Machine learning and artificial intelligence applications in artificial lift systems have seen a growth in importance recently and are no longer a nice to have, but essential tools for design, optimisation and failure prediction. Real-time optimisation techniques that help to optimise the production, however, do not necessarily holistically consider the equipment reliability and best operating range.
Whenever a failure occurs, the reasons could be attributed to more than one condition and hence, Root Cause Analysis is often a complicated process involving visual inspection and laboratory analysis to confirm the reason for the failure. The best operating envelopes for the lift system are also at the discretion of the optimisation engineer, who may not visit often enough to account for changing operating conditions. This may lead to the system operating in a conservative fashion leading to reduced production. As an example, a rod pump might operate at a lower speed anticipating high rod stresses based on historical operation. In some instances, the systems might not be designed for the desired operating environment and may pose a threat to its reliability. There is a need for a technology which would serve as a guide to overcome these challenges using real-time diagnosis and provide foresight into future operations and potential problems that may increase operator costs.
Emerson’s predictive analysis for artificial lift using the Knowledge Net (KNet) Machine Learning Platform is an engineered solution for predicting failures or abnormal working conditions before their onset. The solution utilises historical data from the wells to build a solid offline well model, which then gets trained on the real-time data as the well comes on to production. As the lift systems are subject to many complex events that might lead to a potential failure, the principal component analysis helps in the elimination process. With a comprehensive Failure Mode Effect Analysis and Root Cause Analysis library, the solution captures, in real-time, the abnormality and translates it into a potential run time deviation. With a prior indication, the condition can be corrected or interventions planned more efficiently. The dynamic modelling of key performance indicators based on system intelligence help in driving asset performance to identify the priorities relevant to the existing conditions. This widens the scope from mere well performance to complete asset performance enhancement.
How to navigate the high-stakes economics of AI infrastructure
IT in Manufacturing
Nearly 60% of data centre operators are expected to adopt liquid cooling within five years as AI workloads drive demand for higher rack densities. Understanding the full financial picture of liquid cooling, from capital investment to long-term operational savings, is now essential.
Read more...The wireless route to safer, more efficient mining operations Emerson Automation Solutions
Editor's Choice Sensors & Transducers
Modern wireless level measurement technologies are helping mining operations improve process control, increase safety and reduce installation costs. Emerson’s Rosemount 3408 wireless non-contacting radar level transmitter uses 80 GHz frequency-modulated continuous wave radar with HART communication to reach remote assets where cabling is impractical.
Read more...Protecting the digital mine RJ Connect
IT in Manufacturing
As mining becomes increasingly connected and automated, cyber threats are emerging alongside the benefits of digitalisation. RJ Connect and provides rugged networking, edge computing and cybersecurity technologies from MOXA to help protect critical mining operations from evolving attacks.
Read more...Predictive maintenance is the critical enabler for AI data centre liquid cooling systems
IT in Manufacturing
As liquid cooling becomes essential for AI data centres, predictive and condition-based maintenance are emerging as critical enablers of thermal stability. Schneider Electric argues that the infrastructure is already generating the data needed and the question is whether operators are equipped to use it.
Read more...Siemens software scales with Simple Energy EV development Siemens South Africa
IT in Manufacturing
: Simple Energy, an Indian electric two-wheeler manufacturer, has expanded its use of Siemens Xcelerator software to manage complex vehicle variants and enterprise-level bills of materials, accelerating product development as its EV portfolio scales.
Read more...ESG as a growth lever RS South Africa
IT in Manufacturing
Leading organisations are treating ESG not as a compliance obligation but as a practical tool for operational efficiency, supply chain resilience and business performance, with data and collaboration central to unlocking its value.
Read more...Preparing Africa’s data centres for the demands of autonomous AI Schneider Electric South Africa
IT in Manufacturing
Africa’s data centre infrastructure must evolve significantly to support agentic AI workloads, with GPU-intensive computing, advanced cooling and reliable power demanding a fundamentally different approach to facility design and investment.
Read more...Virtualising the control room to reshape building management systems Schneider Electric South Africa
IT in Manufacturing
Schneider Electric explains how virtualising building management systems frees facilities teams from ageing hardware, delivers stronger cybersecurity and enables portfolio-wide control from a single interface.
Read more...‘Control Talk SA’ podcast with Emerson on level measurement for the mining industry Emerson Automation Solutions
Level Measurement & Control
Reliable level measurement is critical to keeping South African mines safe, productive and sustainable, but harsh conditions can make accurate readings challenging. In this Emerson podcast, Shivam Misra explores how advanced radar technology and remote monitoring are helping mines improve measurement reliability while reducing operator exposure to hazardous areas.
While every effort has been made to ensure the accuracy of the information contained herein, the publisher and its agents cannot be held responsible for any errors contained, or any loss incurred as a result. Articles published do not necessarily reflect the views of the publishers. The editor reserves the right to alter or cut copy. Articles submitted are deemed to have been cleared for publication. Advertisements and company contact details are published as provided by the advertiser. Technews Publishing (Pty) Ltd cannot be held responsible for the accuracy or veracity of supplied material.
printer friendly version