A probability model to detect carbonate cementation in sandstones and other enigmatic wireline facies. (August 2020)

• Record Type: Journal Article
• Authors: Bunch, Mark A.
• Is Part Of: Marine and petroleum geology. Volume 118(2020)
• Publication Date: 2020-08
• Subjects: Siliciclastic succession -- Carbonate cement -- Wireline logging -- Probability -- Statistical modelling -- Machine learning -- Igneous intrusions; Submarine geology -- Periodicals; Petroleum -- Geology -- Periodicals; Géologie sous-marine -- Périodiques; Pétrole -- Géologie -- Périodiques; Petroleum -- Geology; Submarine geology; Periodicals; Electronic journals; 551.468
• DOI: 10.1016/j.marpetgeo.2020.104424
• Languages: English
• ISSNs: 0264-8172

Abstract

Abstract: 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 classifying other cryptic 'electro-facies', for example those representing enigmatic or ambiguous rock types not easily interpreted from common wireline log data using standard theory. This test presents a case study by which the probability log modelling approach predicts the presence of both basic and evolved, felsic igneous sill intrusions within the stratigraphic succession of the Faroe-Shetland Basin of NW Europe. Highlights: A wireline log probability model can predict the presence of carbonate cementation in sandstones of the Otway Basin. Cementation can also be predicted in other sandstones in a different basin. The model automatically adapts to differing data baselines and ranges, and missing data streams. Predictions match core-calibrated interpretations of Formation Micro-Imager data. The model can be trained to report the probability of other ambiguous or undetectable rock units.