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Events 12th September 2023

Upcoming Technology Days in Norway

Join our upcoming workshops in Stavanger & Oslo, where we will present user case studies and live demonstrations of our integrated reservoir modelling technology. Hear the latest updates and speak to our experts about how tNavigator can support your projects. AGENDA One asset, one software approach for static to dynamic modelling Production enhancement & optimisation …

News 14th July 2023

Rock Flow Dynamics Unveils Latest Version of tNavigator

Rock Flow Dynamics (RFD) is pleased to announce the release of the latest version of our flagship product, tNavigator. Version 23.2 is now available to users. tNavigator has long been recognized as a powerful tool for reservoir engineers and geologists, providing accurate modeling and simulation of complex reservoir behavior. The latest version brings significant advancements …

News 13th July 2023

Rock Flow Dynamics Expands Its Global Presence with New Office in Belgrade

Rock Flow Dynamics continues to expand its team and operations globally and has recently opened a development centre in Belgrade, Serbia. RFD’s hub aims to support the creation of tNavigator — a state-of-the-art reservoir modelling and simulation platform that offers advanced innovative tools for geology, geophysics, and reservoir engineering disciplines. The decision to establish a …

Events 15th August 2023

Tee Off with tNavigator – Calgary

Gear up for our annual “Tee Off with tNavigator” tournament in Calgary on August 15th! Join Rock Flow Dynamics for an incredible golf day, with lots of great prizes, food, and drinks. With tee off after lunch and a grand conclusion with dinner and awards at the clubhouse, exact event times will be announced soon. …

Publications 5th June 2023

Analysis of Well Production Data Using Functional Data Analysis

This study employs novel ensemble-based statistical techniques for type-well analysis to quickly analyze the production data from multiple wells in a reservoir. The method is based on functional principal component analysis (FPCA) that accounts for measurement noise and the sparsity of the production timeseries. In particular, the sparse FPCA method is used, which can extract …