This presentation expands the workflow that Kreman et al., (2018) proposed, developing type wells across the basin that capture the regional performance variability.

The integrated machine learning approach produced results that were consistent with the analog approach, but less labor-intensive and leveraged digital technologies to optimize field development.

Given the complexity of unconventional plays, it is critical to incorporate learnings from various data sources. The proposed integrated workflow helps constrain the uncertainty parameters to allow informed business decisions and optimized full field plan of development.

Workflows developed as part of the second approach (proposed ML generated GSAs) can be used for most phases of development. Because the data analytics driven approach matched well with our manual approach, this method can be automated to constantly evolve with additional data as constraints.