How OMV optimized well flow rates with Cognite Data Fusion®

OMV, one of Austria's largest listed industrial companies and a global energy and chemicals group, uses Cognite Data Fusion® to maximize gas condensate production while operating within specific constraints.

A need to balance long-term and short-term goals in production

OMV operates several natural gas fields worldwide. The production is subject to a number of constraints, such as minimum and maximum rates, water handling capacity and market demand.

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 high-value gas condensates, which are a by-product of the separation process). These goals must be balanced while also making sure the field is operating within its specific constraints.

Traditionally, operators would flow wells from highest to lowest priority, as designated by a well priority list prepared by the production technologists. However, 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.

Contextualized, visualized data in an interactive application

OMV developed an optimization model in Cognite Data Fusion® that leverages condensate-gas ratios (CGR), water-gas ratios (WGR), well minimum and maximum rates, facility capacities, planned outages, and other data.

The model outputs flow profiles for each well throughout the gas day to propose optimal flow rates for the wells at the gas field, which maximizes gas condensate production while operating within the constraints. OMV visualized the optimization model in an easy-to-use, interactive application.

Improve transparency, optimize production, and increase profit

The optimization model and application allow users to view relevant, real-time well, separation train, and production data, and forecasted end-of-day production. This level of visibility is expected to:

• Improve transparency by easier sharing of information between different shifts, and between operators and production technologists
• Optimize production by allowing users to edit constraints and change well, separation train, and facilities attributes to tune optimization for different scenarios
• Increase profit by extending the life of gas fields by 35 days per year and adding $300,000 in estimated annual value