Full-field heterodyne dynamic interferometry based on hertz-level low differential-frequency acousto-optic frequency shifter

2017 ◽  
Author(s):  
Zhou Wu ◽  
Wenxi Zhang ◽  
Bin Xiangli ◽  
Xinxin Kong
Keyword(s):  
Full Field ◽  
Frequency Shifter ◽  
Dynamic Interferometry

Full-Field Approach for Modeling of Microstructural Evolutions During Forming Processes

2019 ◽  
Author(s):  
S. Andrietti ◽  
M. Bernacki ◽  
N. Bozzolo ◽  
L. Maire ◽  
P. De Micheli ◽  
...  
Keyword(s):  
Full Field ◽  
Field Approach

Investigation of Terahertz Polarized Frequency Shifter with Crystal Quartz Phase Sections

2007 ◽  
Vol 66 (4) ◽  
pp. 373-382
Author(s):  
V. K. Kiselyov ◽  
V. I. Bezborodov ◽  
P. K. Nesterov ◽  
M. S. Yanovsky
Keyword(s):  
Frequency Shifter ◽  
Crystal Quartz

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

2020 ◽  
Author(s):  
A. Syahputra
Keyword(s):  
Neural Network ◽  
Water Saturation ◽  
Prediction Method ◽  
Neural Model ◽  
Training Model ◽  
Acquisition Time ◽  
Oil Saturation ◽  
Full Field ◽  
Remaining Oil ◽  
Observation Wells

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.

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