The oil and gas industry, a complex and demanding realm that thrives on large-scale operations involving heavy-duty processing equipment, is in the midst of a titanic period of disruption. Overly complex international supply chains are leading to cost overruns, missed deadlines and failed quotas. It’s troubling, but not surprising, that 40% of new capital projects within the industry fail in terms of budgets and schedules, according to a study published by Booz Allen Hamilton.
At the root of these issues is a glaring reality: Oil and gas companies are overwhelmed with floods of data coming from expansive well sites, lengthy pipelines, high-tech equipment and sensitive gas-gathering systems. The quality of these data is not the problem; the problem is that the data tend to flow directly into silos that are beholden to outdated legacy systems. As a result, organizations experience an overwhelming lack of operational insights.
The average offshore production platform has 40,000 data tags, so it’s clear that the data flood is not going anywhere soon. Therefore, it’s crucial for oil and gas companies to learn why and how to use data to their advantage.
Leveraging the digital oil field
Every day thousands of wells are drilled and several miles of pipelines are laid, which constitute the oil and gas industry’s lifeline. However, like most of the infrastructure in the U.S., more than half of the country’s existing 4 million km (2.5 million miles) of oil and gas pipelines were constructed in the 1950s and 1960s.
This growing and aging network has become increasingly difficult to maintain, as corrosion has caused the condition of the pipes and associated equipment to decline. To make things worse, as operations become more remote and complicated, visibility into equipment health is considerably reduced, particularly in deepwater or offshore locations. Consequently, inspection becomes riskier and more expensive; moreover, the lack of visibility may result in unscheduled maintenance and downtime. Even worse, it could lead to oil spills or accidents triggered by failing equipment.
Maintenance on everything from trucks to tanks is a major cost and a major source of uncertainty for oil and gas companies. Even as organizations invest millions of dollars into upkeep and repairs, the risk of a spill or breach, along with the resulting fallout, looms large.
For an industry as cryptic as the oil and gas industry, the Industrial Internet of Things (IIoT) has far-reaching implications on its processes and operations. The most striking impact of the IIoT is that it helps give greater visibility into an entity’s operations, resulting in improved production, lower operating costs and enhanced safety.
A great competitive advantage lies in creating digital oil fields that are capable of producing and analyzing vast amounts of data in real time, and the IIoT makes this possible. Through a combination of connected technology and cognitive data science, companies can easily develop this capacity without making a significant investment in high-priced, hardto- find human capital.
The positive impact of this data-driven approach cannot be overstated, especially when companies concurrently adopt a methodology known as cognitive predictive maintenance.
Cognitive predictive maintenance is a way to leverage real-time data to take a smarter, safer approach to service and repairs. By analyzing data streams coming out of digital oil fields, it’s possible to predict when service is actually necessary rather than presumably necessary. This specific but substantial capability has the potential to slash costs, eliminate inefficiencies and boost safety throughout the industry.
Leveraging historical and real-time equipment data with the expertise of predictive analytics and machine learning can power smarter data-driven decisions, resulting in enhanced profitability, reduced operational costs, increased operational visibility and control, and optimized resource allocation.
Improved asset tracking and predictive maintenance can help oil and gas companies significantly enhance their efficiency. On offshore platforms, which generally witness high production volumes, even minor speedrelated improvements can have a considerable and direct impact on yield as well as savings. On the other hand, in the case of low-volume regimes such as oil sands, well-planned and precise operational changes can result in the enhanced operational and economic life of equipment, not to mention increased revenue. In fact, according to McKinsey and Co., enhancing production efficiency by 10% can result in a bottom-line impact of up to $260 million on a single brownfield asset.
The IIoT has the potential to transform this industry in a dramatic way, but this will be possible only if the volume of data is effectively leveraged by enhancing sensor connectivity. Also, real-time data must be monetized to increase the efficiencies alongside the oil and gas value chain.
Saving money, lives
Even as oil and gas exploration has become more high-tech, the industry still relies on massive machines operating in extreme conditions. Predictive maintenance is a strategy that is uniquely suited to address the challenges this volatile, mission-critical equipment faces on a daily basis, helping companies optimize the following realms:
Equipment health. Capturing and analyzing data from performance sensors reveals exactly how well something like a drill or a pipeline is performing. With a holistic view of the health of this equipment, it’s relatively easy to spot weaknesses before they translate into failures. For instance, an underperforming drill can automatically have its speed reduced so it continues to operate but does not destruct. The long-term value of all equipment is maximized.
Operational efficiency. Any site that is contending with equipment failure is losing money by the minute. Cognitive predictive maintenance reduces instances of failure while accelerating the time necessary to perform mandatory repairs. As a result, delays and downtime are largely eliminated. In fact, the U.S. Department of Energy estimates that integrating connected technology more broadly into oil and gas could lead to a 45% reduction in downtime.
Worker safety. Workplace accidents are disruptive and costly. Many of these accidents occur while workers are conducting routine inspections and improvements. Cognitive predictive maintenance promises to slash the number of incidents by eliminating the need for many in-person inspections and indicating when human presence is too dangerous. If and when there is a high probability of an accident, it’s apparent before anyone arrives on site.
Oil and gas companies are constantly trying to juggle safety, productivity and cost-effectiveness. Fortunately for them, cognitive predictive maintenance is an approach that improves each priority without calling for compromises.
Ultimately, a smarter approach to maintenance is good not only for business leaders but also for everyone who works in the industry or consumes its products. That is the definition of a win-win situation.
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