Comparison of active impurity control between lithium and boron powder real-time injection in EAST

2021 ◽  
Vol 96 (12) ◽  
pp. 124034
Author(s):  
W Xu ◽  
J S Hu ◽  
Z Sun ◽  
R Maingi ◽  
G Z Zuo ◽  
...  
2021 ◽  
Author(s):  
Teymur Sadigov ◽  
Cagri Cerrahoglu ◽  
James Ramsay ◽  
Laurence Burchell ◽  
Sean Cavalero ◽  
...  

Abstract This paper introduces a novel technique that allows real-time injection monitoring with distributed fiber optics using physics-informed machine learning methods and presents results from Clair Ridge asset where a cloud-based, real-time application is deployed. Clair Ridge is a structural high comprising of naturally fractured Devonian to Carboniferous continental sandstones, with a significantly naturally fractured ridge area. The fractured nature of the reservoir lends itself to permanent deployment of Distributed Fiber Optic Sensing (DFOS) to enable real-time injection monitoring to maximise recovery from the field. In addition to their default limitations, such as providing a snapshot measurement and disturbing the natural well flow with up and down flowing passes, wireline-conveyed production logs (PL) are also unable to provide a high-resolution profile of the water injection along the reservoir due to the completion type. DFOS offers unique surveillance capability when permanently installed along the reservoir interface and continuously providing injection profiles with full visibility along the reservoir section without the need for an intervention. The real-time injection monitoring application uses both distributed acoustic and temperature sensing (DAS & DTS) and is based on physics-informed machine learning models. It is now running and available to all asset users on the cloud. So far, the application has generated high-resolution injection profiles over a dozen multi-rate injection periods automatically and the results are cross-checked against the profiles from the warmback analyses that were also generated automatically as part of the same application. The real-time monitoring insights have been effectively applied to provide significant business value using the capability for start-up optimization to manage and improve injection conformance, monitor fractured formations and caprock monitoring.


Author(s):  
Joseph Jutras ◽  
Rick Barlow

MBS, the software based leak detection system employed by Enbridge, is a real time transient model and as such requires fluid characteristics of the various batches that enter the pipeline. In the past, of the 25 plus pipelines modeled, only 4 received fluid identifiers from the field. These fluid identifiers are a sub-string of the batch identifiers stored in flow computers located at custody transfer locations. On the remaining pipelines, Enbridge used fluid density from the field to infer fluid type and therefore characteristics. In the past whenever a number of fluids had the same density, MBS assigned a best-guess of fluid type. The ‘MBS Real Time Injection Batch Data’ project was proposed to bring fluid identifiers to MBS on the remaining lines with the purpose of improving MBS’ selection of fluid properties. Since injection points on the remaining lines were not custody transfer there were no flow computers at these locations. An existing application called Commodity Movement Tracking, or CMT, was used to provide fluid names to the leak detection model. CMT holds past, present, and future injection batch information in an Oracle database. Batch identifiers are queried, placed into the SCADA system, and forwarded on to MBS. This paper explores the new approach, introduced by the ‘MBS Real Time Injection Batch Data’ project, of providing MBS with batch identifiers.


2020 ◽  
Vol 64 (10) ◽  
pp. 1513-1518
Author(s):  
Andrea Saporito ◽  
Christian Quadri ◽  
Thorsten Steinfeldt ◽  
Thomas Wiesmann ◽  
Laura Micòl Cantini ◽  
...  

2013 ◽  
Author(s):  
J. O. Arukhe ◽  
S. Ghamdi ◽  
M. Dhufairi ◽  
L. Duthie ◽  
K. Omairen ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document