Integrating Analytics and Machine Learning for Prediction of Mechanical Properties

What is it about?

Known for its highly anisotropic character, the Tuscaloosa Marine Shale (TMS) is an economically challenged formation in need of improved stress estimates. To avoid steep declines in production, hydraulic fracturing is used to release the hydrocarbons trapped in the matrix of the shale. Hydraulic fracture propagation and the overall hydraulic fracturing process depend on an accurate estimation of Young's modulus and Poisson's ratio. Moreover, to predict stress in horizontal laminated shales, several vertical transverse isotropic (VTI) models were proposed since ANNIE. However, such analytical methods require extensive calculations and knowledge of dynamic-to-static ratios when estimating mechanical properties from well logs. Machine learning (ML) can be applied to generate geomechanical synthetic logs and therefore, may represent an alternative to the current anisotropic techniques. In this paper, the Gradient Boosting algorithm is used to estimate the static properties of the Tuscaloosa Marine Shale.

Featured Image

Photo by Markus Winkler on Unsplash

Photo by Markus Winkler on Unsplash

Why is it important?

Machine learning eliminates all tedious steps associated with the calculation of stiffness coefficients and the need to convert dynamic properties to static. The presented ML model can be run in most well log analysis platforms by anyone who disposes of a complete suite of sonic logs and seeks to better understand the mechanical properties of other TMS wells. This work creates a bridge between analytical methods and machine learning. Both petrophysical and geomechanical input was necessary, while results can further aid completion engineers in selecting the best intervals for hydraulic fracturing.