Determination of oil and water distributions in the pore space of reservoir rock is important in oil recovery. pore space of reservoir rock is important in oil recovery. Several indirect methods, such as testing wettability of mineral surfaces, assessing capillary pressure and relative permeability behavior, and measuring electrical resistivity, have been practiced to estimate the oil and water distribution in porous rock. However, a direct method of determining liquid distributions in situ has been lacking. Cry-scanning electron microscopy (cryo-SEM) provides a means of visualizing oil and water in the provides a means of visualizing oil and water in the individual pore segments of a porous rock. The liquid-bearing rock is frozen and kept cold at about 100 K. The frozen sample is then fractured, coated with evaporated chromium, and examined in a scanning electron microscope. Secondary and backscattered electron images and x-ray maps of the fracture surface identify the locations of oil and water in the exposed section of the pore space. Sometimes complementary fracture surfaces can be imaged. Series of the backscattered electron images illuminate the mechanisms of oil-water displacement in strongly water-wet and mixed-wet sandstones. Introduction Waterflooding is the most common process to recover petroleum from porous rock after primary production. However, the performance of this process is production. However, the performance of this process is erratic; it differs from reservoir to reservoir. Differences in rock, oil, brine, temperature, and history of liquid occupancy in the rock have all been implicated. The attendant uncertainties make it difficult to predict the portion of oil that can be recovered from a given portion of oil that can be recovered from a given reservoir by this process. Prediction is important in assessing the feasibility of conducting a more advanced recovery process, such as carbon dioxide flooding, steam flooding, or surfactant-based flooding, either during or after waterflooding. More accurate prediction requires thorough knowledge of the mechanisms of the waterflooding and other immiscible displacement processes. processes. The success of waterflooding depends on the water sweep efficiency and the efficiency of water displacing oil in the swept region. The water sweep efficiency is outside the scope of this paper. The capillary and thin film phenomena of liquid-liquid displacement in the swept region are the roots of this paper. Many laboratory experiments have shown that the mechanism and efficiency of oil recovery by waterflooding depend greatly on wettability. On smooth solid surfaces, wettability can be determined by measuring contact angle between oil-water interface with the solid surface: the solid surface is strongly water-wet if the contact angle measured from the water phase approaches 0 deg., the solid surface is weakly-wet if phase approaches 0 deg., the solid surface is weakly-wet if the contact angle approaches 90 deg., and the solid surface is strongly oil-wet if the contact angle approaches 180 deg. In contrast, the wettability in natural porous media is difficult to determine because the solid surfaces are rough, angular, and heterogeneous. Many researches have shown that at the end of waterflooding of strongly water-wet rock, oil is stranded in many pore bodies as blobs, and water lines most of the pore walls; residual oil saturation (ROS) as high as 48% has been reported. P. 459
In the industrial era 4.0, domestic baby incubator producers are facing the challenge of free trade of foreign products that will compete in innovation with the application of IoT technology. One of the opportunities that arise consciously or unconsciously at the NICU (Neonatal Intensive Care Unit) unit in Hospitals, in general, is that there are no facilities for parents to monitor the baby’s condition inside the incubator directly. The purpose of this research project is to build a prototype of an internet-based baby incubator monitoring system based on things equipped with various sensors that will send data to the server in real-time and mobile apps for monitoring facilities for parents, including the voice of the baby. This study focused on how to develop baby incubators that can listen to baby’s crying, capture the voice, and interpret it using artificial intelligence. More than 40 (forty) voice datasets were used and successfully classified into five possible terms of the baby’s condition: burping, sleepy, hungry, uncomfortable, and pain by energy signal and spectrum analysis. The benefit of this research is as a driver of innovation for baby incubator products that will support the national medical devices manufacturing company.
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