Turning a Breakthrough Technology into a Scalable Process: Sealed Wellbore Pressure Monitoring

Iriarte, Jessica (Well Data Labs) | Hoda, Samid (Well Data Labs) | Guest, Ryan (Well Data Labs) | Van Domelen, Mary (Well Data Labs)


Abstract A breakthrough patent-pending pressure diagnostic technique using offset sealed wellbores as monitoring sources was introduced at the 2020 Hydraulic Fracturing Technology Conference. This technique quantifies various hydraulic fracture parameters using only a surface gauge mounted on the sealed wellbore(s). The initial concept, operational processes, and analysis techniques were developed and deployed by Devon Energy. By scaling and automating the process, Sealed Wellbore Pressure Monitoring (SWPM) is now available to the industry as a repeatable workflow that greatly reduces analysis time and improves visualizations to aid data interpretations. The authors successfully automated the SWPM analysis procedure using a cloud-based software platform designed to ingest, process, and analyze high-frequency hydraulic fracturing data. The minimum data for the analysis consists of the standard frac treatment data combined with the high-resolution pressure gauge data for each sealed wellbore. The team developed machine learning algorithms to identify the key events required by a sealed wellbore pressure analysis: the start, end, and magnitude of each pressure response detected in the sealed wellbore(s) while actively fracturing offset wells. The result is a rapid, repeatable SWPM analysis that minimizes individual interpretation biases. The primary deliverables from SWPM analyses are the Volumes to First Response (VFR) on a per stage basis. In many projects, multiple pressure responses within a single stage have been observed, which provides valuable insight into fracture network complexity and cluster/stage efficiency. Various methods are used to visualize and statistically analyze the data. A scalable process facilitates creating a statistical database for comparing completion designs that can be segmented by play, formation, or other geological variations. Completion designs can then be optimized based upon the observed well responses. With enough observations and based on certain spacings, probabilities of when to expect fracture interactions could be assigned for different plays.

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