Probabilistic Uncertainty Modelling to De-Risk Fault Seal Analysis

Abstract

Construction of a geological model for fault seal analysis requires interpretation of fault and horizon surfaces, and definition of the relationship between these features (i.e. fault cut-off lines). The resultant model can be populated with lithological information, typically derived from interpreted well logs, allowing fault sealing capacity to be estimated. In each case, several factors, including the limits of data resolution and human bias, will result in errors in interpretation that will affect the accuracy of subsequent fault seal prediction.

Here we describe a new, rapidly-applicable, software tool which stochastically models cut-off line position and percentage shale content (Vsh), allowing the uncertainty in fault displacement and seal capacity to be quantified. The method uses a Monte Carlo simulation to randomly sample normally distributed variations of the input data. Uncertainty in the structural model is incorporated by applying a deviation value to the elevation of cut-off lines on fault surfaces. Data quality is often poorer at depth due to signal attenuation and depth conversion errors. Variations in data confidence with depth are accounted for by using a depth dependent deviation. Modelling shale percentage uncertainty is performed by defining a deviation value for the Vsh curve resulting in variations in the proportion of shale at each point in the stratigraphic sequence.

The results are presented as a map of probability of an across-fault lithological juxtaposition or fault sealing proxy value (e.g. shale gouge ratio). The outputs are displayed as an along-strike projection of the fault, colour-mapped for the mean, mode or a percentile (e.g. P10, P50, P90). Moreover, the results present the likelihood of an outcome to occur, allowing risk to be quantified. This is demonstrated using real-world models from hydrocarbon basins around the world, including offshore Canada and New Zealand.

AAPG Datapages/Search and Discovery Article #90323 ©2018 AAPG Annual Convention and Exhibition, Salt Lake City, Utah, May 20-23, 2018