Managing fracture driven interactions (FDIs) and real-time visibility into completions operations are two of the major topics of discussion in the industry right now. Being able to bring both of these together creates a powerful tool for operators looking to position themselves at the cutting edge of upstream technology. This webinar will describe the development and application of several unique / novel machine learning tools that can be deployed in real-time to better manage the challenges presented by FDIs.

One example that we will review is using machine learning to assist with active well defense scenarios. The Society of Petroleum Engineers (SPE) paper SPE-199735 outlined a collaboration between Well Data Labs and Abraxas. Since then, machine learning has been developed that can automatically identify offset pressure responses. This removes a major component of human error and analyst bias and also provides the ability to conduct much deeper analysis in a fraction of the time.

Another example that has been developed relies on alerts that notify an operator within seconds of pressure spikes that exceed specific thresholds. Using data honed from hundreds of thousands of stages, models built and trained to identify FDIs from these enormous data sets are vastly more capable of identifying troubling trends than the human eye. 

Taking this analysis from post-job to real-time creates the ability to transition much of the learnings and work that have been the realm of science experiments and move them into regular pad operations. We will review operational examples that we have worked with clients on, demonstrating how this technology and its application were validated. This allows the operator to benefit from insights and, in some cases, make adjustments on the fly.

Thank you Well Data Labs for sponsoring this webinar!

PRESENTER: Rair Barraez, Well Data Labs. 

Location: Zoom Webinar 
Date/Time Information: Tuesday, May 04, 2021 (from 10h00 to 11h00) – Mountain Time
For more Information and Registration visit CSUR Website (Limited availability)