Using probabilistic shale smear modelling to relate SGR predictions of column height to fault-zone heterogeneity

Abstract

Fault-seal analysis in hydrocarbon exploration often involves prediction of the sealing capacity of fault rock at reservoir-reservoir juxtapositions on subsurface faults. A proxy property, such as Shale Gouge Ratio (SGR), is mapped on to the fault surface, and then SGR is either (a) calibrated by observations of known sealing faults, to define its sealing capacity (empirical approach), or (b) assumed to be equal to the composition of the fault rock, for which a database of capillary threshold pressures is available from cores (deterministic approach). The deterministic approach implicitly assumes that capillary pressures measured on centimetre-scale samples are representative of seismically mappable faults, for example that faults of intermediate SGR are equivalent to phyllosilicate framework fault rocks.This contribution builds on earlier outcrop and modelling work to suggest an alternative explanation for the observed progressive increase in sealing capacity on faults of increasing SGR. Stochastic models of disrupted shale smears display the same pattern of increasing sealing capacity as SGR increases. These models have a bimodal 'fault rock' composed only of sealing shale smears and non-sealing matrix and, yet, at intermediate SGR the predicted column heights are similar to those normally ascribed to intermediate composition fault rocks. The resulting 'fault-seal envelope' in the models is a statistical estimate of the maximum trappable column height, dependent on the random occurrence of a gap in the smeared fault surface. research-articlethematic set article: fault and top seals18110.1144/1354-079311-013G. YieldingProbabilistic shale smear modelling 2012

Uncertainty in fault seal parameters: implications for CO<sub>2</sub> column height retention and storage capacity in geological CO<sub>2</sub> storage projects

“…The resulting column heights also follow a probability distribution (table 2). The results of the MCMO models highlight the differences between the three approaches that link FRCC to fault rock threshold pressure with the approach of Sperrevik et al (2002) generally resulting in lower column heights the approaches of Bretan et al (2003) and Yielding (2012) for both Reservoir A and B (Tab. 2,Figs.…”

“…The SGR algorithm, similar to other algorithms like the Shale Smear Factor (Lindsay et al, 1993), the Clay Smear Potential (Fulljames et al, 1997) or the Probabilistic Shale Smear Factor (Childs et al, 2007) which all use a combination of throw clay bed distribution or thickness to predict the effects of clay smears, do not consider the detailed fault rock distribution and fault zone complexity observed on outcrops or at the centimetre, and sub-centimetre scale (Faulkner et al, 2010;Schmatz et al, 2010). It has however been successfully used during the last two decades to predict hydrocarbon fault seals in the subsurface (Manzocchi et al, 2010;Yielding, 2012).…”

“…For reservoir A, the models which are used to investigate the impact of uncertainties in wettability (Wet1-Wet3) have column heights ranging from 14.8±0.9 m to 14.6±3.6 m (after Sperrevik et al, 2002), and from 73±4 m to 72±18 m (after Bretan et al, 2003), and from 111±6 m to 110±27 m (after Yielding, 2012). Models which simulate uncertainties in FRCC in the same reservoir have column heights ranging from 16±7 m, and from 74±14 m to 95±80 m, and from 111±14 m to 111±55 m, for the three different approaches respectively.…”

Actors, actions and uncertainties: Optimizing decision making based on 3-D structural geological models

“…Once both of these points have been determined, the higher one is defined to be the final spill point used to determine the maximum fill capacity of the trap. Given a juxtapostion with layers overlying the seal, due to fault displacement, the respective section is checked for fault sealing by taking into account the Shale Smear Factor (SSF) value which is the ratio of fault throw magnitude D to displaced shale thickness T (Lindsay et al, 1993;Yielding et al, 1997;Yielding, 2012):…”

Actors, actions, and uncertainties: optimizing decision-making based on 3-D structural geological models

“…Once both of these points have been determined, the higher one is defined to be the final spill point used to determine the maximum fill capacity of the trap. Given a juxtaposition with layers overlying the seal, due to fault displacement, the respective section is checked for fault sealing by taking into account the shale smear factor (SSF) value, which is the ratio of fault throw magnitude D to displaced shale thickness T (Lindsay et al, 1993;Yielding et al, 1997;Yielding, 2012):…”