Brief Background On the Oil & Gas Industry
Oil, also known as a “black gold,” has been one of the most valuable and in-demand resources in the world since ancient times. It’s easy to see why countries have historically sought to secure supplies, and why oil has been one of the major factors in military conflicts. Oil provides nearly half the world’s energy, powers the majority of vehicles, and is a base ingredient for many industrial chemicals. Oil is the lifeblood of industrialized countries: most manufacturing, technologies, plastics, and fertilizers would not be possible without it.
Why Is AI Important To the Oil & Gas Industry?
The price of oil has shown sluggish growth since its 2014 collapse. Although the adoption of new technologies such as directional drilling and hydraulic fracturing have increased yields, the industry continues to seek solutions to boost business, and many see AI as the answer.
To apply AI technology to the oil and gas industry, oil companies and startups generally first establish either a research group or a research center for the purpose. AI for oil and gas is a huge potential market, expected to reach US$2.85 billion by 2022. Presently, North America is the largest market using AI in oil and gas, followed by Europe and Asia Pacific.
Oil and gas is a huge industry which includes upstream, midstream and downstream components. There are different ways to apply AI technologies to these different sectors. The common factor is that AI can help oil and gas companies lower costs and make more accurate decisions.
Where AI Can Be Applied
The oil and gas industry is adopting new technologies in its quest to be more efficient and profitable with low margins, and AI and cognitive computing are a perfect fit. “The next generation of competitive advantage in the energy marketplace will go to forward-thinking players who invest a lot on digital IoT and artificial intelligence capabilities like Cerebra from Flutura,” says Archie W. Dunham, JAG chairman emeritus and former independent non-executive Chairman of Chesapeake Energy in Oklahoma City and retired ConocoPhillips Chairman.
A decade ago, the most advanced AI could only advise companies in retrospect for example that they should have taken a specific preventative action in order to prevent failure. Nowadays, with the help of advanced sensors and software powered by AI, companies can digest a large amount of data and output real-time responses on the best course of action. Within a few years, the Industrial Internet of Things (IoT) will comprise more than a trillion sensors that generate and share data, and these innovations will dramatically change the way oil and gas companies operate.
The use cases below illustrate how AI is already helping the oil and gas industry.
The upstream sector is usually known as the Exploration & Production (E&P) sector, and includes companies that locate and extract crude oil or natural gas. Most drilling and production wells are located in remote areas, and sending workers there increases costs. Onsite operating costs can be reduced by using sensors and the Internet of Things (IoT) powered by AI to handle data collection and system control in real time.
- Calgary-based Ambyint has developed intelligent High-Resolution Adaptive Controllers (HRACs) which integrate with the hardware and instrumentation, such as the motor, controller, variable frequency drive, and other moving parts of lift systems. The adaptive controllers can deliver real-time control and optimization capabilities at the well, leveraging edge computing capabilities to deliver both physics-based analytics and modern data science in real time.
- MinePortal, developed by Seattle-based DataCloud, is a cloud-based platform for real-time management and analyzing of the geosciences data. The service integrates exploration drill data, block models, and control measures into a single platform, which can help make better, faster drilling and blasting decisions to improve productivity.
- Silicon Valley data supplier Tachyus developed a platform that collects data from sensors and integrates it with data from seismic activity, drilling logs, cores, completion designs, production data, and maintenance records. Combining physical modeling and machine learning, the platform can predict mechanical equipment failure and identify optimal operational plans, resulting in significant cost reductions while boosting production.
The midstream sector processes, stores, and transports crude oil, natural gas, and liquefied natural gas. This sector is the important link between remote oil and gas producing areas and population centers where most consumers are located.
- AKW Analytics uses machine learning and patent-pending technologies in its PALM (Petroleum Analytics Learning Machine) software product suite, which provides big data analytics for E&P and midstream pipeline gathering operations. The New York based company has built a real-time intelligent system with forecasting and optimization capabilities for better decisions and operating performance.
The downstream sector includes oil refineries, petrochemical plants, petroleum product distributors, and natural gas distribution companies. This sector produces countless products including gasoline, diesel, jet fuel, lubricants, plastics, fertilizers, natural gas, and propane.
- Digital H2O is an American digital oilfield solutions company that uses a proprietary data model and predictive algorithms to develop software-based insights and solutions for the end-to-end management of water in oil and gas production. This is important, as oil refineries use a huge volume of water. Digital H2O’s service can manage water use more efficiently to reduce costs.
- Downstream refiners need to streamline their refinery and petroleum delivery operations to accelerate revenue growth. California-based Oracle Cloud helps downstream companies with its Oracle EPM Cloud, which can increase modeling speed and forecast financials through scenario analysis, lowering operation costs.
The digital revolution in oil and gas industry is taking off. Improved safety and productivity can be achieved by automating routine manual activities, which can also reduce the risk to human workers. AI also has the potential to free scientists and engineers from repetitive and time-consuming tasks, and can assist in decision making. Moreover, AI systems can automate and optimize data-rich processes to mitigate business risks.
AI’s power and potential to increase efficiency and cost-effectiveness is making the technology increasingly attractive and speeding its adoption across all sectors of the oil and gas industry.
Analyst: Paul Fan| Editors: Robert Tian、Michael Sarazen