A Machine Learning Approach for Predicting Rock Brittleness from Conventional Well Logs
- Jodel Cornelio (University of Southern California) | Iraj Ershaghi (University of Southern California)
- Document ID
- Society of Petroleum Engineers
- SPE Eastern Regional Meeting, 15-17 October, Charleston, West Virginia, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- machine learning, well logs, geomechanical, brittleness
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- 92 since 2007
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In modern logging practices, the Poisson ratio and the Young Modulus as measures of rock Brittleness may be estimated from the dipole sonic and bulk density logs. This is important when subterranean formations are considered for fracturing or when unintended fracturing can be a concern under high injection gradients in fluid injection processes. For most oilfields, the bulk of well log suites run in the past has been limited to basic lithology logs and occasionally some porosity logs. In particular, the sheer sonic velocity log, an important component for estimation of geomechanical properties, has not been a standard measurement on common log suits.
In this paper, we present the result of a study where shear travel time is correlated with measurements from caliper and shale content. The training set we used consisted of well logs for wells that included the shear travel time. We experimented with various approaches and developed a process for in cooperating DNN (deep neural network) to correlate the shear log data to other measurements.
|File Size||18 MB||Number of Pages||38|
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