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In today’s market climate companies operating in the oil and gas industry should take advantage of the proliferation of sensors, big data and analytical techniques, coupled with the advancements in asset performance modelling, reality modelling, and 3D visual operations.

The oil and gas industry has always strived to reduce costs, improve performance, and eliminate risk. The last few years however, have seen the community hit by a series of blows with serious consequences. With declining oil production, plummeting oil prices and a dramatic cutback in spending, things appeared bleak. Yet there are still technologies, processes, and applications available that allow producers to innovate and become agile and forward-looking, even in tough times. Instead of waiting for oil prices to recover in order to start spending, some oil and gas CIOs and COOs are working together to take advantage of digital advancements to help them achieve operational excellence.

Integrated Planning and Operations

Ideally in any business, efficiency and performance can only be improved if everyone works together, but this is sometimes harder in practice than it first seems. In the oil and gas industry, it would be ideal if exploration were in touch with what is happening in production; if production were working with operations; if operations were synchronizing with the inspection team and so on. In other words, a seamless connected environment used to manage different business functions and pinpoint inefficiencies.

For instance, Emerson Process Management, a supplier of products, services, and solutions that improve process-related operations across many industries, created a data control centre that brought together groups from disparate departments across multiple locations to one central hub using state-of-the-art technologies around communications, conferencing, analytics, and more. The integrated operations centre is called iOps and leverages Bentley’s AssetWise Operational Analytics application. The iOps centre helps clients simulate business conditions and make decisions in a matter of minutes rather than days or weeks. The predictive operational analytics software on the connected servers provides the centre’s clients with an array of readily-mapped inputs within user-driven dashboards for full visibility.

Operational Performance Monitoring Creates Clarity

With the Internet of Things (IoT) adding more to a world already overloaded with data, from wearable technology to self-driving cars, asset-intensive industries are quickly jumping on board and embracing this revolution. With so much data flying around, the problem is how to capture it and use it to your advantage. Applications that help optimize the use of assets give user groups the ability to see a holistic view of the day-to-day running of the operation, from production performance against targets, KPIs, chemical usage and spend, corrosion levels, maintenance plans, inventory and more. It also gives the operational or integrity management leader the information and support they need to make the right decision.

More importantly, engineers won’t need specialized data analysts to carry out analyses. Saving time on data manipulation is based on any user, from a wellhead engineer to a CFO, creating dashboards tailored to what they want to see for their role, while the embedded analytics does the job for them in the background.

Reduce Costs and Operational Risk with Improved Asset Reliability and Integrity

Although spending has been drastically reduced, cost reduction and risk are still at the top of any CIO’s list. Now the focus is on the remaining assets and optimizing them to gain the best performance, reduce failures, and maintain availability. Key to this is reliability and maintenance to extend the life of aging assets safely and reliably. Having a programme in place for inspections, maintenance, integrity, and performance will significantly reduce risk and associated operating costs, while increasing the life of assets that would normally be replaced. 

Asset reliability and integrity management cover a wide variety of areas, from risk-based inspections analysis and safety integrity management to condition-based monitoring with dynamic measurement points, asset health indexing, and, in the same system, reliability-centred maintenance, maintenance task analysis, and root cause analysis. With these strategies in place, it is easier to identify and predict the failure of assets that pose the greatest risk to the operation. Identification leads to controlled and proactive inspection and maintenance practices that are crucial in running an efficient and productive operation.

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Incorporate asset design data with designs to help risk based inspections.

Deliver More Insight with Advanced Analytics

Advanced analytics provides more depth to the levels of insight by doing more complex analysis from wider sources of data, as well as visualization and data mining. With the power to predict, it adds an extra level to not only what happened and why, but when will it happen again. For instance, predictions can help with calculating production against chemical costs, the corrosion levels of a pipe in terms of when it will need replacing and the subsequent maintenance required. The next logical step from this form of logic is prescriptive analytics – not only predicting an outcome, but also what is the best action to take next with the most advantageous outcome. With machine learning, this becomes a reality.

Machine learning involves doing the tasks engineers perform but with the ability to make the right decision from a variety of options. By using historical condition data from assets (corrosion, vibration, and so on) as well as current conditions (for example, temperature, pressure, turbidity), machine learning can sort through large data sets and identify patterns or connections, predict outcomes based on knowledge, and make predictions and recommendations to decision makers on the best course of action to take.

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Display all of your operational data and more on a single dashboard and explore deeper.

Asset Lifecycle Information Management Leads to Agility and Value

With so many assets producing so much data around the world, this information can be used to provide much more insight into the performance and efficiency of any operation. What is often forgotten is the information on the assets themselves. Asset lifecycle information management provides structured control of asset information and managed change from cradle to grave, ensuring engineers, maintenance and operations always have accurate information.

Knowing what and where assets are located exactly, what condition they are in, how they are performing and what is their remaining life is critical to knowing you have control over your operation with line of sight. Maintaining a seamless information stream between asset data, documents, organizations, requirements, people, and processes; asset lifecycle information management assures information integrity. For example, BP sees asset information becoming increasingly important and valuable within the business. By using cutting-edge technology solutions BP expects to provide new understanding of how to derive value from data. This will help drive standardisation and project execution efficiency that will be driven out across the enterprise rather than each project working on their own.

The Digital Transformation Has Already Started

Advances in analytics, generated data, and hardware can lead to significant advantages across all areas of the oil and gas spectrum when it comes to leading the digital innovation charge, culminating in the ‘digital oilfield.’ Key to this is the convergence of operational technology (OT) and information technology (IT) for improved decision-making. While this is a step forward, converging engineering technology (ET) will provide more significant improvements to asset performance. With asset-related information linked to the digital engineering model, it facilitates efficient modifications and renovations. Before and during design, functional definitions and requirements define expected asset behaviour. With model data being used more often in the oil and gas industry, often in separate locations, it makes sense to incorporate them into the whole system for improved visibility.

Engineering data (models that are in the form of networks, schematics, catalogues, 3D designs, and so on) are not a static view, but can be a continuously evolving living thing, as assets change over time in terms of functions and repairs. Engineering modelling data is the ‘digital twin’ of the physical asset. These digital representations of the physical asset allow producers to understand, predict and optimize the performance of their assets and their business. With real-time information from the OT laid on top of the models, they can then navigate and display all information relating to that asset or process, only recommending an engineer for inspections purposes if necessary, and all done remotely. The true value of convergence lies in reduced asset downtime and maintenance costs, which will only become more accelerated with the inclusion of machine-learning technology.

You don’t have to wait for an upturn in demand and oil prices. Change can only take place if you embrace the technology that is already out there and available.