Ranking and selecting fault models using flow-indicator fault properties and simple streamline simulations

Abstract

Fault zones in porous sandstones are commonly divided into two parts: a fault core and a damage zone. Both fault-zone elements could influence subsurface fluid flow and should be incorporated in a geologically realistic model. The fault core can be implemented in the model as a transmissibility multiplier (TM), while the damage zone can be implemented by modifying the grid permeability in the cells adjacent to the model faults. Each of the input parameters used in calculating the TM and damage-zone permeability modification is subject to geological uncertainty. Here an iterative workflow is employed to define probability distribution functions for each of the input parameters, with the result being many fault-model realizations. Here two methods are examined for ranking and selecting the fault-model realizations for further analysis: (i) calculating the flow-indicator fault properties (effective cross-fault transmissibility and effective cross-fault permeability) from the static model; and (ii) employing a simplified flow-based connectivity calculation, returning dynamic measures of model connectivity. The aims are to outline the methodology and workflow used, evaluate the impact of the different input parameters on the results, and examine the results of the static and dynamic approaches to understand how the ranking and selection of models compares between the two. Our results are dependent on the structural model. In a strongly compartmentalized model based on the Gullfaks Field, North Sea, fluid-flow-indicator fault properties are weakly correlated with measures of dynamic behaviour. In particular, models with low fault transmissibility show a much greater range of dynamic behaviour, and are less predictable, than models with high fault transmissibility. In a weakly compartmentalized model with strongly channelized fluvial facies based on the Whitley Bay area in NE England, there was a strong correlation between flow-indicator fault properties and measures of dynamic behaviour. We ascribe these results to the greater complexity of flow paths expected when a highly compartmentalized model contains faults that are likely to be baffles to cross-fault flow. Thematic collection: This article is part of the Fault and top seals collection available at: https://www.lyellcollection.org/cc/fault-and-top-seals-2019