Complete Form to Download the Technical Paper
"*" indicates required fields
At the Pipeline Technology Conference in April 2024, Nigel Curson presented a technical paper that explored a novel use of machine learning to optimise oilfield water flood systems, identifying constraints and quantifying performance improvements with high accuracy, surpassing traditional methods.
The paper provide insights into a pioneering method for optimising oilfield water flood systems using machine learning, overcoming challenges of internal constraints and imperfect data and showcases how supervised learning algorithms accurately quantify performance improvements, offering insights into practical implementation and future optimisation strategies in the oil and gas industry.
Complete the form to download the full paper.
Related Insights
From chokepoints to corridors: why energy security now depends on infrastructure resilience
In the 31st issue of Dar Magazine, Nigel Curson, EVP of Technical Excellence, explores why resilient infrastructure will play a defining role in Africa's long-term energy security and economic...
Industry Report – Capability Gap Analysis for Newbuild Offshore CO2 Pipeline Transportation Systems
Penspen's new report, commissioned by the North Sea Transition Authority, examines the challenges posed by transporting carbon dioxide through newbuild offshore pipelines, and addresses the steps...
Managing Electrical Interferences in Pipelines – A Practical and Cost-Effective Approach
The rapid expansion of AC/DC transmission networks, electrified transportation systems and pipeline infrastructure has increased the spatial overlap among these...
Growth Across Borders: Meet Nilesh
A lifelong passion for continuous improvement has seen Nilesh soar in his career. Now, as Principal Mechanical Engineer at Penspen, he's using his experience to mentor graduate...