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Honeywell - supporting decision making in operations with better data

Tuesday, September 18, 2018

Staff in oil and gas operations need to make many decisions. Better data to help them make these decisions. Dan O'Brien and Bart Winters from Honeywell share some ideas on better ways to do it.

The oil and gas industry can significantly impact production and profitability when it has the ability to make fast, smart business decisions.

Oil and gas industry facilities must operate not only at desired capacity, but also at optimal efficiency. This requires a clear understanding of current operating conditions and performance, as well as the ability to detect undesirable process conditions and equipment issues before they occur, and then to systematically address them as part of a continuous improvement process.

Also, experience has shown that upstream operators can avoid critical asset failure, unplanned shutdowns, and improve bottom line results by implementing a connected asset performance management strategy.

Companies are able to use advanced data analytics to predict failures of critical pieces of equipment.

All of this needs data. So there needs to be systems for streamlining data collection, validation, surveillance and notification processes from field systems and engineering applications.

It also requires an integrated operations platform delivering both operational intelligence and field system and engineering application integration for improved operational performance.

What digital transformation means

For most industrial organizations, digital transformation means the following.

Access to key operating data.

Understanding operational models, KPIs, metrics, and performance characteristics to detect deviations between expected/predicted conditions and actual.

Event detection, prioritization, notification and workflow to take pro-active actions and intervene in time to minimize negative impact and eliminate surprises.

Visualization and analytics to promote awareness, increase insights, and accelerate decision-making.

Today's business challenges

Exploration and production (E&P) companies are faced with transforming vast amounts of data into useful information to support safer operations and increase the productivity of their business.

This requires new capabilities for integrating and associating data from diverse applications to conduct analysis and visualization that will lead to actionable insight.

Important questions for today's operating companies include:

How does your business currently measure productivity?

How do you make operational and asset decisions?

What types of decision-support tools do you use?

How are process and equipment data used to make informed decisions?

How quickly is real-time asset information available?

How do you track results based on asset performance monitoring?

Operational objectives

Oil and gas producers have a critical need to ensure essential safety procedures perform as designed, alarms are well managed and enforced, well performance is instantly available, and collaboration through remote operations is a reality.

The goal is to improve production and, at the same time, reduce the need for onsite personnel, which is particularly important when moving into higher-risk environments.

Specific operational objectives for upstream firms include the following:

Deploy online, continuous monitoring and exception-based alerts for process performance, equipment, and controls.

Capitalize on increased data availability across the enterprise.

Put data into context so as to compare assets to determine similar conditions or behaviour.

Implement tools for process and reliability engineers, enabling visual data exploration to decrease reliance on complex machine learning algorithms to solve problems.

Establish collaboration with both internal and external subject matter experts (SMEs).

Critical factors for results

Every upstream organisation has identified critical factors for optimizing its business results. They can include:

Production certainty - upstream firms seek to eliminate surprises in their production planning. They are focused on achieving more efficient and effective asset operations while reducing safety risks, increasing uptime, and making best use of resources. Well performance monitoring and data acquisition are essential for reducing production downtime and improving safety via faster identification of integrity issues.

Asset reliability - due to the criticality of upstream assets, it is important to identify potential failures and service requirements as early as possible. With the availability of real-time information regarding the health of production equipment, companies can shift away from older maintenance techniques toward condition-based and predictive maintenance methods.

Agile remote operations - managing a large amount of geographically distributed assets is a vital task for energy producers. Operators must be able to turn wells on and off as determined by production demand, however, there is concern about deploying humans to areas that are less than hospitable. At the same time, they have a requirement for integrated operations management applications providing an overall view into what is happening across all their production assets.

The mark of a successful operating company is its ability to reliably meet production targets and shareholder expectations. For example, this might require tools providing accurate information on whether an oilfield is producing at the level predicted by reservoir engineers, wells are producing the right mixture of oil and other products, production equipment is healthy enough to maintain uninterrupted operations, and the operator is receiving the right production allocation.

A connected ecosystem

Due to increasing competitive pressures, oil and gas firms are seeking to move beyond traditional operating strategies to a fully connected and flexible automation ecosystem - one that uses a constant stream of data from connected operations and production systems to learn and adapt to new demands.

The result can be a more efficient and agile production, less downtime, and a greater ability to predict and adjust to operational changes.

The automation industry is now leveraging the Industrial Internet of Things (IIoT) to deliver solutions comprising smart connected assets, enterprise integrated automation, secured cloud-based data and advanced analytics. These solutions employ real-time plant data with advanced software, analytics and plant process models to enable operational improvements and increase reliability.

For example, the latest cloud-based Supervisory Control and Data Acquisition (SCADA) systems allow multiple SCADA servers to operate as one within a single asset or across the enterprise, and enable seamless global access to points, alarms, interactive operator control messages and history. They can be used to operate production assets remotely, rather than sending technicians into the field for manual intervention.

Key to implementing a connected automation ecosystem is the use of a cloud-based, multi-asset model 'Digital Twin'. The digital twin unifies existing data silos into a virtual entity, federates data across different applications and edge devices to drive end-to-end integration, deploys process simulation technology beyond current scope of process design, and utilizes the cloud to overcome maintainability issues and enable third-party expertise.

Better decision making

Thanks to recent developments in real-time process performance monitoring, upstream operators have become significantly smarter and more responsive.

Problems that caused them to be less efficient or productive - and that went undetected for weeks or months - are now visible and resolvable quickly and proactively, and decisions that used to take days can be made in hours.

For operating companies, the avoidance of downtime and suboptimal performance, coupled with better efficiency and agility, and lower maintenance costs, can be worth millions of dollars per year in terms of reduced unplanned capacity loss and fewer lost profit opportunities.

Leading automation suppliers like Honeywell now offer connected asset performance management solutions that employ data from process and asset measurements to enable digital transformation, and in doing so, support key decision makers. They provide real-time digital intelligence through advanced process and event data collection, asset-centric analytics and powerful visualization solutions, turning plant data into actionable information to enable smart operations.

The new technology is designed to dramatically simplify data access and connectivity. It supports digitalization by standardising work processes, applying high-skill resources to high-skill tasks, providing a single version of the truth to enable process intelligence, reducing missed opportunities, and accelerating response.

By taking advantage of advanced digital twin technology driving the most effective monitoring, analytical, and predictive capabilities, process engineers can implement around-the-clock monitoring of plant data, and ongoing operational health checks and recommendations to close performance gaps.

Honeywell's asset performance management solution improves upon analytic approaches that solely rely on a statistical model to detect deviations from normal. Having a fundamental, physics-based model allows users to model and compare expected process performance against actual results and use these deviations as early indicators of health degradation.



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» Honeywell Process Solutions
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