[CCoE Notice] Dissertation Announcement: Mohamed Adel Gabry, "Applications of Continuous Wavelet Transform in Hydraulic Fracturing and Reservoir Management"

[Greenwell, Stephen J]

[Dissertation Defense Announcement at the Cullen College of Engineering]
Applications of Continuous Wavelet Transform in Hydraulic Fracturing and Reservoir Management

Mohamed Adel Gabry
November 14th, 2024; 9:00 AM - 12:00 AM (CST)
Location: Room 123 Petroleum Eng. Bldg. 9

Committee Chair:
Mohamed Y. Soliman, Ph.D.

Committee Members:
S.M. Farouq Ali, Ph.D. | Ganesh Thakur, Ph.D. | Birol Dindoruk, Ph.D. | Ahmed Alzahabi, Ph.D.
Abstract
The Continuous Wavelet Transform (CWT) is an advanced signal processing technique used to analyze complex physical systems. It addresses key aspects of hydraulic fracturing diagnostics, including fracture closure identification, dynamic fracture event detection, microseismic event prediction, water hammer modeling, and inter-well connectivity for optimizing water flooding operations. For fracture closure analysis, a novel CWT-based method is introduced to detect fracture closure pressure, where pressure fall-off signals are decomposed to allow precise event identification. This approach is validated through simulation and field data, reducing reliance on reservoir geomechanical parameters and supported by physical measurements like strain gauges. CWT is also applied to dynamic fracture event detection, where normalized scalograms are used to predict microseismic events associated with hydraulic fractures. When integrated with machine learning, this method enhances hydraulic fracturing modeling and operational efficiency. Furthermore, CWT is utilized in water hammer modeling by treating water hammers as damped harmonic oscillators, allowing for the analysis of post-fracture treatment signals. This automates the evaluation of induced fracture complexity by correlating damping ratios with fracture intensity logs. In reservoir management, Cross Wavelet Transform Coherence (CrWTC) is employed to map inter-well connectivity (IWC) between injectors and producers, thereby optimizing waterflooding operations. CrWTC offers a detailed analysis of injection and production rate data, surpassing traditional statistical methods. This technique is validated through simulations and field datasets, improving the reliability of IWC assessments and supporting enhanced oil recovery (EOR) strategies. Overall, this research confirms CWT's effectiveness in fracture diagnostics and reservoir management, proposing innovative methods that integrate with machine learning for real-time decision-making in hydraulic fracturing and EOR operations.


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