Development of a highly accurate machine learning algorithm constrained by well-log data and its application in Lithological classification

The drilling success rate is the most important goal for any oil/gas company. For a start-up company, any failure in drilling will be a disaster. To this end, the Petro-Lin Energy Corp. wishes that through the combination of mature hydrocarbon prediction techniques and new research results such as machine learning, the success rate of hydrocarbon prediction, the theoretical basis for well placement can be provided in Roncott oil-field, which will improve the success rate in drilling. The University of Calgary is one of Canada’s top research institutes, especially in the areas of exploration geophysics, seismic data processing and petroleum engineering. On the other hand, Petro-Lin Energy Corp. will collaborate with researchers from University of Calgary to access the most up-to-date research results and the most advanced technology available in precise well placement, so that the drilling success rate can be improved to reduced drilling cost and environment impact, and it can effectively save the partner’s research cost. In addition, through the two-year project, the intern will receive hands-on industry problem and other training activities.

Faculty Supervisor:

ZhangXing John Chen

Student:

Wei Zhou

Partner:

Petro-Lin Energy Corporation

Discipline:

Engineering - chemical / biological

Sector:

Mining and quarrying

University:

University of Calgary