A probability model to detect carbonate cementation in sandstones and other enigmatic wireline facies

• Published in: Marine and Petroleum Geology
• Main Author: Bunch, M.A.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2020
• Subjects: Siliciclastic succession; carbonate cement; wireline logging; probability; statistical modelling; machine learning; igneous intrusions; Carbonic acid
• Online Access: http://hdl.handle.net/2440/126755; https://doi.org/10.1016/j.marpetgeo.2020.104424

Zones of carbonate cementation identified within lower Paaratte Formation sandstones of the eastern Otway Basin, Victoria, southeastern Australia, can be quantitatively detected by a wireline log-based probability model. Though trained on these zones, the same model appears to accurately predict carbonate cementation within Late Cretaceous-to-Eocene reservoir sandstones of the Latrobe Group supersequence of the Gippsland Basin, 100s of km to the east. Predicted carbonate cementation matches published evidence (a regional petrographic study) and provides a plausible interpretation for corresponding sections of Formation Micro-Imager data acquired more recently. These Latrobe Group sandstones are thought to have once been pervasively cemented prior to development of the secondary porosity responsible for providing reservoirs to the main petroleum system of the basin. However, model predictions imply that discrete, heavily cemented zones remain, which must have acted, and must still act, as local obstructions to reservoir fluid migration. These zones would also likely react with carbonic acid formed at plume fronts generated by dedicated CO2 storage operations of the future. The cemented zones that have been predicted are sparse, spatially sporadic and cannot be resolved by 3D seismic reflection survey data at present-day reservoir depths. These predictions therefore emphasise the difficulty in mapping the distribution of carbonate cemented zones. However, they provide calibration data for future mapping systems. Two different tests of the probability log model were undertaken. One benchmarks its predictions against those of a neural network trained using the original model development dataset from the Otway Basin. Predictive performance of the neural network model is significantly worse than that of the probability log model when making predictions using wireline data acquired at a well in the Gippsland Basin. The second test demonstrates that the general probability log modelling approach is amenable to ...