Adding geologic prior knowledge to Bayesian lithofluid facies estimation from seismic data

Abstract

Using inverted seismic data from a turbidite depositional environment, we have determined that accounting only for rock types sampled at the wells can lead to biased predictions of the reservoir fluids. The seismic data consisted of two volumes resulting from a (multi-incidence angle) sparse-spike amplitude variation with offset inversion. Information from a single well (well logs and petrological analysis) was used to define an initial set of lithofluid facies that characterized rock type and porefill fluid to emulate a typical exploration setting. Based on our geologic understanding of the study area, we have augmented this initial model with lithofluid facies expected in the given depositional environment, yet not sampled by the well. Specifically, the new lithofluid facies accounted for variations in the mixture type and proportions of shales and sands. The elastic property distributions of the new lithofluid facies were modeled using appropriate rock-physics models. Finally, a geologically consistent, spatially variant, prior probability of lithofluid facies occurrence was combined with the data likelihood to yield a Bayesian estimation of the lithofluid facies probability at every sample of the inverted seismic data. Applying the augmented geologic prior probabilities, we were able to generate a scenario consistent with all available data, which supports further development of the field. In contrast, using the initial, purely data-driven lithofluid facies model based on a single well, the Bayesian classification would lead to prospectivity downgrade or suboptimal development of the field. We found that limited well control in quantitative interpretation needs to be counterweighted by geologic prior information based on detailed stratigraphic interpretation, to derisk geologic scenarios without bias.

Stochastic inversion of seismic data by implementing image quilting to build a lithofacies model for reservoir characterization of Delhi Field, LA

“…In more reliable and realistic geomodeling, many different sources of uncertainty have to be considered such as proportion of facies, size of the geobodies, stacking pattern, rock physics relations, rock-fluid type and so forth. Stochastic seismic inversion is the approach that facilitates implementation and addressing of previously mentioned sources of uncertainty (Gonzalez et al, 2016).…”

Prestack inversion for porosity, shale volume, and sand probability in the Havert Formation of the Goliat Field, SW Barents Sea

“…The application of Bayes' theorem in seismic reservoir characterization makes it possible to assign probabilities to any existing knowledge (prior probability), which is used to constrain new evidence. Ezequiel et al (2016) describe the upside of adding existing geologic knowledge about the area under investigation to constrain the Bayesian facies classification. The new evidence is commonly the output from seismic inversion such as AI and V P ∕V S .…”

Prestack inversion and multiattribute analysis for porosity, shale volume, and sand probability in the Havert Formation of the Goliat field, southwest Barents Sea

“…Ezequiel et al (2016) describe the upside of adding existing geologic knowledge about the area under investigation to constrain the Bayesian facies classification. The new evidence is commonly the output from seismic inversion such as AI and V P ∕V S .…”