Machine Learning & Data Science
Bring Big Data insights and innovation from R&D to the factory floor
Data-driven models identify hidden patterns that can lead to increased quality and predictability
Helping Manufacturing Excellence Managers improve performance using data
Deliver value to your industrial plants with models that apply machine learning and data driven techniques to identify root causes of variability in quality or output. Use model results to implement new programs that minimize variability and achieve predictable growth.
Use data to solve longstanding quality or performance issues
Develop practical solutions for solving difficult business problems with the help of intelligent models based on a large volume and variety of observations or measurements.
Discover hidden causes for less-than-optimal performance
Data analytics and machine learning algorithms find underlying correlations between a wide range of data inputs and create a model that can be validated to run process simulations
Choose/implement use cases with the best chance of success
IPCOS data specialists combine engineering, mathematics, and IT expertise, assuring a higher chance of success in developing and implementing machine learning technologies in the factory.
Improve Production Surveillance
A high performance data analytics and visualization environment that provides the capability to integrate a wide range of field data to bring out hidden information and extract reservoir and production intelligence.
Integrated Data Analytics And Visualization for Reservoir and Production Performance Management
Detect Well Interference
Detect well interference events based on a novel PI-based approach with no prior knowledge mitigating human bias and implemented as a recommendation system for interference.
Automatic Well Interference Identification and Characterization: A Data-Driven approach to Improve Field Operation
Drive Directional Drilling Efficiency
Develop a real-time deep learning model to detect and estimate the duration of downlinking sequences of Rotary Steerable Systems (RSS) based on a single measurement (standpipe pressure, SPP)
The Development and Application of Real-time Deep Learning Models to Drive Directional Drilling Efficiency
Less expenses on operator trips
Less errors due to manual work
Integrated Asset Modeling
Cost-effective integration of GAP, MBAL and PROSPER to maximize production across multiple assets.
Turnkey models provide in-depth intelligence for improved analysis, troubleshooting, and operation