Machine learning in fluid composition quantification

A critical issue in the oil and gas industry is to quantify the composition of fluids flowing back from the hydraulic fracturing process. This quantification is usually carried out by a manual process (frequently via a visual test) to estimate the water and oil produced from a well flow back process. A sample of these onsite tests are sent to laboratories for chemical analysis. This process has been the status quo for decades. This approach is manual, prone to error, and does not lend itself to sophisticated real time analysis. Machine learning techniques have significantly developed in the last decades, and combining with in-depth mathematical basis, it is now capable of producing a revolutionary impact to almost every industrial application. This research project aims at developing a machine learning framework, that can detect the fluid composition based on the sensor data as well as referencing chemical analysis results.

Faculty Supervisor:

Yau Shu Wong


Yile Zhang


MaxFleet Solutions




Information and communications technologies




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