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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

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

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 and implement use cases with the best chance of success

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.


IPCOS Case Study

Integrated Data Analytics And Visualization for Reservoir and Production Performance Management

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Integrated Data Analytics And Visualization for Reservoir and Production Performance Management
Automatic Well Interference Identification and Characterization - A Data-Driven approach to Improve Field Operations

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.


IPCOS Case Study

Automatic Well Interference Identification and Characterization: A Data-Driven approach to Improve Field Operation

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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)


IPCOS Case Study

The Development and Application of Real-time Deep Learning Models to Drive Directional Drilling Efficiency

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The Development and Application of Real-Time Deep Learning Models to Drive Directional Drilling Efficiency

End-to-end value

20%

Less expenses on operator trips

10-40%

Less errors due to manual work

Other services

Integrated asset modeling: Minimize Manual Data Processing

Integrated Asset Modeling

Cost-effective integration of GAP, MBAL and PROSPER to maximize production across multiple assets.

Process Modeling Predicts Safety Margins

Process Modeling

Turnkey models provide in-depth intelligence for improved analysis, troubleshooting, and operation

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