This joint-project aims to address the fundamental research challenge related to the development of Big Data-driven distributed microseismic monitoring method for hydrofracturing oil exporation. This research challenge is related to microseismic image information processing and utilization, i.e., how to quickly and efficiently extract and analyze the information of interest for oil detection, tracking its distribution/storage from distributed data/images acquired by a huge number of sensors deployed across the oil field, through either fixed locations or autonomous vehicles platforms, such as unmanned ground vehicle (UGV). To address this challenge faced by the O&G industry, this joint project is focusing on novel Big Data-driven solutions which can integrate distributed data filtering, intelligent analytics and machine learning techniques to offer unique benefits in terms of its ability to bring in data from multiple and distributed sensing sources and provide a full suite of descriptive, predictive and prescriptive analytics.
Funded by The Royal Society of Edinburgh (RSE), £12,000
|Effective start/end date||1/04/17 → 31/03/19|
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