Artificial Intelligence in Upstream Oil and Gas: Top Use Cases and Benefits
ENERGY AND RESOURCES | 8 min READ
    
Digital Transformation in Upstream Oil and Gas
Digitally transforming the upstream oil and gas industry promises huge potential for businesses in shortening the cycle leading to better prospects. A recent survey from EY has confirmed that 58% of industry O&G industry leaders believe COVID-19 has made investing in digital technology more urgent. Using the cloud, IoT, and AI for integrating subsurface wells and facilities brings abundant opportunities in their cumulative management, just as it does in the drilling sector. This ensures a coherent environment for constructing various AI use cases in oil and gas industry and leaves room for re-inventing them if need be.
Sanjay Bajaj
Sanjay Bajaj

SVP and Delivery Leader

Energy & Utilities

Birlasoft

 
Top Challenges in Upstream Oil and Gas Industry
In the post-Covid-19 era, by 2026, global oil consumption is expected to reach 104.1 mb/d — an increase of 4.4 mb/d from 2019. Given that, the upstream oil and gas industry is currently plagued with the challenge of increasing energy output with reduced emissions (given the sustainability regulations) while dealing with highly volatile prices.
Artificial Intelligence in Upstream Oil and Gas
The upstream segment of the oil and gas industry is extremely capital intensive and comes with enormous uncertainties and challenges. Amid a growing pressure to cut operational costs, big oil is looking to automate its processes using artificial intelligence (AI) to predict equipment failure and ensure no pitfalls in the production environment. Factors such as these have led to Gartner declaring AI to be one of the top game-changing technologies in 2021.
Top Applications of AI in Upstream Oil and Gas
Let's now explore the existing applications of AI in upstream oil and gas and analyze the possibility of further development. As we advance will also outline the recent trends in developing AI-based tools and explore their impacts on reducing the risk and accelerating the industry at large.
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  1. Exploration
    1. Seismic Image and Surveying
    2. Exploration Tank Inspection
  2. Drilling
    1. Precision Direction Drilling
    2. Salt Deposition Risk Monitoring
  3. Extraction
    1. Production Optimization
    2. Extraction Pipeline Inspection
    3. Extraction Tank Inspection
    4. Gas Leak Detection
    5. Vessel Maintenance and Monitoring
    6. Refinery and Plant Inspection
    7. Extraction Pump Monitoring
    8. Oil Well Life Prediction
#1 Exploration
#1.1 Seismic Image and Surveying
Exploring oil and gas reservoirs requires 3D imaging of the field and processing the data acquired from petrophysical and geophysical studies, including seismic surveying at the scale of the reservoir. AI reduced the time taken to process these 3D images, an otherwise time-intensive process.
Furthermore, the 3D images (or seismic cubes) are studied and segmented by experts, and the whole process can take more than a year to conduct an accurate seismic study. Using modern deep learning-based pattern recognition techniques makes it possible to accelerate the interpretation process by a factor of 10-1000.
#1.2 Exploration Tank Inspection
Potential cracks or corrosions in the exploration tank can have disastrous repercussions on the environment and society. To prevent this from happening, industries are now deploying AI-powered crack detection robots that inspect the welding site for cracks by moving a pipe connected through a welding joint in the tank. These robots are also being deployed to inspect the pipeline for intelligent pipeline monitoring, even though they need experienced engineers to manipulate them.
Artificial Intelligence in Upstream Oil and GasArtificial Intelligence in Upstream Oil and Gas
#2 Drilling
#2.1 Precision Direction Drilling
Reinforcement learning (a form of semi-supervised ML) is incredible new way organizations are leveraging AI to control their drilling equipment. It works by training a machine learning model based on simulations of steering the drill into the subsurface and historical drilling records. Furthermore, it brings the sensor data from seismic surveys and other important factors like pressure, temperature, etc.
Later, the drilling machine engineer can input arguments to penalty or reward functions to help the machinery adapt to changing operating conditions, thus reducing gas extraction costs.
#2.2 Salt Deposition Risk Monitoring
Unmitigated salt depositions in hydroprocessing effluent trains and FCC and crude fractionator overheads can cause numerous operational hiccups, including safety and economic risks. Manually formatting and analyzing data from these unaligned systems can take hours. One solution to this problem is to employ deep learning for semantic segmentation while interpreting salt deposits on seismic images.
This makes it easier to calculate salt deposition temperatures and compare them with safety margins. Various AI tools are now available that provide this information in real-time.
#3 Extraction
#3.1 Production Optimization
By leveraging technologies like the internet of things (IoT) and AI, we can now address the growing challenges in optimizing oil and gas production. For instance, AI is highly resourceful when weeding out false alarms and making accurate predictions based on various modeling approaches using recent trends.
Furthermore, AI comes in handy while forecasting electrical subversive pump (ESP) operational issues and developing a predictive ESP maintenance model. For demonstration purposes, real-time production data can be transmitted into an ML-based predictive maintenance model from SCADA systems using secure IoT connections over the cloud.
#3.2 Extraction Pipeline Inspection
One of the most common methods for non-destructive testing of oil and gas pipelines is the magnetic flux leakage (MFL) technique which looks for defects and anomalies in the extraction pipe wall. However, this method is quite difficult and involves highly complex MFL image analysis. Modern machine learning techniques using kernelization, support vector regression, principal component analysis, and methods for reducing feature space dimensionality offer a highly promising outlook in this respect. The models still use MFL data, but the performance is adequate for detecting defects and assessing their severity.
#3.3 Extraction Tank Inspection
Drones autonomously inspect offshore infrastructure like extraction tanks using LiDAR for navigation and sending the live feed for real-time AI-powered analysis. According to some tests, AI tools have detected cracks in oil tanks as high as 19.4 meters. This is a huge enabler for oil and gas companies higher up in the supply chain. Manual extraction tank inspections can cost them hundreds of thousands of dollars since the tank is taken out of service for days to construct scaffolding and ventilate.
#3.4 Gas Leak Detection
Pioneered by leading laboratories, we have tools that use sensors coupled with machine learning to quickly and successfully detect accidental gas leaks. Gas leak detection sensors now offer heightened sensitivity for ethane and methane gases and, with a power neural network, can detect accidental gas releases well in time.
Another method for detecting gas leaks is using multimodal AI-powered sensor fusion. It requires testing a thermal camera and semiconductor gas sensors array for thousands of gas samples across multiple classes to detect consistent clusters of gas readings in real-time.
Artificial Intelligence in Upstream Oil and Gas
#3.5 Vessel Maintenance and Monitoring
AI has a huge role in enabling the supply chain management of the oil and gas industries. One of its many applications includes vessel maintenance, tracking, and monitoring. Through AI-powered tools, businesses can track deliveries to handle demand from end consumers and inventory management. In addition, companies are now signing long-term agreements with AI solutions providers to deploy tools like lubricant condition monitoring systems. This reduces fuel consumption and, by extension, greenhouse gas emissions from complex offshore operations.
#3.6 Refinery and Plant Inspection
Autonomous drones are being deployed to reduce the risk to plant and refinery personnel's health and safety. The infrared sensors on these drones are highly sophisticated and are used for plant equipment and ground surveillance. When coupled with AI-powered smart inspection tools, the system also improves product lifecycle management, reducing time and costs using cognitive analytics and predictive asset optimization. The collected data is later used to eliminate forced outages and disruptions in the supply chain.
#3.7 Extraction Pump Monitoring
Glitches in the extraction pump can cause major disruptions in the upstream oil and gas industries. For instance, in 2019, a faulty well pump at an unmanned platform in the North Sea recurringly disturbed the production for Aker BP. The solution they deployed was an AI program for monitoring data from sensors on the pump. The tool ensured the production remained undisrupted thereon by flagging glitches before failure. This brought together the best of IoT and AI to enable equipment and pipeline operators to preempt expensive extraction pump failures.
#3.8 Oil Well Life Prediction
Demand for higher short-term volumes has led businesses to sacrifice the amount of oil that can be recovered. To tackle this issue, AI offers several powerful benefits across the entire oil and gas value chain and helps oil and gas upstream assess the life of reservoirs and oil wells. AI tools read the geology of an area and determine which wells are at risk of depletion. Furthermore, instead of testing wells individually, machine learning is being used to optimize the entire system.
Benefits of Using AI in Upstream Oil and Gas
Reduction in Dry Well Exploration
Modern AI-based techniques have enabled upstream oil and gas companies to ascertain geological features like rock formation for ensuring they don't waste time exploring dry wells. In addition, geologists are utilizing well-log data to develop ML models with existing and new seismic profiles, and it's helping them make an educated guess regarding the location of potential oil and gas reservoirs.
Artificial Intelligence in Upstream Oil and Gas
Quicker Closure of Exploration/Drilling Projects
Companies actively involved in the drilling/exploration projects are using AI to develop algorithms for drilling with precision, thus reducing the risk of oil spills, accidents, and fire. The same technology also helps them enhance the penetration rate. A different application of AI helps these businesses optimize their production by identifying areas where they are lagging.
Better Equipment Availability and Uptime
AI helps businesses increase their revenue by cutting unnecessary maintenance costs and downtimes from equipment unavailability. While preventive maintenance is conducted on the manufacturer's schedule, AI-powered predictive maintenance uses real-time process and equipment data build trends to forecast changes in the process equipment. This helps improve operational productivity, and thus, uptime.
Cost Savings
According to E&Y, 52% of oil and gas majors are implementing AI/ML in their business, and there's a good reason for it. Work in upstream oil and gas industries was once considered highly labor-intensive, but AI has helped change this perspective. Companies are now maximizing production while minimizing costs, all thanks to AI-powered process automation and predictive maintenance.
Digitizing upstream oil and gas is more of a deployment challenge than a technological one and needs more by-the-business and for-the-business approaches. There are almost always some inherent tensions between identifying opportunities that fix business problems and the need for replicability and incessant upscaling. Given that, more than anything, there's a requirement for sturdy governance in the mass deployment angle of AI in upstream oil and gas, given the sheer promise the technology offers.
 
 
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