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Company

An Austrian integrated oil and gas producer who operates several natural gas fields worldwide

www.omv.com

 

Key products and solutions

Cognite Data Fusion®
Cognite Maintain

BOOK DEMO
SUMMARY

How OMV optimized well flow rates with Cognite Data Fusion®

OMV used contextualized data in Cognite Data Fusion® to maximize gas condensate production while operating within constraints.

The solution is estimated to generate $300,000 in value a year.

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$300,000 in estimated annual value

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Extends the life of gas fields

CHALLENGE

Balance long-term and short-term goals in production

OMV operates several natural gas fields worldwide. The production is subject to a number of short- and long-term goals and constraints, such as well minimum and maximum rates, water handling capacity and market demand. High-value gas condensates are a by-product of the separation process.

OMV’s production technologists are faced with a tough task: balancing long-term goals (the life cycle of the field) against short-term goals (meeting demand and producing condensate) while making sure the field is operated within its constraints. To achieve this in day-to-day operations, the operators have flowed the wells from highest to lowest priority as given by a well priority list prepared by the production technologists.

By optimizing condensate production according to the constraints instead of sequentially flowing the wells from highest to lowest priority, OMV could achieve the same goals while also producing more condensate.

SOLUTION

An optimization model powered by Cognite Data Fusion®

OMV and Cognite developed an optimization model that uses information such as condensate-gas ratios (CGR), water-gas ratios (WGR), well minimum and maximum rates, facility capacities, planned outages, and more to propose optimal flow rates for the wells at the gas field. The model outputs flow profiles for each well throughout the gas day, which maximizes gas condensate production while operating within the constraints.

Users can interact with the optimization model in an application that enables them to:

  • Edit constraints and change well, separation train and facilities attributes.

  • Run optimization for different scenarios.

  • View relevant, real-time well, separation train and production data, and forecasted end-of-day production.

  • Share information between different shifts, and between operators and production technologists.