Evaluating the effects of distinct water saturation states on the light penetration depths of sand-textured soils

2021 ◽  
Author(s):  
Gladimir V. G. Baranoski ◽  
Mark Iwanchyshyn ◽  
Bradley Kimmel ◽  
Petri Varsa ◽  
Spencer Van Leeuwen
2012 ◽  
Vol 1426 ◽  
pp. 175-180
Author(s):  
M. A. Vieira ◽  
M. Vieira ◽  
P. Louro ◽  
V. Silva ◽  
A. S. Garção

ABSTRACTThis paper reports on light filtering devices based on a-SiC:H tandem pi´n/pin heterostructures. The spectral sensitivity is analyzed. Steady state optical bias with different wavelengths, are applied from each front and back sides and the photocurrent is measured. Results show that it is possible to control the sensitivity of the device and to tune a specific wavelength range by combining radiations with complementary light penetration depths. The transfer characteristics effects due to changes in the front and back optical bias wavelength are discussed.Input red, green and blue pulsed communication channels are transmitted together, each one in a specific bit sequence and the multiplex signal is analyzed. By superimposing appropriate background and depending on the channel/background wavelength combinations, the device behaves as a long- or a short- pass filter, producing signal attenuation, or as an amplifier, producing signal gain. A physical model is presented to support the filter properties of the device.


Metals ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1382
Author(s):  
Gonzalo Rodríguez Prieto ◽  
Luis Bilbao

Light penetration depth is a fundamental property that has been researched extensively with a large amount of materials. Among those studies, different planetary atmospheres and material phases, like plasmas, had been previously addressed, both theoretically and experimentally. However, no experimental data are available for platinum and iron gases due to the difficulties for the creation of gas state from a solid metal material. This work present experimental penetration depths at 532 nm laser light for iron and platinum gases produced by a carefully tuned exploding wire system in atmospheric air. Iron presents a larger dispersion on the data than platinum, which is explained because of its large magnetic permeability value, that generates a less homogeneous gas than in the platinum case.


Author(s):  
A. Syahputra

Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.


2020 ◽  
pp. 28-34
Author(s):  
I.S. Putilov ◽  
◽  
I.P. Gurbatova ◽  
S.V. Melekhin ◽  
M.S. Sergeev ◽  
...  

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