Hydrogeochemical characteristics and genesis of seismic observation wells in Shandong Province, China

2021 ◽  
Vol 14 (22) ◽  
Author(s):  
Shujuan Su ◽  
Qifeng Chen ◽  
Chunhong Zou ◽  
Bingdun Yan ◽  
Guilin Du ◽  
...  
Keyword(s):  
Shandong Province ◽  
Seismic Observation ◽  
Observation Wells

Hydrogeochemical characteristics and genesis of Seismic observation wells in Shandong Province

2021 ◽  
Author(s):  
Shujuan Su ◽  
Qifeng Chen ◽  
Chunhong Zou ◽  
Guilin Du ◽  
Heqian Wang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Oxygen Isotopes ◽  
Water Samples ◽  
Atmospheric Precipitation ◽  
Shandong Province ◽  
Seismic Observation ◽  
Hydrogen And Oxygen Isotopes ◽  
Recharge Sources ◽  
Distribution Rules ◽  
Observation Wells

Abstract Hydrogeochemical characteristics, controlling factors, and recharge sources of the seismic observation wells in Shandong Province were investigated by analyzing cation and anion concentrations, hydrogen and oxygen isotopes in well water. A total of 17 water samples in seismic observation wells were collected on April 25-29, 2018. The results show that temperatures of seismic observation wells were in the range of 14.8 to 52.1°C, and the values of δD and δ 18 O ranged from -72.4‰ to -37.9‰ and from -9.4‰ to -4.3‰, respectively. Using C.A. ЩукаЛев’s classification method, the water samples of 17 seismic observation wells were classified into 7 types: Cl·SO 4 -Na·Ca, SO 4 -Na, Cl-Na, HCO 3 -Na·Ca, HCO 3 -Mg·Na·Ca, HCO 3 -Na and HCO 3 -Mg·Ca·Na. The results indicate that the hydrogeochemical characteristics of 17 seismic observation wells, with a certain spatial distribution pattern, are affected by several factors, such as the tectonic, topography, stratigraphy, hydrology and meteorology. The analyses of ratio coefficients, Schooller diagram, hydrogen and oxygen isotopes compositions, Giggenbach and Gibbs diagram suggest that the atmospheric precipitation is the main recharge source of 17 observation wells. The recharge sources of deep lateral runoff and sedimentation water, moreover, play a significant role in some seismic observation wells. Combined with the amount of precipitation, the distance from recharge areas, the closure degree of observation wells and the stage of water-rock reactions, the development directions of faulting and topography control the directions of groundwater recharge, runoff and discharge, which make hydrogeochemical characteristics represent complex spatial distribution rules.

Conception of vadose zone research in the area of Goczałkowice reservoir.

2013 ◽  
Vol 2 (1) ◽  
pp. 22-26
Author(s):  
Joanna Czekaj ◽  
Kamil Trepka
Keyword(s):  
Vadose Zone ◽  
Unsaturated Zone ◽  
Vertical Profile ◽  
Structural Model ◽  
Historical Data ◽  
Integrated System ◽  
Nitrate Transport ◽  
Strategic Research ◽  
Research Site ◽  
Observation Wells

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.

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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