E73 AI - Electronic Permit to Work System & Software Experts

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Cognitive intelligence in energy industry

Cognitive intelligence, also known as cognitive computing, is a rapidly growing technology in the Energy industry. This technology uses advanced algorithms and machine learning to analyse large amounts of data and make predictions and recommendations based on that data.

In the oil and gas industry, the advantage of cognitive intelligence can be used to identify:

Optimize operations - Cognitive intelligence can be used to analyze production data, identify patterns, and make recommendations for optimizing production and reducing costs.
Improve safety - Cognitive intelligence can be used to analyze safety data, identify potential hazards, and make recommendations for improving safety.
Predict equipment failure - Cognitive intelligence can be used to predict equipment failure and recommend maintenance and repair actions before equipment fails.
Improve exploration - Cognitive intelligence can be used to analyze geological data and identify potential areas for exploration.
Reduce environmental impact - Cognitive intelligence can be used to monitor environmental data and make recommendations for reducing the environmental impact of oil and gas operations.

The benefits of implementing cognitive intelligence in the oil and gas industry include:

Increased efficiency - Cognitive intelligence can help companies optimize their operations and reduce costs, leading to increased efficiency and profitability.
Improved safety - Cognitive intelligence can help identify potential safety hazards and make recommendations for improving safety, reducing the risk of accidents and injuries.
Better decision-making - Cognitive intelligence provides valuable insights and recommendations, enabling better decision-making and risk management.
Reduced downtime - Cognitive intelligence can predict equipment failure and recommend maintenance and repair actions, reducing downtime and improving productivity.
Improved environmental stewardship - Cognitive intelligence can help companies monitor and reduce their environmental impact, improving their reputation and regulatory compliance.

Computerised Maintenance Management System (CMMS):

Computerised maintenance management system (CMMS) helps businesses manage their maintenance operations effectively. Businesses may plan, track, and organise maintenance work for facilities, machinery, and equipment with CMMS.

Work order management, preventive maintenance planning, asset tracking, inventory management, and maintenance history tracking are among a CMMS's basic features.

The CMMS equips maintenance teams to be more proactive and responsive to equipment faults, decreasing downtime and increasing asset lifespan by centralising maintenance data and automating regular operations.

CMMS software provides managers with real-time insights into maintenance efforts, enabling them to evaluate performance indicators, spot reoccurring issues, and improve maintenance tactics. Organisations may increase equipment reliability, allocate resources more efficiently, and cut costs by using this data-driven approach to maintenance.

Reliability-Centered Maintenance (RCM):

A thorough approach to maintenance called reliability-centred maintenance (RCM) focuses on finding the best maintenance jobs to perform in order to maximise asset reliability. RCM is largely used in sectors like aviation, manufacturing, and energy where equipment reliability is crucial.

The RCM procedure entails a systematic examination of the equipment's operations, modes of failure, and effects.

Based on each component's importance and effect on the functioning of the entire system, it seeks to find the best maintenance plan for that component. RCM places a strong emphasis on preventative maintenance procedures that lower operating risks, cut downtime, and prevent failures.

Organisations can use data to inform decisions about maintenance chores, such as time-based maintenance, condition-based maintenance, or run-to-failure strategies, by putting RCM principles into practise. Improved asset performance and increased safety are the results of using RCM to balance maintenance costs and system reliability.

Electronic Permit to Work System:

A complex software programme created specifically to monitor and regulate work permits in hazardous and industrial settings is known as an electronic permission to work system. Historically, obtaining a permit to work was a laborious and error-prone paper process that could result in a danger to one's safety.

Electronic permit to work solutions have made it possible for enterprises to expedite their permit approval processes and guarantee a safer working environment.

By using a defined workflow, this system enables authorised people to request and get permits for particular work tasks, such as maintenance, repair, or building. Multiple stakeholders, such as permit applicants, permit issuers, and permit approvers, often participate in the system. To ensure adherence to safety requirements and reduce potential dangers related to the activity, each stakeholder participates in the process.

Automated permit creation, real-time status monitoring, risk analyses, hazard identification, emergency response planning, and interaction with other safety management tools are frequently essential components of an electronic permit to work system. Organisations can increase safety compliance, lower administrative costs, and boost overall management efficiency for work permits by digitising the permit-to-work process.

Incident Management System:

An organisation’s safety incidents and near-miss events can be reported, tracked, and resolved more easily with the help of an incident management system.

For monitoring accidents, making sure prompt responses are given, and looking into fundamental causes to stop recurrences, it offers an organised framework.

Through an easy-to-use interface, the system enables employees to report problems, and it may automatically notify all necessary parties, such as management and safety officers. To help uncover trends and patterns that can help prevent similar accidents in the future, incident data is recorded, categorised, and analysed.

Risk analyses, incident procedures, tracking for corrective actions, and reporting capabilities are frequently found in incident management systems. Organisations can enhance their safety culture, encourage openness, and guarantee regulatory compliance by putting such a system in place.

Predictive Maintenance

Data from a variety of equipment, such as pumps, gas engines, gas turbines, and compressors, was used to train our models.

To forecast typical and healthy values for offshore and onshore rotating machinery and process instruments, we develop predictive machine learning models. To automatically indicate departure from desired setpoints and parameters, we do anomaly detection.

By preventing equipment tripping through early anomaly identification and warnings, we increase equipment reliability. Based on a failure mode library that we have established with related underlying causes, we provide a prescriptive component to prescribe a subsequent course of action.