EFFICIENT AND ECONOMIC HYDRAULIC FRACTURE DIAGNOSTICS USING METAMATERIALS AND EDGE AI
The chief objective of this effort was to develop improved methods of subsurface imaging using metamaterials, seismic detection, electromagnetic detection, and edge artificial intelligence. These methods are applicable to downhole evaluation related to hydraulic fracturing operations for unconventional oil and gas, gas storage, and geothermal wells. Poor understanding of proppant placement and fracture geometry leads to hazards for environmental and human health, poor system design, and inefficient energy production. Proppants, used to create more permeable fissures, can provide increased understanding of subsurface conditions and operational effectiveness. Increased information regarding completed fracture characteristics and proppant usage will benefit the energy industry in general. The goal of the project is to create a system for real-time propped fracture characterization that increases knowledge of fracture design and well operation. The goals of this study were achieved through material design and production, laboratory seismic and electromagnetic testing, machine learning algorithm development, and field trial planning. Through Phase I of this project, Oceanit produced and tested metamaterials as smart proppant particles and developed machine learning algorithms for their electromagnetic and acoustic detection. The materials provide increased contrast for EM and acoustic characterization with potential for long distance measurements. Edge computing was implemented with metamaterial detection algorithms and inversion methods in order to provide near real-time interrogation. The result of this Phase I effort is an inexpensive subsurface forensic system based on existing well construction technologies and electromagnetic and seismic interrogation methods. Oceanit produced and characterized metamaterial proppant additives for electromagnetic and seismic detection along with machine learning systems that identify proppant location and environmental conditions. These efforts will lead to a diagnostic technology for detecting proppant location and fracture geometry for incorporation in reservoir and fracture modeling, well performance and operations, along with well design and construction. Applications The smart proppant additive and edge computing capabilities developed in this project can be applied to a wide variety of subsurface energy technologies, including unconventional oil and gas, carbon storage, induced seismicity monitoring, and geothermal power generation. Increased understanding of subsurface conditions will lead to better designs for well construction and operation. Knowledge obtained from this technology will improve fracture and reservoir modeling along with fluid flow determination.
- Research Organization:
- Oceanit Laboratories
- Sponsoring Organization:
- USDOE Office of Fossil Energy (FE)
- Contributing Organization:
- University of Houston: Dr. Jiefu Chen
- DOE Contract Number:
- SC0021959
- OSTI ID:
- 1875300
- Type / Phase:
- SBIR (Phase I)
- Report Number(s):
- DE- SC0021959
- Country of Publication:
- United States
- Language:
- English
Similar Records
Metamaterials for Geothermal Well Integrity Monitoring DOE Phase I Final Report
High-Resolution Fiber-Optic Sensing System for 4D Mapping, Detection, and Characterization, and Integrity Assessment of Legacy Wellbores for Carbon Storage