Vahabi, N;
(2017)
Analysis of Oil and Gas Big Data Using Artificial Intelligence.
Presented at: UNSPECIFIED.
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Abstract
Monitoring in-well flow is essential for the oil and gas industry to manage the oil field. The flow surveillance identifies the well condition allowing optimisation of the quality and volume of oil or gas production and saving costs for the oil companies. The well operators need to know the type of fluid in the pipe, the combination and ratio of each fluid in multi phase flow regimes (e.g. gas, oil, water), the settings of Inflow Control Valves (ICVs) which control the flow rate in the main pipe and from several side branching pipes coming from different underground reservoirs. The well operators also need to know the speed and direction of the fluid flow at each point down the well. Distributed Acoustic Fibre optic Sensors alongside or inside the well pipe are used to collect acoustic data as a function of time from effective acoustic sensors spaced by about a metre or less along thousands of kilometres of oil and gas well. The size of collected sound data from sensors is more than a Terabyte which can be analysed successfully using Artificial Intelligence Machine Learning algorithms.
| Type: | Poster |
|---|---|
| Title: | Analysis of Oil and Gas Big Data Using Artificial Intelligence |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Keywords: | Big data, classification algorithms, multi phase flow classification. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1571727 |
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