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News 19th April 2024

Visit Rock Flow Dynamics at OPES (Oman Petroleum & Energy Show) 2024

Oman Conference & Exhibition Centre, Muscat, 22-24 April Oman’s leading International Oil, Gas & Energy event returns to Muscat this year from 22-24 April. Visit Rock Flow Dynamics at stand 3320 to learn about our advanced modelling technology for geoscientists & engineers, and workflows to support companies in reaching their energy mix goals. Meet our …

Publications 17th April 2024

Using Dynamic Simulations to Improve Geological Models: A Full Field Integrated Study of an HPHT Gas Field in the Norwegian Sea

Abstract Assisted history matching is a pivotal process in optimizing reservoirs models through the calibration to observed well and pressure field data. Robust reservoir forecasting is essential for effective asset management and development planning; however, it is significantly impacted by uncertainties inherent in the subsurface. Large uncertainties exist in the underlying geological and simulation inputs …

Publications 22nd February 2024

Uncertainty and Risk Due to Known Knowns, Known Unknowns and Unknown Unknowns in Saline Aquifer CO2 Sequestration

Abstract Sustainable economic development and environment protection is pivotal for life on earth. Geological storage of increasing emission of CO2 in saline aquifers has huge potential. But saline aquifers are poorly understood due to paucity of data. Known knowns, known unknowns and unknown unknowns are strikingly different in saline aquifers compared to hydrocarbon fields. These uncertainties …

Events 15th February 2024

Join us at the Annual Technology Summit in Cairo

Join us at the tNavigator Technology Summit in Cairo on February 15th, where innovation meets opportunity! 📅 Date: February 15th, 2024📍 Location: Renaissance Cairo Mirage City Hotel, Cairo, Egypt This event provides a unique opportunity to connect with fellow tNavigator users, share insights, and explore the latest updates and features. Immerse yourself in a day …

Publications 6th October 2023

Optimizing Well Trajectory Using Sequential, Hybrid Sequential, and Fully Concurrent Method Utilizing Machine Learning: A Case Study of a Tight Limestone Reservoir

Abstract This paper describes the process and results of using machine learning to automatically determine the optimum well trajectories for a tight limestone reservoir. The study aimed to find the best trajectories to maximize gas production while following certain constraints related to safe drilling operations and surface limitations. These constraints included the available surface locations, …