High-value Applications of Computer Vision in Oil and Gas (2023) - viso.ai (2023)

FAQs

What are the applications of computer vision in oil and gas? ›

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).

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.

Road Condition Monitoring. 3D model Building using Computer vision. Cancer Detection using Computer Vision. Plant Disease Detection using Computer Vision.

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.

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.

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.

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.

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.

Different types of computer vision include image segmentation, object detection, facial recognition, edge detection, pattern detection, image classification, and feature matching.

Which oil and gas companies are using AI? ›

According to GlobalData's thematic research report, Artificial Intelligence (AI) in Oil & Gas, leading adopters of AI include Shell, BP and ExxonMobil.

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.

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.

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.

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.

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.

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.

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.

What does AI computer vision equate to? ›

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.

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.

Computer vision is a subset of machine learning that enables computers to gain a high level of understanding based on videos and digital images.

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.

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.

So a word processor, spreadsheet, web browser, and graphics software are all examples of application software, and they can do many specific tasks.

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.

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.

What is the difference between computer vision and image processing? ›

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.

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.

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.

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.

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.

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.

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 ...

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.

What are the application of computer vision in automotive industry? ›

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.

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.

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.

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.

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.

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.

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.

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.

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.

How many companies use computer vision? ›

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.

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.