
TY  - JOUR
A1  - Smalley, P.C. 
A1  - Jablonski, D. 
A1  - Simpson, I. 
T1  - A PROCESS FOR PREDICTING POROSITY AND PERMEABILITY IN DEEP EARLY JURASSIC/TRIASSIC TARGETS, AUSTRALIAN NORTH WEST SHELF
JF  - The APPEA Journal
JO  - The APPEA Journal
Y1  - 1998
VL  - 38
IS  - 1
SP  - 759
EP  - 775
KW  - 
UR  - https://doi.org/10.1071/AJ97050 
N2  - In deep exploration prospects, reservoir quality is often a key risk. We describe a hybrid empirical-theoretical approach to minimise this risk:Use available regional petrographic-sedimentological data to tune theoretical depth-porosity-permeability curves.Verify that the model correctly represents the controlling geological processes by comparing these curves to core analysis data.Re-tune the model to the expected conditions in the deep prospects, using empirical quartz cementation predictions, regional depositional models and pressure prognoses from basin modelling.Use the re-tuned model to extrapolate porosity to the new depth, then predicting permeability from porosity.Eight studied wells in the Early Jurassic/Triassic, Dampier Sub-basin, provided an understanding of regional diagenetic style and the major reservoir quality controls. BP&apos;s PermPredictor model was used to construct regional, zone-specific depth-porosity-permeability curves from the petrographic and sedimentological data: sand ductile grain content, grain size, sorting and quartz cementation. Quartz cement correlates with burial depth, beginning at ~2,200 m and increasing by seven per cent (&#xb1; two per cent) per km.The regional modelled depth-porosity-permeability relations agree well with the core analysis dataset, indicating model reliability. The modelled curves were then re-tuned to the predicted conditions in two notional exploration prospects, with top-structure depths of 4.7 km (Prospect 1) and 4.4 km (Prospect 2), the latter of these being overpressured. Predicted porosities were 5&#x2212;11 per cent in Prospect 1 and ll&#x2212;17per cent in Prospect 2, with permeabilities of 30&#x2212;250 mD and 400&#x2212;1,000 mD respectively assuming a clean sand composition. A dirty sand model (less likely) predicts 
ER  -

