Augment Workforce with Intelligence to Boost Profitability: The Oil and Gas

Oil prices have been swinging sharply in recent years, fluctuating from lows of $30 per barrel to highs of $100 per barrel. The unexpected price volatility is causing stress on the CAPEX and OPEX of the oil and gas value chain. Compounding the challenges, the negative perception of fossil fuels, rising trade tensions and geopolitical upheaval are adversely affecting near-term demand and business costs. 

As a result, more companies are looking towards “Digital Transformation” to drive effective capacity, not only through CAPEX but also OPEX investments. By using analytics and Artificial Intelligence (AI), companies empower the workforce and enable swift responses to market changes. They can drive optimised operations and improved asset availability as these are more scalable and have a shorter lead time. 

Using digital tools to tackle Oil and Gas data challenges

Long before the term “AI” was coined, Oil and Gas companies were collecting huge volumes of operational data, from exploration to production. However, in the past, turning that vast amount of raw data into contextual information around equipment and processes for production improvement was often challenging due to the limitations of technology. 

Nevertheless, the advancement in technology in recent years will allow companies to address this issue. Thanks to cloud computing, analytics and artificial intelligence, plus greater clarity on the use cases, companies are starting to realise the benefits that digital tools enable from unprecedented real-time insights into their operations. Leveraging existing operational data and newly available data sources will provide great opportunities. By improving asset reliability, efficiency and safety performance, companies will significantly improve their business performance. For instance, a 0.1% increase in production due to improved process and operating efficiency can easily yield several millions of dollars in additional revenue. 

First steps to enable Analytics and A.I.

A digital twin is a complete 360-degree replica of a physical asset, such as pipelines, gathering systems, heat exchangers, turbines, pumps, compressors or entire plants. It enables analytics and AI to model and control the process while monitoring equipment health and reducing unplanned downtime. As the foundation of a digital transformation, it optimises production, detects equipment problems before failures occur, and uncovers new opportunities for process improvement. 

At the engineering phase, a 3D plant model allows multi-disciplinary teams to interact with the data visually. With the unified data-centric models, engineers are empowered with the ability to visualise the downstream impacts of their actions when they make design changes during the project execution. As a result, it eliminates information silos and reduces design cycles through improved collaboration and change management process. 

3D visualisation of assets using a single, integrated source of up to date, trustworthy information Source: AVEVA

 

An operations management user interface Source: AVEVA

 

A good illustration is the data-centric engineering and design platform deployed by Aibel that facilitates collaboration across multiple offices, enabling them to deliver the Johan Sverdrup offshore platform on time and under budget. Aibel is a leading service company within the upstream oil and gas industry. It delivers customized turnkey solutions for engineering, construction, modifications and maintenance, operating out of nine offices spread out across Europe and Southeast Asia. 

Next, these common sets of data and information are shared across departments, from engineering to procurement, construction, commission, and operations. 

Empowering human to better interface with Machine

As the operational life continues, the digital twin is automatically updated in real-time with current data, work records, and engineering information, to optimise maintenance and operational activities. Engineers and operators can therefore easily search the asset tags to access critical, up-to-date engineering information and diagnose the health of a particular asset. Previously, such tasks would take considerable time and effort, leading to issues being missed and increasing failures or production outages. With the digital twin, operational and asset issues are flagged and addressed early on: the workflow becomes preventative instead of reactive. 

A good illustration is the Abu Dhabi National Oil Company or ADNOC’s Panorama Digital Command Centre that provides operational visibility across the entire hydrocarbon value chain, from exploration to distribution of products, breaking down information silos, and providing real-time operational insights based on a single trusted view. This not only improves operational efficiencies but also uncovers new pathways to optimise performance. 

Combining Analytics and Artificial Intelligence

The real-time data processing from the digital replica can then be fed into analytics and artificial intelligence. The aim is to optimise overall production, process conditions and even predict failures ahead of time. The digital twin, when combined with powerful analytics and artificial intelligence, enables predictive maintenance and optimal operations. With advanced pattern recognition, statistical models and machine learning technology, relevant data is transformed into useful contexts with decision support. It empowers workers to make technical decisions on the fly to reduce unplanned downtime and optimise operating conditions. 

Augmented and Virtual Reality to visualise data

Augmented/Virtual Reality (AR/VR) combines data and visualisation to provide field operators with an augmented overlay view of the physical asset. This allows workers to quickly access step-by-step procedures for maintenance or training needs just by aiming the mobile tablets at the faulty equipment, without having to manually search for the relevant information. It greatly improves operational efficiency and production uptime.  

For instance, Italpresse Gauss has been transforming its operations with AR/VR to support remote maintenance and asset performance optimisation. They build machines and automatic work cells for light alloy casting primarily for the global automotive industry. Their high-fidelity virtual environment helps provide a safe and reliable environment for their workers to study, inspect, and test asset maintenance and optimisation strategies prior to implementation. 

The potential artificial intelligence and analytics hold for the oil and gas industry is not just hype. Real use cases demonstrate how leveraging advanced analytics, artificial intelligence and AR/VR for assets, processes and operational control not only enables oil and gas companies to maximise return on assets and improve operational efficiencies but also augments workers in their daily workflows to help get more done. 

Eddy Lek – AVEVA

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