Chevron: SuperMajor Shale in 2020?

SuperMajors’ shale developments are assumed to differ from E&Ps’ mainly in their scale and access to capital. Access to superior technologies is rarely discussed. But new evidence is emerging. This note assesses 40 of Chevron’s shale patents from 2019, showing a vast array of data-driven technologies, to optimize every aspect of shale.

Explaining Shale: Can Machine Learning Capture Complexity?

This data-file decomposes the drivers of shale productivity in Alberta’s Duvernay play, across a correlation-matrix of 23 different variables.


Machine learning can be used to predict 78% of the variance in wells’ performance from this data-set, surpassing the 19-67% predictive power of regression models (chart above). Accordingly, $1M/well savings are suggested, while well productivity can improve by 19-97%.

Shale is a data industry. “Big data” approaches are the only way to capture the complex inter-correlations within shale’s productivity drivers. As shown below, well EURs are meaningfully correlated with 12 variables. The “largest” driver is “proppant placed”, which is itself meaningfully correlated with 16 other variables.

Machine learning is still in its infancy in the shale patch, representing c2% of total industry-research. It presents material upside.

U.S. Shale: Winner Takes All?

Shale is a ‘tech’ industry. And the technology is improving at a remarkable pace. But Permian technology is improving faster than anywhere else. These are our conclusions after reviewing 300 technical papers from 2018. We address whether the Permian will therefore dominate future supply growth.

Copyright: Thunder Said Energy, 2022.