Client’s Background: LTTS’ (L&T Technology Services Limited) client ranks among one of the world’s largest oﬀshore drilling rig contractors. Due to the dependence on manual processes and lack of knowledge transfer, several operational, procedural and process ineﬃciencies had crept into their system. Sometimes, these ineﬃciencies resulted in greater than a million-dollar loss per day.
Client’s Requirement: The client wanted to build a digital twin to review, assess, and map the performance of their operational model and identify areas to improve eﬃciency and automation. After careful evaluation of numerous solution providers, the client selected LTTS with its expertise in asset management services, a strong understanding of the oil & gas industry, and proven credentials in delivering digitalization solutions across industries.
LTTS has been working on building digital twin models for factories and plants across various verticals. The company has developed an iOS app – Factory D.O that demonstrates digital twin possibilities in an automotive factory.
Challenges: The client had to overcome two main challenges:
- Inefficiencies in each Trip-In, Trip-Out (TITO) operation causing losses to the tune of millions of dollars
- Manual entry of data during the drill, resulting in erroneous data in the system
LTTS’ Solution: Machine learning algorithms were built to take the data from the sensors and build a digital twin model of the rig. This model helped in identifying ineﬃciencies and areas that can be automated. The entire process comprised the following:
Code: There were more than 15, 000 lines of existing code and ML algorithms that were reworked and refactored as per the industry standard.
ML Algorithms: More than 54 diﬀerent sets of ML algorithms were designed and enhanced to meet the speciﬁc requirements of the client. The solution included prediction algorithms to accurately predict a drop in eﬃciency.
Visualization: One of the key requirements was to use data from the sensors to build an application that visualizes possible scenarios based on the analyzed data parameters. MATLAB was used to simulate the data received from rigs and build dashboards that could visualize data scenarios for prediction and analysis.
Accrued Benefits: LTTS delivered highly accurate digital twin dashboards that can detect areas of ineﬃciencies. The digital twin contributed to an approximate cost saving of 20 percent in rig operations. The solution showed areas of rig operation that can be automated, further adding to the cost beneﬁt. This data also helped the client in further planning and development of their rigs at other locations.