Driving the Future of Energy with Enterprise AI for a Leading LNG Producer
About the client
A leading Liquified Natural Gas (LNG) producer in the World with a high export capacity worked with us to build an Enterprise AI application to identify patterns and insights in sub system data, predict outcomes in complex scenarios and improve system performance over time.
The client has a massive infrastructure and operates all oil and gas activities in its state of origin, like production, exploration, production, refining, storage, and transportation.
We partnered with a leading industrial service provider to build a solution for the client on C3.ai, a comprehensive Enterprise AI application development platform. Our Enterprise AI solution could develop cutting-edge ML Models to predict and perform structured maintenance of energy subsystems.
Improve energy output, efficiency & reduce production downtime
Energy producers across the globe are looking to continuously maximize energy output, improve efficiency and reduce production downtime. With traditional optimization techniques that rely on fewer variables and are slow with recommendations, achieving these business goals is a far-fetched dream. However, moving to Enterprise AI for an Energy company is no cakewalk, and implementing a single enterprise AI application requires developing, deploying, & maintaining a large number of machine learning (ML) models.
Our Approach and Solution
1. Accelerated Delivery
The C3 EnterpriseAI Application Development platform uses a model-driven architecture to accelerate delivery and reduce the complexities of developing enterprise-scaleAI applications. It provides a low-code/no-code AI and IoT platform with 26 times faster application development and production deployment. The platform has thousands of pre-built and extensible models on various underlying infrastructures.
2. Deploying product strategy
With our experience & expertise in the various modules of the C3 AI Suite, we could implement the Enterprise AI solution using the client’s raw data that delivered value in a few months. Our in-house team of C3-certified Data Integrators, Data scientists, and Application Developers helped the client achieve this mammoth implementation rather quickly, unlike the industry norm of 1-3 years. Here is how we did this:
· We conducted multiple workshops with clients to develop a deep understanding of 5+ years of their system data.
· We leveraged the C3™ AI Suite for Data Integration, Data Normalization, ML Model Training, Deployment, and Application Development for the client.
· We performed extensive data analysis &ML Models development at a subsystem level.
· The solution implementation was done phase-wise with quick value realization and demonstration of capabilities.
· With continuous management and re-training of ML Models based on the latest data, the solution delivered high accuracy and efficiency and minimized false positives.
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Team members involved
Our solution can prevent unforeseen incidents in the subsystems for the client by detecting early signs of anomalies like corrosion etc. With our Enterprise AI solution, the client is more proactive, can track machinery downtimes, and predict the time to carry out maintenance activity. Here are the highlights of the solution and the impact it made.
250+ ML models deployed to predict outcomes across various subsystems
90-95% accuracy for predicting anomaly events compared with ground truth ·
Intuitive visualization for PredictiveMaintenance
With better visibility of overall processes and operations, the client can make informed decisions to improve operations efficiency, achieve cost reduction, and minimizes the risk of failure.