Integrated Predrill and Real–Time Pore Pressure prediction for Exploration Well Drilling in Xihu Depression, East China Sea

2020 ◽  
Author(s):  
Fawei Lu ◽  
Feng Gui ◽  
Meng Wang ◽  
Sanjeev Bordoloi ◽  
Michael Reese
2019 ◽  
Vol 7 (11) ◽  
pp. 414 ◽  
Author(s):  
Yang Yu ◽  
Huiping Xu ◽  
Changwei Xu

Seafloor observatories enable continuous power supply and real-time bidirectional data transmission, which marks a new way for marine environment monitoring. As in situ observation produces massive data in a constant way, the research involved with data acquisition, data transmission, data analysis, and user-oriented data application is vital to the close-loop operations of seafloor observatories. In this paper, we design and implement a sensor web prototype (ESOSW) to resolve seafloor observatory information processing in a plug-and-play way. A sensor web architecture is first introduced, which is information-oriented and structured into four layers enabling bidirectional information flow of observation data and control commands. Based on the layered architecture, the GOE Control Method and the Hot Swapping Interpretation Method are proposed as the plug-and-play mechanism for sensor control and data processing of seafloor observatory networks. ESOSW was thus implemented with the remote-control system, the data management system, and the real-time monitoring system, supporting managed sensor control and on-demand measurement. ESOSW was tested for plug-and-play enablement through a series of trials and was put into service for the East China Sea Seafloor Observation System. The experiment shows that the sensor web prototype design and implementation are feasible and could be a general reference to related seafloor observatory networks.


2020 ◽  
Vol 12 (5) ◽  
pp. 755
Author(s):  
Dae-Won Kim ◽  
Young-Je Park ◽  
Jin-Yong Jeong ◽  
Young-Heon Jo

Sea surface salinity (SSS) is an important tracer for monitoring the Changjiang Diluted Water (CDW) extension into Korean coastal regions; however, observing the SSS distribution in near real time is a difficult task. In this study, SSS detection algorithm was developed based on the ocean color measurements by Geostationary Ocean Color Imager (GOCI) in high spatial and temporal resolution using multilayer perceptron neural network (MPNN). Among the various combinations of input parameters, combinations with three to six bands of GOCI remote sensing reflectance (Rrs), sea surface temperature (SST), longitude, and latitude were most appropriate for estimating the SSS. According to model validations with the Soil Moisture Active Passive (SMAP) and Ieodo Ocean Research Station (I-ORS) SSS measurements, the coefficient of determination (R2) were 0.81 and 0.92 and the root mean square errors (RMSEs) were 1.30 psu and 0.30 psu, respectively. In addition, a sensitivity analysis revealed the importance of SST and the red-wavelength spectral signal for estimating the SSS. Finally, hourly estimated SSS images were used to illustrate the hourly CDW distribution. With the model developed in this study, the near real-time SSS distribution in the East China Sea (ECS) can be monitored using GOCI and SST data.


Author(s):  
Huiping Xu ◽  
Changwei Xu ◽  
Rufu Qin ◽  
Yang Yu ◽  
Shangqin Luo ◽  
...  

2013 ◽  
Vol 20 (6) ◽  
pp. 1284-1292
Author(s):  
Nan LIN ◽  
Yazhou JIANG ◽  
Xingwei YUAN ◽  
Jing GUO ◽  
Jianzhong LING ◽  
...  

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