The behavior of gases within subsurface pores determines the oil and gas recovery and CO2 storage in the region. In this study, we report a novel method based on Raman spectroscopy for observing the distributions of CH4 and CO2 gases in the pores of sandstone reservoirs. First, we designed a pressure-cell to inject gases into a sample. Then, CH4 and CO2 gases were injected into the sample using the pressure-cell placed on the Raman spectroscopy system sample stage. Quartz and feldspar were the predominant minerals in the sample. The CH4-occupied pores exhibited a Raman peak at 2917 cm-1 . Two-dimensional (2D) and three-dimensional (3D) mapping of the pores depicted the geometry of the CH4 gas-filled pores. After injecting CO2 gas, we observed an intensity peak at 1388 cm- 1 ; we obtained 2D and 3D maps of the CO2 gas-filled pores based on this peak value. This study demonstrates the potential use of Raman spectroscopy as a visualization tool to reveal the pore geometry of sandstone reservoirs and determine the distribution of gases within such reservoirs. Our study can serve as a foundation for understanding the behavior of gases in subsurface reservoirs, improving oil and gas prospecting and exploration, and assessing CO2 storage. .

With colonoscopy becoming a common procedure for individuals aged 50 or more who are at risk of developing colorectal cancer (CRC), colon video data is being accumulated at an ever increasing rate. However, the clinically valuable information contained in these videos is not being maximally exploited to improve patient care and accelerate the development of new screening methods. One of the well-known difficulties in colonoscopy video analysis is the abundance of frames with no diagnostic information. Approximately 40% - 50% of the frames in a colonoscopy video are contaminated by noise, acquisition errors, glare, blur, and uneven illumination. Therefore, filtering out low quality frames containing no diagnostic information can significantly improve the efficiency of colonoscopy video analysis. To address this challenge, we present a quality assessment algorithm to detect and remove low quality, uninformative frames. The goal of our algorithm is to discard low quality frames while retaining all diagnostically relevant information. Our algorithm is based on a hidden Markov model (HMM) in combination with two measures of data quality to filter out uninformative frames. Furthermore, we present a two-level framework based on an embedded hidden Markov model (EHHM) to incorporate the proposed quality assessment algorithm into a complete, automated diagnostic image analysis system for colonoscopy video.