Applications of Continuous Wavelet Transform in Hydraulic Fracturing and Reservoir Management

This dissertation explores the application of Continuous Wavelet Transform (CWT) in fracture diagnostics and reservoir management, offering an in-depth analysis of various methods and their effectiveness. 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 waterflooding operations. In fracture closure analysis, the study reviews established methods like the Nolte, tangent, and compliance methods, introducing an innovative approach that integrates these techniques with fluid flow equations and mathematical models. A novel CWT-based method is proposed for detecting fracture closure pressure, where pressure fall-off signals are decomposed to enable precise event identification. This approach is validated through simulation and field data, minimizing reliance on reservoir geomechanical parameters and supported by physical measurements such as strain gauges. The application of CWT extends to dynamic fracture event detection, where normalized scalograms effectively predict microseismic events associated with hydraulic fractures. Integrated with machine learning, this method enhances hydraulic fracturing modelling and improves hydraulic fracturing operations. Additionally, CWT is applied to water hammer modeling, treating water hammers as damped harmonic oscillators to analyze post-fracture treatment signals. This approach automates the induced fracture complexity evaluation by correlating damping ratios with fracture intensity log. In reservoir management, Cross Wavelet Transform Coherence (CrWTC) is used to map inter-well connectivity (IWC) between injectors and producers, optimizing waterflooding operations. CrWTC provides a detailed analysis of injection and production rate data, surpassing traditional statistical methods. The technique is validated through simulations and field datasets, improving the reliability of IWC assessments and contributing to enhanced oil recovery (EOR) strategies. Overall, the dissertation validates CWT's effectiveness in fracture diagnostics and reservoir management, proposing innovative methods that can be integrated with machine learning for real-time decision-making in hydraulic fracturing and Enhanced Oil Recovery operations.

Name thesis:degree_name

Doctor of Philosophy

Discipline thesis:degree_discipline

Petroleum Engineering

Grantor

University of Houston

Year dc:date.issued

2024

Author dc:creator

• Gabry Abdelsalam, Mohamed Adel 1988-

Advisor dc:contributor.advisor

• Soliman, Mohamed Y.

Committee members dc:contributor.committeemember

• Alzahabi, Ahmed
• Thakur, Ganesh
• Dindoruk, Birol
• Ali, S.M. Farouq

Language dc:language.iso

English

Handle dc:identifier.uri

https://hdl.handle.net/10657/18281

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/18281