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News 5th December 2023

Rock Flow Dynamics welcomes Santos to the tNavigator community

Rock Flow Dynamics are excited about working with Santos to integrate the tNavigator suite into Santos’ subsurface workflows across the E&P & CCUS value chain. The tNavigator suite impresses with its broad capabilities, extending beyond reservoir simulation to include static and structural modeling, as well as coupled thermal and geomechanical workflows, crucial for all STO’s …

News 15th November 2023

Rock Flow Dynamics Hosts Successful Annual Technology Summits in Middle East

During November, Rock Flow Dynamics, a leading provider of reservoir simulation and modeling solutions, held its Annual Technology Summit in Muscat, Oman and Abu Dhabi, United Arab Emirates. These exclusive events showcased the company’s dedication to advancing industry knowledge, fostering collaboration, and unveiling the latest advancements in reservoir engineering. The Annual Technology Summits featured comprehensive …

Events 5th December 2023

Carbon Capture Storage Summit – Tokyo

5 & 6 December 2023 | Tokyo, Japan Unleashing innovation for a greener tomorrow Explore cutting-edge insights at the Carbon Capture & Storage Summit Tokyo 2023! Delve into global case studies, engage in deep discussions on CCS intricacies, and discover innovative applications. From addressing environmental challenges to redefining industry standards, witness the future of sustainable …

News 9th October 2023

Rock Flow Dynamics Showcases Leading Reservoir Simulation Technologies at ADIPEC 2023

Rock Flow Dynamics, a leader in reservoir simulation and modeling solutions, earlier this month took part in the Abu Dhabi International Petroleum Exhibition and Conference (ADIPEC) 2023. The company’s participation included a prominent booth featuring live demonstrations and engaging presentations by company experts. At the Rock Flow Dynamics booth, seven presentations were hosted, featuring technical …

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, …