JOGMEC has been working on several research projects in order to improve the efficiency of operations in oil and gas E&P business by incorporating digital technologies such as artificial intelligence（AI）and internet of things（IoT）since 2018.
As the first research project in drilling was prediction of mud loss while drilling operations by using commercialized AI software. However due to mismatch of the analytical algorithm of the AI and insufficient data quantity, significant result could not be obtained. On the other hand, importance of selecting proper analytical algorithm and preparing sufficient data is recognized from this project.
The second project is prediction of stuck pipe while drilling. Based on the lessons learned from the first project above, the research consortium was established with Japanese E&P companies and the other research institutes including universities in order to gather sufficient data for analysis and develop the AI algorithm. Some algorithms were tried and one of them might look to detect the indication of pipe stuck from the provided data. Currently, the system verification test is being carried out at actual drilling site.
In 2019, JOGMEC joined the Rig Automation and Performance Improvement in Drilling（RAPID）consortium which is led by the University of Texas at Austin. In RAPID, JOGMEC has been working on 2 research projects, one is the condition based maintenance（CBM）of mud pumps and the other is the CBM of mud motor. The objectives of these projects are to estimate the accumulated damage on equipment or tools in order to optimize the timing of maintenance. Currently, data acquisition at actual drilling site is planned for verifying the algorithm of mud pumps CBM, and the algorithm of mud motor CBM is under development.
