Summary In recent years seismic inversion techniques have been developed using the Bayesian statistical framework to lower the uncertainty of an initial belief by observing (relevant) data. We build on this and seek to estimate a posterior probability model of different lithology and fluid classes (LFCs) and their porosity distribution in the subsurface, based on a prior probability model and the seismic data. In settings with well-known geology, better predictions can be obtained by dividing the reservoir rock body into several distinct LFCs representing different rock properties like for example sand of high or low porosity. However, in an exploration setting such distinctions may be time consuming and unfeasible due to lack of information. Instead, we would prefer an inversion that could predict porosity from a unified LFC covering all types of sands in the reservoir. In this abstract we demonstrate that the latter approach may produce results of similar quality as the model with distinct LFCs. Our focus is to invert for porosity to map the distribution of reservoir quality in a northern North Sea reservoir. The results are promising and indicate that a unified LFC model could work equally well for fast-track results and/or where data is limited.

Summary Sedimentary rocks often obey vertical transvers isotropy due to the nature of the sedimentation process or the inherent orientation of rock grain minerals (e.g. clay platelets). Despite several documented cases in the literature, the effect of anisotropy is often neglected in seismic AVO inversion. We study the effect of transverse isotropy in shale encasing isotropic sand using probabilistic AVO inversion. The inversion algorithm is inverting seismic pre-stack data to lithology and fluid probabilities. A synthetic case demonstrates how anisotropy in shale may lead to wrong interpretation of fluid content in underlying sand. A field case in the northern North Sea shows that accounting for anisotropy in shale may improve the discrimination between good and poor sand of an Oxfordian turbidite reservoir. The results are encouraging and consistent with observations of sandy intervals in the wells and the depositional system of the study area. Our results clearly demonstrate that transverse anisotropy in sediments may give a significant contribution to the AVO gradient that otherwise could be misinterpreted. In the study area it becomes especially important, since there is a lack of contrast in acoustic impedance between the encasing shale and the target sand.

SummaryWe present stochastic simulations of sub-seismic faults conditioned on displacement intensity and stress orientation maps generated from a geomechanical model. The simulations are performed in an iterative process where new faults are proposed and have a high probability of being accepted if the contribution of the new fault gives a better match with the input displacement intensity map. The algorithm is demonstrated on a synthetic test case and shows that displacement field and strike orientation of the simulated sub-seismic faults matches the pattern of the displacement intensity map and the orientation of the maximum horizontal stress from a geomechanical model.

Lithology and fluid prediction from seismic data is traditionally done in two steps; first an inversion of the seismic data to elastic parameters, and subsequently a prediction of lithology and fluid based on a rock physics model linking the elastic parameters to individual lithology and fluid combinations. Recently, a number of inversion algorithms have been developed that, based on Bayesian statistical methodology, estimate the probability of lithology and fluid directly from seismic data. In this paper we compare the performance of two state-of-the-art Bayesian inversion algorithms on a real data set from the Volund field in the North Sea. The first algorithm follows the traditional two-step approach and cannot take into account the stratigraphic ordering of lithology and fluid. The second algorithm, referred to as one-step, evaluates possible lithology and fluid combinations within a vertical window around each inversion point enabling correct stratigraphic ordering. We find that the one-step inversion resolves more details and honours the data more strongly than the two-step approach. The latter is more prone to return the prior model if information in the seismic data is not sufficiently strong. Both models detect hydrocarbon filled sand injectites that are typical for the field.