ABSTRACT
This study investigates the impact of micro-fractures on gas flow behavior in coal formations, specifically within the context of CO2-based Enhanced Coal Bed Methane Recovery (ECBMR). Employing comparative analysis, various gas flow models, including Unipore Diffusion Model (UDM), Bidispersed Diffusion Model (BDM), Fractal Fractional Diffusion Model (FFDM), Time-Dependent Diffusivity Model (TDDM), Anomalous Sub-Diffusion Model (ASM), and Free Gas Density Gradient Model (FGDGM), are evaluated for their efficacy in capturing the complexities. The study aims to provide insights into the accuracy and applicability of these models, considering the heterogeneity of coal seams and the influence of micro-fractures on gas flow dynamics. The major findings include the categorization of different gas flow models based on their applicability to CO2-based ECBMR. For instance, the study suggests utilizing BDM and FFDM models while considering the heterogeneity of coal seams. Similarly using the TDDM model for time dynamics of ECBMR will give higher accuracy. The article contributes to a deeper understanding of gas migration processes in coal, particularly in the context of ECBMR, with implications for optimizing recovery strategies and addressing challenges associated with micro-fracture-induced variations in gas flow behavior.
ABSTRACT
To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth's subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.
