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The ultimate goal of our work is to develop an electromagnetic multi-frequency integrated system to enable real-time and in-situ characterization during high power electromagnetic (HPEM) operations, e.g. logging while lasing (LWL) or logging while microwaving (LWW). The sensing platform should provide information about the environment, the substrate, the near-wellbore (ahead of the beam), and the process. Real-time sensing is necessary because HPEM operations are faster and avail more control to target the electromagnetic energy to attain a specific purpose. Thus, the sensing systems must be compatible and operate simultaneously with HPEM radiation. In this context, electromagnetic multi-frequency characterization could provide valuable information to evaluate the near-wellbore volume, fluids, and the environment. This paper introduces essential spectrometry components and analytics. The experiments reported focus on the characterization of tight-sandstones rocks from different reservoirs. The characterization process used various tools: microwave and radio-frequency resonators, Fourier-transform infrared reflectometry (FTIR), porosimetry, and sonic reflectometry. Five resonances were measured using a custom-made microwave resonator with measurements at frequencies between 980 MHz – 3.5 GHz. As this comprehensive characterization evolves, it will use machine-learning algorithms to display the correlation between the electromagnetic response of the material and other rock properties; e.g., clay content, saturation, porosity, and mechanical properties. FTIR and low-frequency impedance enable the characterization at surface and bulk volume, respectively. The experimental results confirm the correlation between some chemical, mechanical, and electromagnetic properties of materials. These relations could be used to derive clay content, total organic content, maturity, saturation, among other material properties. The results of this characterization contribute to the growing database of electromagnetic properties of rocks under different configurations. The expectation is that this information can be coupled with statistical and machine learning algorithms to build edge neural engines for subsurface characterization and HPEM tools. Electromagnetic tools and methods for subsurface allow characterizing the formation with high accuracy and resolution. The combined results and algorithms could enable the estimation of rock type, porosity, saturation, and other properties while conducting HPEM operations. The information provided could eventually enable automated systems and logging-while-lasing. This work presents the foundational blocks to achieve this and guide the development of subsurface laser technologies. |
