Computer Vision is a key technology of Artificial Intelligence (AI) that is rapidly entering the oil and gas industry, creating significant potential for innovation and growth. In many industries, AI has already triggered substantial changes and transformed the competition rules. Instead of relying on traditional, human-centered processes, companies aim to create value using AI technology.
As AI is changing the rules of competition, organizations race to build up internal capabilities, create custom AI vision applications and gather learnings with early adoption to iteratively optimize and operate AI technology at scale. In the following, we will cover:
- Technology Trends in the Industry
- Computer Vision Technology in Oil and Gas
- List of top AI vision applications
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Technology Trends of AI in Oil and Gas
Emerging technology and breakthroughs in the field of computer vision and Edge AI allow the highly scalable use of distributed computer vision applications. Modern edge computing and deep learning move computer vision from the cloud to the network edge.
The combination of the Internet of Things with on-device machine learning allows processing the video streams of distributed cameras in real-time, at high computational efficiency. Those technological advances make it possible to build large-scale deep learning applications with a large number of connected endpoints (AIoT).
As a result, it becomes possible to build mission-critical, large-scale computer vision systems with remote cameras connected to computing devices. To learn more about Edge AI technology, I recommend reading our article Edge AI – Driving Next-Gen AI Applications.
Compared to conventional IoT sensors and low-power devices, cameras provide a contactless method that provides rich information about complex objects and situations. With computers that can see, it becomes possible to automate human tasks and accelerate processes, increase operational efficiency, and reduce human error or subjectivity.
Adoption of Computer Vision in Oil and Gas
Companies in oil and gas are generally adopting AI technologies with the main goal to improve operational efficiency through industrial automation (Industry 4.0). This typically translates to accelerating processes and reducing operational risks.
The major application types include:
- Maintenance and service life prediction
- Safety and compliance monitoring
- Reliability, reduce business interruption
- Risk evaluation, structural health monitoring
- Sustainability and resource optimization
- Non-destructive testing and inspection
- Analyze fatigue and corrosion of systems
In the following, we will highlight some popular use cases of AI vision in more detail.
Oil or Gas Pipeline Inspection at Scale
Assessing large-scale infrastructure systems to determine their condition and health-stat under in-service or extreme hazard events poses great challenges to the operators. Deep learning approaches leverage computer vision models for the conditional assessment of large-scale systems through extracting critical information from the remote sensing data of cameras.
Firstly, the visuals need to be pre-processed on pixel-level with traditional computer vision methods. Next, deep learning models (e.g., R-CNN) are applied to evaluate the condition of different critical components. Application experiments demonstrated that DL models are able to rapidly and accurately detect the damage location and level. Hence there is a high potential for large-scale oil/gas pipeline assessment at both spatial and temporary scale over conventional models.
Remote Oil and Gas Field Monitoring
Real-time oil and gas field monitoring with cameras to automate and digitize oil development sites for maintenance of offshore oil and gas fields. Such systems aim to increase oil and gas productivity by monitoring and predicting the condition of load pumps with machine learning techniques.
The digital transformation of the oil and gas industry is driven by low-cost sensors and high-performance computing with distributed systems to extract high-value information from big data, directly at the source of data (Edge Intelligence). The multi-dimensional value and comparably low costs of cameras allow large-scale video analysis without the need to attach physical sensors.
Automatic Recognition of Analog Instruments
Computer vision can be used to read analog gauges at power substations and other equipment. Cameras with computer vision are used to automatically read oil level gauges, winding temperature gauges, and SF6 gas density gauges (find the study here).
Thereby, vision algorithms use color segmentation to detect the position of the pointers and scale marks. Such applications work much faster and more accurately than humans and help to avoid dangerous accidents and expensive production interruptions.
Wireline Spooling Automation With Computer Vision
In the oil and gas industry, wirelines are used for well intervention and reservoir evaluation. While the toolstring is retrieved from the well, the wireline cable, typically under tension, is spooled on a drum. Improper spooling could cause severe cable damage.
New computer vision applications have been implemented to detect spooling anomalies (Inception-V3 base network) and to predict the cable position in real-time (VGG-19 network).
Leak Detection With Computer Vision
Machine vision is used to detect methane gas emissions using regular infrared cameras. For example, a use case for deep learning based methane detection was recently developed. The automated approach simplifies the leak detection analysis with very high accuracy, as high as 95-99%.
Traditional optical gas imaging (OGO) methods to detect methane leaks are labor-intensive and unable to provide leak detection results without the judgment of a human operator. Computer vision approaches for optical gas imaging with convolutional neural networks (CNN) require training with methane leak images to enable automatic detection.
Corrosion Detection With Deep Learning Models
Corrosion is a major defect in structural systems; it has a significant economic impact and can pose safety risks if left untended. Inspection tasks that must be performed periodically are often carried out manually, sometimes in hazardous conditions.
In addition, the manual interpretation process is usually very expensive, time-consuming, and subjective. Therefore, deep learning methods analyze the video images of cameras to automate inspection tasks.
A key indicator during inspections is the presence of corrosion. Hence, computer vision has been successfully applied in use cases for automatic rust detection. This leads to cost savings and faster, better decision-making of preventative or corrective measures based on quantitative insights at scale.
Geological Assessment and AI-aided Exploration
Computer vision tools are used for rock typing based on images of rock samples extracted from the wells. Therefore, deep neural networks (DNN) are applied. Traditional methods of petrophysical interpretation are time-consuming, and the results depend strongly on the human expert (subjectivity).
In tests, the ML model’s accuracy was 92% compared to manual interpretation and about 1’000 times faster than the manual method. Interestingly, the study found that the second manual interpretation showed an accuracy of 91% compared to a second manual interpretation.
This shows how AI methods are the obvious way to accelerate the process and, even more critically, exclude subjectivity in the interpretation process.
Intelligent Fire Detection With AI Vision
Fire is one of the most severe accident causes that may lead to casualties, considerable production loss, and equipment damage. Traditional fire detection was done by human operators through video cameras, especially in petroleum and chemical facilities. However, it’s almost impossible for human operators to spot fires in time with hundreds of video cameras installed in large-scale settings. Human subjectivity, distraction, and visual perception limit the accuracy of human safety supervisors.
Intelligent fire detection applies computer vision methods to the video of cameras to detect fires. The method uses background subtraction to detect motion and reduce computational complexity. The object detection and image classification models perform fire detection at a rate of 98.4%, with a false alarm rate of 99.9%, at 27.4ms detection time per frame.
Today, we are only seeing the beginning of the era of AI-driven applications. Edge AI made it possible to move AI vision capabilities from the cloud to the field, enabling large-scale applications. Because of the strategic importance and distinct operational workflows, most oil and gas companies aim to build and operate their custom computer vision systems.
New applications for computer vision in the oil and gas industry primarily aim to improve maintenance, safety, management, life-cycle sustainability, quality, and operational efficiency.
To find related applications, check out our industry report about computer vision in energy and utilities industry applications.
How To Get Started
If your organization is looking for a full-stack computer vision platform to build, deploy and monitor custom and enterprise-grade computer vision applications, check out Viso Suite. The Viso platform leverages the latest Edge AI technologies with no-code and low-code tools to accelerate the building and operating of large-scale deep learning systems.
Get in touch with sales and get a personal demo.
Computer vision can be used to read analog gauges at power substations and other equipment. Cameras with computer vision are used to automatically read oil level gauges, winding temperature gauges, and SF6 gas density gauges (find the study here).Which is an example of computer vision based AI application? ›
Faceapp relies on computer vision to recognize patterns. Its artificial intelligence capabilities have enabled it to imitate images with increasing efficiency over time, using the data it receives from numerous sources. Faceapp transfers facial information from one picture to another at the micro-level.What are the three applications of computer vision in AI? ›
Road Condition Monitoring. 3D model Building using Computer vision. Cancer Detection using Computer Vision. Plant Disease Detection using Computer Vision.How does AI help oil and gas industry? ›
The oil and gas industry is benefiting from AI technology, which is being used to save lives, improve safety, monitor the environment, facilitate disaster response efforts, and provide decision-making support for operators.What type of computers are used in the oil and gas industry? ›
Vector computers are being used in petroleum engineering to simulate the flow of oil and gas in a reservoir, the faster performance of the vector machines mak ing many, heretofore, unmanageable calculations possible.What are some examples of vision systems using AI? ›
- Drone monitoring of crops.
- Yield monitoring.
- Smart systems for classifying and sorting crops.
- Automatic pesticide spraying.
- Weather records.
- Forest information.
- Smart Farming.
- Crop field security.
Computer vision describes the ability of machines to process and understand visual data; automating the type of tasks the human eye can do. So in layman's terms, computer vision is AI applied to the visual world.What are the real world applications of computer vision? ›
The application of computer vision for security purposes is diverse. It's face recognition, crowd detection, human abnormal behavior detection, illegal parking detection, speeding vehicle detection and more. The technology helps strengthen security and prevent accidents of various kinds.What types of applications are applied with computer vision? ›
Computer vision's application in artificial intelligence is spreading into new industries such as automotive, healthcare, retail, robotics, agriculture, autonomous flying such as drones, and manufacturing, among others.What are the three types of computer vision? ›
Different types of computer vision include image segmentation, object detection, facial recognition, edge detection, pattern detection, image classification, and feature matching.
According to GlobalData's thematic research report, Artificial Intelligence (AI) in Oil & Gas, leading adopters of AI include Shell, BP and ExxonMobil.How AI can be the tool that transforms oil and gas for the future? ›
By embracing AI, oil and gas companies can refocus employees on new ways of thinking and working. At their core, oil and gas companies are process-driven. From upstream to downstream, every step of the value chain is organized by rules and regulations designed to achieve a safe and efficient working environment.How are robots used in the oil and gas industry? ›
Robots have applications across the oil and gas industry in various tasks ranging from surveys, material handling, and construction, to inspection, repair and maintenance. They can be customized for various tasks to ease the work and improve efficiency.Which platform is best for computer vision? ›
- OpenCV. A software library for machine learning and computer vision is called OpenCV. ...
- Viso Suite. ...
- CUDA. ...
- MATLAB. ...
- Keras. ...
- SimpleCV. ...
- BoofCV. ...
- Facial recognition.
- Self-driving cars.
- Robotic automation.
- Medical anomaly detection.
- Sports performance analysis.
- Manufacturing fault detection.
- Agricultural monitoring.
- Plant species classification.
The oil field of the future will be built on key technologies such as: Drone- and robot-based exploration to reduce capital costs and time to production. Nanosatellite monitoring to reduce ongoing operation & maintenance costs. Aerial gas leak detection to detect and address methane emissions.What flow measuring technologies are employed in the oil and gas industry? ›
Ultrasonic flow meters are an ideal and cost-effective flow measurement solution for measuring liquids in the petroleum field because they are accurate, require minimal maintenance, and can be used in harsh environments. They have a long life due to their durability and design.What is IoT market for oil and gas? ›
The global iot in oil and gas market is expected to grow from USD 11.34 billion in 2022 to USD 22.24 billion by 2029 at a CAGR of 10.1% during the forecast period.What are four common uses of vision systems? ›
The technology and methods can be used for various applications from being able to identify defects, provide product sortation, barcode reading, end-of-line vehicle inspection, product checking, and robotic production, just to name a few.Is LiDAR a vision system? ›
LiDAR is a binary sensor: it can detect only the presence or absence of matter. It can only detect if anything is there – and no other information. It is limited by range. A visual perception system using a pair of stereoscopic cameras can perceive the world just like human eyes do – 3D and full color.
Computer vision is the branch of Artificial Intelligence responsible for digital systems that detect and process visual information, that is, all kinds of data learned from digital images, videos, and other elements.What is the difference between AI and machine learning and computer vision? ›
In simple terms, computer vision is a technology that attempts to train computers to recognize patterns in visual data in a similar way as humans do. On the other hand, machine learning is a process that enables computers to learn how to process and react to data inputs based on precedents set by previous actions.Is computer vision the same as machine learning? ›
Computer vision is a subset of machine learning that enables computers to gain a high level of understanding based on videos and digital images.What are 5 real life applications of computers? ›
- Home. Computers are used at homes for several purposes like online bill payment, watching movies or shows at home, home tutoring, social media access, playing games, internet access, etc. ...
- Medical Field. ...
- Entertainment. ...
- Industry. ...
- Education. ...
- Government. ...
- Banking. ...
In 2023, Computer Vision Will Grow in Numerous Fields
Among growth areas for computer vision, we can expect to see edge computing, healthcare, LiDAR (mapping), retail, health and safety, and autonomous driving. In all of these areas, computer vision will be used to detect and analyze a growing stream of data.
Computer vision is a rapidly growing field in research and applications. Advances in computer vision research are now more directly and immediately applicable to the commercial world. AI developers are implementing computer vision solutions that identify and classify objects and even react to them in real time.How to use AI in computer vision? ›
Computer vision is a field of AI that trains computers to capture and interpret information from image and video data. By applying machine learning (ML) models to images, computers can classify objects and respond—like unlocking your smartphone when it recognizes your face.What are three 3 examples of computer software application? ›
So a word processor, spreadsheet, web browser, and graphics software are all examples of application software, and they can do many specific tasks.Is computer vision in demand? ›
Computer Vision Engineers are highly sought-after for their valuable skills to make computers “see.” In other words, they are the people who enable computers to process visual data for solving problems or performing specific tasks.Is computer vision easy or hard? ›
But it's still a really hard problem that requires knowledge, not just data. The human brain can connect the dots based on information adjacency - how contextually close pieces of information are to each other - but this is learnt over time and can be hard to teach a computer.
Image processing algorithms are used to extract information from images, restore and compress image and video data, and build new experiences in virtual and augmented reality. Computer vision uses image processing to recognize and categorize image data.What is the most advanced AI on the market? ›
GPT-3 was released in 2020 and is the largest and most powerful AI model to date. It has 175 billion parameters, which is more than ten times larger than its predecessor, GPT-2.What company is leading the AI race? ›
Consumers believe that Google is leading the race in generative AI, according to the results of a study released by digital agency Critical Mass on Wednesday, despite the company botching the reveal of its AI chatbot Bard in February.Which companies are most advanced in AI? ›
- Google Cloud. Google, a leader in AI and data analytics, is on a massive AI acquisition binge, having acquired a number of AI startups in the last several years. ...
- IBM Cloud. ...
- Alibaba Cloud. ...
- Amazon Web Services (AWS) ...
- DataRobot. ...
- Baidu AI Cloud. ...
- Microsoft Azure. ...
Crude oil price prediction is a challenging task in oil producing countries. Its price is among the most complex and tough to model because fluctuations of price of crude oil are highly irregular, nonlinear and varies dynamically with high uncertainty.What will AI replace in the future? ›
- Customer Service Representatives. Most of the time, the queries and problems of customers are repetitive. ...
- Receptionists. ...
- Accountants/ Bookkeepers. ...
- Salespeople. ...
- Taxi and Truck Drivers. ...
- Retail Services. ...
- Proofreaders and Translators. ...
- Security and Military Personnel.
The implementation of AI in the oil and gas industry is expected to bring significant benefits, including but not limited to: a) Enhanced safety: AI-powered solutions can help in identifying potential hazards for prevention of incidents and accidents, thereby minimizing risks to workers and the environment.How AI is transforming the oil and gas industry? ›
An example is the use of machine learning to analyze seismic data for patterns that indicate the presence of oil and gas resources. Models of oil and gas reservoirs built with analytics powered by artificial intelligence (AI) may be useful to save costs and increase efficiency in the oil and gas industry.What are five 5 uses of robots in industry and society? ›
Five little known uses for robots: (1) explosives handling by explosives manufacturers and also by armed forces that must dispose or handle them; (2) using lasers on robotic arms to strip paint from air force plans; (3) having a robot scale the heights of a dam or nuclear chimney to inspect and analyze the concrete; (4 ...What is computer vision in energy and utilities? ›
Computer vision for utility infrastructure monitoring includes a wide range of use cases to recognize the state of pipes, cables, sewers, wires, plants, and equipment needed to provide utility services.
Automatic computer vision systems can inspect the surface of manufactured components, for example, wheels. Multiple cameras placed over the production line can be used for defect detection in real-time.What is the use of computer vision in automotive industry? ›
Computer vision automotive systems heavily rely on data to operate efficiently and accurately. The data needs of automotive vision systems include: High-quality image data: This is crucial for accurate object detection, recognition, and tracking in automotive vision applications.What are one of the best examples of computer vision? ›
Some of the most notable machine vision systems and application examples that exist today include: Drone monitoring of crops. Yield monitoring. Smart systems for classifying and sorting crops.How AI is impacting computer vision? ›
Computer vision is a branch of AI employing data to detect and recognize objects seen through a computer's camera. This cousin of AI recognizes objects and their environmental conditions through a camera lens and gives the computer a digital grasp of its surroundings to interact with objects.What is computer vision pipeline? ›
A computer vision pipeline is a series of steps that most computer vision applications will go through. Many vision applications start off by acquiring images and data, then processing that data, performing some analysis and recognition steps, then finally performing an action.What are some examples of utilities that are related to computer performance? ›
- File Management System.
- Disk Management tools.
- Compression tools.
- Disk cleanup tool.
- File Management System.
- Disk Defragmenter.
- Backup utility.
Computer vision can automate several tasks without the need for human intervention. As a result, it provides organizations with a number of benefits: Faster and simpler process - Computer vision systems can carry out repetitive and monotonous tasks at a faster rate, which simplifies the work for humans.What are the computer vision tasks in autonomous vehicles? ›
Computer vision technologies allow self-driving vehicles to classify and detect different objects; by using LiDAR sensors and cameras and by combining data with 3D maps, autonomous vehicles get to measure distances, and spot traffic lights, other cars, and pedestrians. Let's take spotting traffic signs as an example.What is an example of computers in automotive technology? ›
For example: There is probably a computer controlling the automatic transmission. If the car has anti-lock brakes, there is a computer reading the wheel speed and controlling the brakes. Many air bag systems have their own computers.What are vision systems primarily used in industry for? ›
Industrial vision systems can inspect objects for defects and imperfections, including surface defects, faulty packaging, and malformed parts. Machine vision improves the accuracy and efficiency of the inspection process, and the technology frequently locates flaws that a human would otherwise miss.
However, a 2021 IDG/Insight survey found that while only 10% of organizations are currently using computer vision, 81% are in the process of investigating or implementing the technology.What is the importance of visual inspection in automotive industry? ›
In the automotive industry, AI-powered visual inspection finds defects in the production phase. It helps inspectors catch defects in hazardous areas or confined spaces like storage tanks to ensure workers' safety.