IIoT use cases in the oil and gas industry
An industry in turmOil
The oil and gas industry (O&G) is riding out a violent wave that crashed crude oil per barrel prices to less than $40, down more than 60% from the high of summer 2014. The result is a favorable receipt at the gas pump for consumers, but massive losses for oil and gas producers. In fact, in the third quarter of 2014, when per barrel oil prices were more than $100, major gas producers posted a total net income of $22.9 billion, according to Bloomberg. It had disappeared by the end of 2015.
An increased supply and lower demand has some countries in the Organization of the Petroleum Exporting Countries (OPEC), including Venezuela and Brazil, finding themselves in financial and political turmoil. For oil and gas companies, decreased earnings have resulted in a number of bankruptcies, layoffs, and investment cuts.
Many experts believe the prices of oil will remain low for years, so to make up for the lost revenue, O&G producers are looking for ways to decrease production costs. One way to do so is by investing in the internet of things. According to a Business Insider report:
- Over the next three to five years, 62% of oil and gas executives worldwide say they will invest more than they currently do in digital, according to a recent Microsoft and Accenture survey.
- Oil and gas companies will use IoT devices and their associated analytics to survey land for new potential drilling sites and extract the oil from the ground. Among oil and gas executives, 89% believe they can leverage analytics to improve business practices, according to Microsoft and Accenture.
- Business Insider estimates the number of devices used on oil extraction sites — primarily wells — will increase at a 70% compound annual growth rate (CAGR). The devices will primarily be internet-connected sensors used to provide environmental metrics about extraction sites.
- By fully optimizing the IoT solutions available, an oil and gas company with $50 billion in annual revenue could increase its profits by nearly $1 billion, according to a Cisco study.
Here are five use cases for how smart devices can bring many of the richest companies into the future.
Optimizing well and field work
Data collection is the single most important reason for why an oil and gas producer would implement an IoT solution. With the production of millions of barrels of oil per day from rigs around the world, gathering and organizing data has never been more important. By some estimates, internal data generated by large integrated O&G companies now exceeds one-and-a-half terabytes a day. Being able to harness and use that data increases the efficiency of workflow, supply chain, and people management, among other things.
The key to organizing big data is by offloading it onto the cloud, so hardware won’t get weighed down. The cloud is a great place to hold information because it can be easily accessed by software that can run it through a number of analytic processes.
“Small oil and gas companies are still running off of Excel,” said Shantanu Agarwal, a partner at Energy Ventures at TiEcon 2015. “They have to move to cloud. They have to get to those new business systems. These guys need adoption.”
Gathering data is important, but getting results can’t be had without a way to smartly store and analyze that data. That’s where the cloud comes in. With a connection between sensors and the cloud, information can be stored and sent worldwide so analysts can assess current operations. That added visibility and insight allows O&G companies to seemlessly connect their massive operations.
Last year, Royal Dutch Shell realized a $1 million return on an $87,000 investment in digital technology to monitor oilfields in some of Nigeria’s toughest terrain. Shell chose random phase multiple access technology to do so. Ingenu CEO John Horn said Shell needed just eight access points to cover its oil field footprint in Nigeria. The $87,000 investment covered the cost of the equipment and its installation. A year after the equipment was deployed, Shell had saved $1 million by reducing site visits and expediting data collection.
Exploring the surface and sub-surface for oil
Trial and error is money thrown away. That’s why using robots and sensors to analyze surface and subterranean environments could save millions of dollars. Seismic nodes collect large amounts of data that can be used to determine sites for oil deposits. Sensors can also collect data on surface materials, temperatures and how equipment preforms in different environments. These readings all help oil producers find new hydrocarbon deposits, determine new spots for drilling, and even find ways to optimize already-operational rigs.
The search for new hydrocarbon deposits demands a huge amount of materials logistics with deep water oil well costs at more than $100 million.
Surveying potential sites involves monitoring the low frequency seismic waves. Probes are put into the earth at the spot being surveyed, which register if the waves are distorted as they pass through oil or gas. This normally means taking a few thousand readings during the typical survey of a potential drilling site. In the past few years that number has jumped to more than a million – increasing the accuracy during exploration.
A good example of a successful implementation for mapping drilling sites is Shell’s use of fiber optic cables. The connected sensors gather data and transfer it to private servers maintained by Amazon Web Services. This gives a far more accurate image of what lies beneath, and can be compared to hundreds of other models.
Once a company finds a good place to drill, they can use analytics to reduce lag time and decrease the number of wells in progress at one time.
Using data to monitor equipment can both save millions of dollars and ensure a safer environment around drill sites. Sensors can continually monitor for any anomalies in the drilling process, and communicate its finding with the maintenance team. Video cameras can also be installed in areas with potentially higher risk of drilling. With a stable network those cameras can send their feed to rig operators.
The savings potentials are immense, avoiding the approximately $10 billion lost due to fuel leaks and thefts in the United States alone.
More efficient and effective maintenance can also avoid shutdowns. According to a report by Deloitte University Press, there were more than 2,200 unscheduled refinery shutdowns in the United States alone between 2009 and 2013, and each of those shutdowns cost global process industries 5 percent of their total production, or $20 billion per year. Ineffective maintenance practices also result in unscheduled downtime that costs global refiners on average an additional $60 billion per year in operating costs.
Remote performance monitoring
It has been estimated that only 1% of the information gathered is being made available to O&G decision makers, according to the Deloitte University Press. Increase data capture can boosting output by as much as 10 percent over a two-year period, that is millions of dollars saved. And if it hasn’t been stressed enough on what money scale the oil and gas industry runs on, an industry-wide adoption of IoT technology could increase global GDP by as much as 0.8 percent, or $816 billion during the next decade, according to Oxford Economics.
The collection of data in terms of performance and the organization of that data can lead to massive increases in production. The ability to compare an oil rigs production with that of another allows producers the ability to find maximum outputs. The metrics taken into account: pressure, temperature, and a number of other things can all be accurately tracked and compared between systems.
The opportunity to automate thousands of wells spread across regions (a large company handles more than 50,000 wells) and monitor multiple pieces of equipment per well (a single pump failure can cost $100,000 to $300,000 a day in lost production) makes production applications for IoT of great importance.
Now that performance is being tracked, predicting becomes much easier. Predicting operations in the oil and gas industry can influence where and how companies deploy their expensive infrastructure.
It can also help engineers map changes in reservoirs over time to determine whether changes need to be made with lifting methods.
A good example of a company taking advantage of predicting production is Apache, a U.S.-based exploration and production company, which collaborated with an analytics software firm to not only improved the performance of its electrical submersible pumps but also developed the ability to predict a field’s production capacity.
It goes without saying, it is better to know if something is worth the cost before throwing money at it and hoping it sticks.