This is a Hybrid Event
15375 Memorial Dr.
Houston, TX 77079
Meeting Time: 11:30 to 1:00
Registration Begins at 11:30
Lunch Served at 11:30
Presentation starts at Noon
NOTE: You Must Be Logged In to Register.
Speaker: Alena Grechishnikova, PhD, Earth Science Advisor, Chevron North America Exploration & Production
The continuous horizontal development of shale plays frequently results in newer child wells being placed in the relative proximity to older parent wells, ultimately generating Frac Hits, or FDIs (Fracture Driven Interactions), impacting production of both child and parent wells. FDIs are becoming a major operational challenge, resulting in hundreds of thousands of lost barrels of production annually. Prevention and mitigation of FDIs calls for accurate account and prediction of FDI impacts. This study is focusing on the Permian Basin to predict production losses in parent wells.
The patent-pending innovation FREYA (FDI Recognition and Elimination Yielding Analysis) is deployed in the Permian Basin to predict the probability, duration, and loss production outcome (LPO) of FDI events using subsurface machine learning. FREYA is designed to enable the design of timely frac-preparation action plans for the operations, reduce LPO impact and identify potential hazards for facilities, such as leaks. Moreover, at the stage of well planning and development strategy design infill well opportunities can be optimized to reduce the impact of FDIs before drilling. FREYA can predict frac hit probability, duration, or LPO from a selected list of predictor variables, both geological and engineering based. AI models are being deployed to refine the drilling campaign and operational practices to minimize production losses. Various strategies may be employed to address the FDI impact, starting with optimized development sequence and timing, well spacing, completion size, as well as switching out the artificial lift type to help offload the water and minimize the duration of LPO. The multidisciplinary approach and data integration through machine learning enables the identification of the most critical features impacting the probability and severity of FDIs, such as distance, depletion, size of the child completion, carbonate content, and structural complexity. Overall, this approach promotes the advancement from reactive and qualitative FDI assessment to quantitative, predictive, and standardized analysis allowing to generate area specific mitigation and prevention strategies across the portfolio.
Speaker Biography: Alena Grechishnikova, PhD, Chevron North America Exploration & Production
Alena is a geoscience professional with over 15 years of domestic and international industry experience as geophysical engineer and advisor, including project management and integrated geology & geophysics for exploration, reservoir characterization, and modeling. She earned both her B.S. and M.S. in Geophysical Engineering from Gubkin Russian State University of Oil and Gas, Moscow, Russia. Alena also holds a Ph.D. in Geophysical Engineering from Colorado School of Mines where she developed and taught academic and industry courses and field trips designed to help integrate multidisciplinary teams of geoscience professionals focused on unconventional reservoir characterization and specifically naturally fractured systems analysis and modeling. Alena then proceeded with a post-doc from University of Utah where she was involved in carbon sequestration research. During her professional career Alena worked for Gazprom, Schlumberger, ExxonMobil, Ursus E&P, and Chevron starting with seismic interpretation and inversion of fluvial reservoirs and proceeding with unconventional reservoir modeling and well planning. Most recently Alena’s focus has been concentrated on unconventional reservoir strategy framing for Frac Hit risk assessment, EOR treatments, and production optimization utilizing machine learning (ML) and artificial intelligence (AI).
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