The oil & natural gas sector is one of the fastest-growing and demand-driven industry. It has witnessed tremendous expansion in terms of technological advancements. Despite the demand for cleaner energy sources, the crude oil and natural gas continue to be preferred by a large number of countries. Major governments and private players are looking into emerging technologies like data analytics, the internet of things, artificial intelligence, and machine learning for enhancing the industry on the whole.
Around the globe, sensors from manufacturing sites are collecting billions of data values every day. The companies are creating globally scaled ‘data lakes,’ allowing its operators to collect and read up to 1 billion bits of data every minute from around the world. These sensors at the refinery and manufacturing facilities provide real-time data, such as temperature and flow rates, which help operations run more efficiently and reduce emissions. These billion little sensors are being used to improve billions of lives across the globe, building a pathway for the Earth’s energy future.
The influx of the information gathered from these sensors and analyzed by high-power computers has been dubbed ‘big data,’ and is being utilized to aid decision-makers, change how businesses and governments operate and improve environmental and energy outcomes. Technology centers in Kuala Lumpur and Bengaluru are quickly migrating from primarily providing enhanced data visualization to now also providing modeling and predictive analytics expertise.
At ExxonMobil’s Permian Basin oilfield in the US’s south-west, data analytics is expected to help the company add an extra 50,000 barrels of oil a day to production by 2025.
A recent survey by Accenture and Microsoft of oil companies found that approximately 90% of respondents believe that increasing their analytical, digital, data, and Internet of Things capabilities would increase the value of their business. It also found that over the next 3–5 years, investment in big data and automation is expected to increase from 56% to 61% and 53% to 65%, respectively. The search for new hydrocarbon deposits demands a huge amount of materials, workforce, and logistics. Since drilling a deepwater oil well generally costs over $100 million, companies don’t want to look in the wrong place.
Big-data analytics is already optimizing the subsurface mapping of the best drilling locations. It is also indicating how and where to steer the drill bit, determine section by section, the best way to stimulate the shale and ensure precise truck and rail operations.
Oil and Gas industry can be broadly divided into three sectors – upstream, midstream, and downstream.
Upstream sector
In the face of big problems like the rising cost of extraction, the key players are have turned to big data for finding solutions to these pressing issues. Royal Dutch Shell has been developing the idea of the “data-driven oilfield” in an attempt to bring down the cost of drilling for oil – the industry’s major expense.
For Hydrocarbon creation:
The essential assignment in oil boring is hydrocarbon investigation, which expects organizations to find oil and flammable gas underneath the Earth's surface. Topographically, layers of rocks shift across locales, despite the fact that they might be comparative basically. By utilizing the capacities of self-governing bots and automatons, industry players need to supplant people while getting to extraordinary conditions or high-chance areas. The exercises which are being gained from one zone are being applied to comparative regions.
Example: Shell utilizes fiber optic links (made in a restrictive association with Hewlett-Packard for these sensors), and the information is then moved to its private servers managed by Amazon Web Services (AWS). Information originating from this gives an unmistakably increasingly exact plan to specialists of what lies underneath and spares a lot of time and exertion.
ESP monitoring:
Most offshore pumps use electric submersible pumps (ESPs), which is one of the most proficient methods for extricating oil from profound inside the Earth. To advance its working, the item is combined with AI and cloud for ongoing checking of these ESPs, that is for the most part introduced in unforgiving condition and are inclined to erosion from seawater and profound water pressure.
Example: Siemen’s predictive maintenance solution called AI4ESP has been designed specially to monitor ESPs remotely. By facilitating real-time analysis of data, the product is able to provide a digital map of ESP operations, effectively creating smart pumps in a digital oil field, increasing the efficiency and reducing the cost.
Optimize drilling processes:
One way to optimize drilling processes is to tweak prescient models that forecast potential equipment failures. As a beginning stage, the hardware is fitted out with sensors to gather information during drilling activities. This information, together with the hardware metadata (model, operational settings, and so forth.), is gone through AI calculations to recognize utilization designs that are probably going to end in breakdowns.
To survey and monitor oil exploration areas:
The company employs a seismic analysis to study the area and indicate whether the given area contains oil and gas deposits. The more sophisticated big data analysis allows understanding the nuances of a particular drilling site before deciding to drill.
Midstream sector
Logistics in the petroleum industry is incredibly complex, and the primary concern is to transport oil and gas with the lowest risk possible. Sensor analytics is the key to ensure the safe logistics of their energy product. Predictive maintenance software investigates sensor information from pipelines and tankers to identify variations from the norm (weakness splits, stress consumption, seismic ground developments, and so forth.), which allows preventing accidents.
Reservoir management:
With the reservoir forming the core of oil and gas production, the degree of maintenance and optimization that it requires is very high. The data from the tanks equipment and integration of various instruments on and surrounding the facility, including information on geology, reservoir engineering, production techniques can be used to feed the AI systems that can improve the functioning of reservoirs. Fuzzy logic, expert systems, and artificial networks are used to characterize the reservoir for optimum production output accurately.
Downstream sector
Oil and gas enterprises can employ data analytics, artificial intelligence, and machine learning to reduce downtime and maintenance costs of the refining equipment, thus improving plant management. As an initial step, the performance of the machine is analyzed by comparing its historical and current operating data. Based on the device’s end-of-life criteria and failure conditions, the performance prediction is tuned further. Finally, the estimated performance of the equipment is visualized and presented to maintenance specialists for them to make decisions regarding replacing this asset.
Automating tasks
This industry is highly labor-intensive, and these workers are employed in a very dangerous condition. Artificial Intelligence and Machine Learning can automate 60-90 percent of the routine tasks while following all the best practices.
It is estimated that the industry could save as much as $50 billion in the coming decade, just by deploying AI and ML solutions.
Production Optimization
As oil pricing is highly fluctuating, the need for companies to optimize their production is essential. It is managed through enhancing an oil well’s life, which is affected by instrumental factors like flow rates and pressure etc. Data from numerous sensors and other devices gives a real-time status of the plant. Employing AI and ML will run the plant at an optimum operating environment.
To forecast production
Shell installs optical fiber cables with sensors within the wells to measure seismic data. This data is further analyzed using artificial intelligence technologies to create 3D and 4D maps of the oil reservoirs to find out how much oil and gas is still left in the tank.
To cut net carbon footprint
All the companies support the vision of a transition towards a net-zero emissions energy system. One way these companies have planned and implemented to reduce emissions is to use carbon capture and storage technology empowered by big data software.
It is estimated that analytics can help in improving production by 6%-8% in the oil refining plant.
The gradual transition towards big data implementation may not be easy for many oil companies since many lack the workforce and capabilities for hiring the required personnel that can handle big data. In a study, it is found that only 4% of the companies across the industry have the workforce with the needed skillset to draw tangible business value from analytics.
Personal and cybersecurity also need attention since this remains a perceived barrier in realizing the value of big data analytics. Big data real-time analytics undoubtedly presents innovative opportunities to establish more efficient oil production, cost and risk reduction, safety improvement, more regulatory compliance, and better decision-making.
Good expertise and strategic prudence while using big data tools, will not only ensure success but will also reduce the margin of error, increase efficiency, and increase the bottom line of the company.
Digital transformation and analytics serve the interconnection between these elements of the business, which can be examined and monitored in detail. New models can be built and simulations created by analysts, to explore how minor tweaks to a specific area of operations can have a big impact on the productivity or efficiency of another. The vast amount of data collected from all areas of the company’s activity means the result of the simulations will hopefully be as close as possible to the way things will play out in the real world. Ultimately this leads to decision-makers being better equipped to make the decisions that affect the company’s fortunes.
References:
- https://energyfactor.exxonmobil.asia/news/a-quantum-leap/
- https://www.forbes.com/sites/bernardmarr/2015/05/26/big-data-in-big-oil-how-shell-uses-analytics-to-drive-business-success/#296af7f2229e
- https://analyticsindiamag.com/five-ways-ai-advanced-analytics-is-used-in-the-oil-gas-industry/
- https://www.ibm.com/industries/oil-gas/resources/cognitive-analytics-infographic/
- https://analyticsindiamag.com/optimizing-drilling-real-time/
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