above, but in this work, we explicitly account for the uncertain relationship between seismic data and lithological models. We introduce a data-driven Bayesian approach to first quantify soft data uncertainty by generating multiple lithology proportions from seismic. The spatial lithology proportion uncertainty arises from the uncertain relationship between low-resolution seismic and lithology proportions. We quantify this proportion uncertainty by learning the statistical relationships with seismic using high-resolution borehole data. Once conditioning the statistical relationships to observed seismic, we can generate multiple realizations of spatial proportions. With the generated proportions as volume averages, a sequential indicator simulation (SISim) is then performed to build high-resolution lithofacies models. This approach is applied to a real channelized turbidite system with four lithofacies. We demonstrate that the generated high-resolution lithological models can preserve the global uncertainties captured by proportion trends while locally matching borehole observations. When compared with the conventional approaches that use deterministic soft data, our statistical learning approach avoids the problem of underestimating reservoir model uncertainty. More importantly, the lithofacies models from the proposed method are less likely to be falsified by observed data. Additionally, an open-source Python library for the uncertainty quantification is provided.