November 2018

Trends & Resources

Business Trends

The oil and gas industry is at a turning point.

Newton, M., AVEVA

The oil and gas industry is at a turning point. Oil prices have witnessed new levels of volatility, causing producers to accept that this environment may be the new norm. While put in place for good reason, heightened regulatory compliance requirements are complicating this equation. Simultaneously, the industry is determining how to create and retain knowledge as its workforce undergoes one of the largest generational transitions in its history, risking business continuity.

In the midst of this environment, downstream producers are striving to improve performance by reducing energy usage and costs, and to reduce safety-related incidents, while optimizing yields and improving operator performance. As resources continue to be cut, how can downstream operators achieve these goals?

The answer lies in digital transformation. Technology is changing industries, and downstream oil and gas is no different. Pivotal technologies such as cloud computing, the Industrial Internet of Things (IIoT), digital twins, and augmented reality (AR) and virtual reality (VR) are changing the way companies do business for the better. However, the trick is ensuring that refineries make the right technology investments to meet these goals and use these technologies to enhance the customer experience. This article provides the information needed to make those critical decisions.

Benefits of digital transformation.

In the present economic environment, capital budgets and overhead are being cut. Simultaneously, energy costs are increasing and feedstock prices are shifting constantly, forcing manufacturers to monitor economic conditions and forecasts daily. It is imperative that manufacturers identify new ways to reduce energy costs across all facets of the business, including process design, implementation and production.

Digital transformation offers new tools that enable downstream producers to improve the yield of valuable products, while also reducing energy consumption and increasing throughput. Think of it as the analog to digital conversion. Taking what is going on in the physical world—a company’s physical assets—and converting it to digital information. Then taking that information and applying digital toolsets to the existing data that has been collected, creating the ability to determine what value can be extracted from this information. An example is the use of artificial intelligence and machine learning to spot anomalies in the operational behavior of critical assets that may indicate a future breakdown.

FIG. 1. Using digital technology, manufacturers can create a complete digital twin of the plant.
FIG. 1. Using digital technology, manufacturers can create a complete digital twin of the plant.

Using digital technology, manufacturers can create a complete digital twin of the plant (FIG. 1). The digital twin pertains to the asset and the operations life cycle, and how the two connect together to create the goods and services produced. It allows operators to take real-time process data and combine it with current economic conditions, which, in turn, enable them to make informed decisions and to evaluate what-if scenarios in batch processing and manufacturing at an expedited rate. Information sharing increases, enabling stakeholders to increase their ability to visualize results and key performance indicator (KPI) data across processes and overall plant production.

For example, among the digital tools, cloud computing specifically offers several opportunities to reduce costs in process design. Due to the nearly infinite processing power and storage available through cloud-based architecture, process design can accelerate, while also reducing capital investment costs for process modeling and training. This eliminates the need for data centers and the costs that go into operating them. Utilizing cloud computing, downstream producers can set up cloud-based servers and computing resources as needed, whether that be three servers one day and 15 the next. Through a cloud-based architecture for process design, information accessibility is increased, availability is enhanced, and total cost of ownership (TCO) is significantly reduced. As process design information is digitally stored in the cloud, it becomes easier to combine this information with other sources of data and to drive the enterprise toward a completely digital planning and operations model.

Through digital transformation, business units that have historically existed in a vacuum can now connect to each other in real time to create a unified supply chain model. With the ability to view all the data in one place, such as a combination of real-time economic and market data sources and plant and production status data, companies are able to maximize profitability. Rather than acting as disparate sources, internal teams become armed with the same information, making them better able to understand what drives their process and how to improve the customer’s experience.

In a unified supply chain model, planning and operations are fused together to make up the digital value chain. A complete 360° view of the digital value chain emerges, allowing all aspects of the enterprise to be visualized, analyzed and optimized—from demand to fulfillment and follow-up. Inputs to the enterprise, such as feedstock and raw materials, are then analyzed in real time against planning, operations, scheduling and distribution. Full plant models can also be managed simultaneously within a supply and distribution network. Fast optimization, combined with user-configurable visualizations and reporting, allows the impact of uncertainties and data changes to be evaluated and understood as they occur. Feasible and robust schedules are then brought to the forefront to guide business decisions that are not only easier to understand, but also easier to explain across the entire enterprise.

A new workforce and workplace.

At present, there is a new generation of mobile-enabled workers, a new regulatory environment and new technology. All are shaping the way industry does business. As a generation of highly experienced refinery operators begins to retire, it is imperative that oil and gas companies determine the safest and best way to train their replacements as fast as possible. At the same time, training budgets are being cut, increasing the need to deliver safe training through a more economic vehicle. Digital transformation offers several options to address these challenges.

For example, VR and/or AR are digital toolsets capable of connecting control room operators and maintenance and field personnel in a single, realistic learning environment. In life-like, 3D environments and high-fidelity, dynamic simulations, operator training is accelerated across several categories:

  • Equipment understanding
  • Hazard and operability study (HAZOP) design evaluation
  • Operator training simulator (OTS) programs, including procedure training, safety scenarios and crew training
  • Maintenance planning and execution
  • Real-time enterprise asset management.

These AR and VR training solutions offer minimized project risk by preventing delays during plant commissioning and startup, while maximizing return on investment (ROI). Mobile technology enables workers to collect data throughout their daily rounds, rather than collecting information on paper and reporting later. It also enables a remote workforce that can connect virtually. People can perform their duties from wherever they are (directly from their handheld device), which enables them to access a digital twin of the plant—thus increasing uptime and reducing operating and maintenance costs.

Mobile technology can also help companies comply with safety, health and environmental (HSE) regulations, which is a paramount focus in the downstream processing industry. Many plants operate under a “zero accidents” culture—the idea that all accidents can be prevented through policy and procedure adherence. Many factors contribute to a good safety culture, including skilled employees, continuous safety risk assessment (HAZOP), and good practices and standards, alongside primary and secondary process safety.

Mobilizing the operator workforce helps ensure that regulatory compliance is consistently met. Anything that was previously recorded on paper (such as repair procedures and audit logs) becomes digital as workers log their maintenance and operations records daily via their mobile devices. These records are automatically stored in a central location and backed up to the cloud, and, additionally, regulatory audit trails are automatically generated.

Technology is also changing the way plants and refineries operate as a whole, offering new ways to capture, share and store information across the enterprise. Since capital expense constraints limit investment in new equipment, companies are turning to asset performance management (APM) solutions. APM combines enterprise data capture with asset management, advanced workflow, mobility, predictive analytics and risk-based management to drive maximum return on asset investment through reduced downtime, lowered TCO, decreased maintenance costs and improved overall equipment effectiveness. This allows operators and maintenance engineers to automatically generate work orders based on data reported from predictive models, allowing potential equipment failures to be addressed before they occur. Advanced data capture and analytic capabilities provide detailed insight into the health of production assets, avoiding unplanned downtime.

An ARC Advisory Group market study on common failure patterns1 found that 82% of failure types are random. Only 18% are predictable and preventable with traditional maintenance methods. Machine learning helps identify inefficiencies and abnormalities in equipment operation long before regular inspection. Engineers can reference operational models and digital twins for recent abnormalities in design vs. operational performance. As data is collected at an operational level and fed into other digital toolsets, it is imperative that oil and gas enterprises make the right digital technology investments.

Technology investment.

Choosing the right technology investment requires analysis of four key technology pillars to ensure successful digital transformations and optimal ROIs:

  1. Comprehensive value chain: Technology investments must enable the digital integration of engineering, planning and operations, control, visualization, information and asset performance solutions to create a 360° view, from the shop floor to the top floor.
  2. Open and system agnostic: Interoperability and cross-platform support accelerate a path toward continual process improvement. Rapidly sharing big data and insights across multiple platforms (including cloud, mobile and AR/VR) requires open, systematic, agnostic technology solutions that augment (rather than rip and replace) existing asset investments. An open, systematic, agnostic approach to digital transformation drives long-term value and lowers TCO.
  3. Digital ecosystems: Technology investments should be backed by a multidisciplinary ecosystem of technology partners with capabilities of design, development, delivery, maintenance and support of industry-specific solutions on a global scale. Ecosystem partners may include software developers, technical distributors, system integrators, original equipment manufacturer (OEM) providers and
    technology partners, all focused on extending value and driving innovation across industries.
  4. Flexible and agile implementation: Adapting to unforeseen events becomes automatic through flexible technology implementation options. True digital transformation platforms provide the ability to choose between deployment options, including on-premise, cloud or hybrid rollouts. Additionally, agility in procurement options allows enterprises to obtain the required tools through several options, including perpetual licensing or subscription-based services. Solutions for implementing technology on an as-needed, staged approach help the enterprise reduce upfront costs and decrease time to value of new technology investments while accelerating a path toward increased profitability.

Digital transformation is a data-driven approach to continuous process improvement. Although it harnesses digital technology, it still requires the buy-in of workers, as well as the collaboration of processes and assets. Eventually, processes and assets will collide to bridge the gap between operations technology and information technology.

However, the larger piece of this puzzle is determining what customers are gaining by partnering with your company. With the array of technology disruptions that have already taken place, downstream companies need to ensure that they are shaping the customer experience in a different way than their current or future competitors. A good example of this disruption at work is the story of Netflix and Blockbuster. Netflix came out of nowhere, leveraging new technology and delivering a customer experience that was miles ahead of anyone else in the industry. Ultimately, Netflix was the reason Blockbuster went out of business. However, could Blockbuster see this coming? In such a turnkey, changing time, there are many industrial businesses that are concerned about this type of disruption catching them on their heels.

To prevent such a disruption, start by taking a look at your company and asking:

  1. What does our customer experience look like?
  2. Are we a commodity or are we a value-added partner?
  3. Can they get the same product from someone else?

Digital transformation does not happen all at once. The key is getting started now. Remember to start small in your strategy and adoption, but to give yourself a competitive edge. Start now to maintain or improve your competitive market and industry position. HP

LITERATURE CITED

  1 Rio, R., “Proactive asset management with IIoT and analytics,” https://www.arcweb.com/blog/proactive-asset-management-iiot-analytics

The Author

Related Articles

From the Archive

Comments

Comments

{{ error }}
{{ comment.comment.Name }} • {{ comment.timeAgo }}
{{ comment.comment.Text }}