Assessment of formation strength from geophysical well logs using neural networks
Document Type
Conference Proceeding
Date of Original Version
12-28-2006
Abstract
This paper presents the methodologies and results of two types of neural networks used to estimate the unconfined compressive strength (UCS) of weakly cemented sandstones from geophysical log data. The first neural network used 29 different logs as input and predicted UCS values at 8 cm depth resolution that were in good agreement with measured values. The second neural network used an innovative approach to improve the depth resolution and detection of thin bedding by relating changes in high resolution logs (e.g. bulk density or resistivity) to changes in strength. Preliminary analyses used to predict the undrained shear strength of marine clay illustrate the potential of this new approach for predicting the strength of weakly cemented sandstones. Copyright ASCE 2006.
Publication Title, e.g., Journal
GeoCongress 2006: Geotechnical Engineering in the Information Technology Age
Volume
2006
Citation/Publisher Attribution
Ressler, Jason E., Christopher D. Baxter, Kathryn Moran, Meghan Paulson, Ion Ispas, and Hans Vaziri. "Assessment of formation strength from geophysical well logs using neural networks." GeoCongress 2006: Geotechnical Engineering in the Information Technology Age 2006, (2006): 129. doi: 10.1061/40803(187)129.