Compositional and diagenetic evolution of a siltstone, with implications for reservoir quality; an example from the Lower Triassic Montney Formation in western Canada

Vaisblat, Noga; Harris, Nicholas B.; Ayrancı, Korhan; Power, Matthew; DeBhur, Chris; Bish, David L.; Chalaturnyk, Rick; Krause, Federico F.; Crombez, Vincent; Euzen, Tristan; Rohais, Sébastien

doi:10.1016/j.marpetgeo.2021.105066

Cited by 11 publications

(1 citation statement)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of these reasons, the deposited sediment predominantly consisted of quartz, chert, feldspars, dolomite, and mixed-layer I/S clays. For a long time, scientists disagreed on whether or not the Montney Formation contained any clay minerals in appreciable amounts [23]. Authigenic clay precipitation occurred in an intermediate stage, followed by quartz and feldspar dissolution and the second phase of carbonate precipitation [24].…”

Section: Geological Backgroundmentioning

confidence: 99%

Machine Learning Approach to Predict the Illite Weight Percent of Unconventional Reservoirs from Well-Log Data: An Example from Montney Formation, NE British Columbia, Canada

Barham,

Zainal Abidin

2023

Applied Sciences

View full text Add to dashboard Cite

Shale mineralogy is critical for the proper design and execution of hydraulic fracturing operations and for evaluating production potential. There has been relatively little research into using artificial intelligence for mineralogical prediction for the Montney Formation. This study aims to predict the Montney Formation illite wt.% using readily available conventional logs, where illite is one of the constituents of shale and can aid in analyzing the brittle and ductile zones within the shale formation. The wt.% of illite is often determined by examining core samples or cuttings using XRD or QEMSCAN; both techniques are time-consuming, costly, and cannot be performed without physical samples. Based on conventional log readings, this study uses artificial neural networks (ANNs) and principal component analysis (PCA) to construct an accurate prediction model for illite wt.%. The feed-forward neural network (FFNN) obtained good overall performance in illite wt.% prediction (R2 = 92%) utilizing the backpropagation algorithm and the B.R. technique for eight input parameters. The ANN model was tested by randomly selecting three wells from the same log dataset excluded from the core training and testing phases. Overall, R2 = 88.5% was found in the tests, which is encouraging. This work demonstrates the viability of employing the ANN in applications involving evaluating mineralogical components of a target source rock using traditional logs, especially when geochemical data are missing or inadequate.

show abstract

Section: Geological Backgroundmentioning

confidence: 99%