Evaluation of Groundwater Types in Earthquake Observation Wells and Relationship Between Formation Cause and Recharge

2021 ◽  
Vol 178 (2) ◽  
pp. 399-410
Author(s):  
Fenghe Ding ◽  
Heqing Ma ◽  
Guofu Luo
2013 ◽  
Vol 2 (1) ◽  
pp. 22-26
Author(s):  
Joanna Czekaj ◽  
Kamil Trepka

Abstract Goczałkowice reservoir is one of the main source of drinking water for Upper Silesia Region. In reference to Water Frame Directive matter since 2010 the strategic research project: „Integrated system supporting management and protection of dammed reservoir (ZiZoZap)”, which is being conducted on Goczałkowice reservoir, has been pursued. In the framework of this project complex groundwater monitoring is carried on. One aspect is vadose zone research, conducted to obtain information about changes in chemical composition of infiltrating water and mass transport within this zone. Based on historical data and the structural model of direct catchment of Goczałkowice reservoir location of the vadose zone research site was selected. At the end of November 2012 specially designed lysimeter was installed with 10 MacroRhizon samplers at each lithological variation in unsaturated zone. This lysimeter, together with nested observation wells, located in the direct proximity, create the vadose zone research site which main aim is specifying the amount of nitrate transport in the vertical profile.


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.


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