How "Oil" of the digital era is transforming the Oil & Natural Gas Industry.
Updated: Mar 25
The Oil and Gas industry is expected reach a market size of $2.85 billion by 2022.
According to Markets and Markets research, Artificial Intelligence (“AI”) in the oil and gas market, worldwide, is expected to grow at a CAGR of 12.66%.
The Oil & Gas industry over time has experienced the way data transforms into intelligence. AI has multiple potential applications in the oil and gas industry in all its operational sectors, upstream, downstream and midstream.
AI assists in helping in each area starting from advances in assets and software to visualising large amounts of collected data for business intelligence. It is now becoming important for a business to understand, develop strategies, and act on the AI movement. Leading oil companies are aware and our using data and analytics to help maximize production with deep-learning and machine learning in order to increase overall efficiency.
Alexander Khaytin, executive director of Yandex Data Factory, said he expects the oil and gas industry to implement solutions based on machine learning and AI because the industry handles “massive volumes of data” and that the “easy solutions for optimizing production and business processes have long since been implemented.”
AI & UPSTREAM PROCESSES
The Upstream level of the Oil & Gas Industry, generally known as the Exploration and Production (E&P) level of the oil and gas industry.
Finding ways to make E&P processes more efficient and optimize operations in this field are applications where AI will help oil and gas companies.
All major leading operators, constantly tend towards improving operational efficiency, making operations faster and more efficient, make assets run better, find bottlenecks in processes, find asset failures before they occur, and eliminate unplanned downtime.
What is AI currently doing in the upstream level of the oil & gas industry?
How does it impact the future of the industry?
What are the results drawn from using current technology?
Oil and gas companies are always looking for innovative techniques to optimize their production. With the help of production optimization, oil and gas businesses aim to improve efficiency and maximize profits. However, traditional techniques prove to be ineffective in improving revenue.
AI systems use large volumes of raw production data. Creation of predictive and estimation models, oil and gas companies can capture and analyse the dynamics of the production procedure.Most companies face recurring failures in production subcomponents resulting in more than 10% of downtime a loss of millions of dollars.
AI can help identify subcomponents which attribute to the failure of events with help of regression based analytics and owners can identify areas of inefficiency and develop strategies to maximise production. by providing actionable and usable insights by identifying three quarters of production impacting events at an average of a week in advance.
The most common problem associated with exploration is low rates of success with high costs, which is a unfavourable combination for any business in any industry.
Traditionally, oil and gas firms used a team of divers and human geological analysts engaged in the exploration of underwater anomalies. In 2016, ExxonMobile collaborated with Massachusetts Institute of Technology to create “self-learning, submersible robots for ocean exploration.” These robots have the ability to detect natural seeps in the ocean floor. According to National Geographic, these seeps, or leakages, occur “when oil escapes into the water column from highly pressurized sea floor rock.” An estimated 60 percent of oil beneath the earth’s surface in North America is due to these seeps. These robots will not only assist in protecting the ecosystem but also search and locate oil resources (reservoirs).
Similar advancements have shown to have automated the analysis of satellite images and rock sample images to categorize the high potential oil fields. The seismic and subsurface data charts are collected in the form of image files and geologists in the R&D team take time to analyse the data for approximately 45000 sq/m of area resulting in realising over all potential.
One of the major operations for any oil and gas company is drilling and mining raw hydrocarbons for producing fuel and other products. Drills have to dig into precise spots in the Earth to obtain crude oil and finding such spots can be complicated.
AI-powered precision drilling can help improve control of the rate of penetration (ROP) enhancement and identify and reduce risks like high risks of accidents, oil spills, and fires before hand. This helps oil-platforms reduce costs and increase overall efficiency.
According to The Oil and Gas Technology Centre, “data optimization can push plant performance beyond 95%, increase production by 2% to 5%, improve efficiency and thus, reduce costs by 10% and perhaps most importantly, it can help prevent disasters like oil spills or fires”.
EFFICIENCY AND INEFFICIENCY OF OIL WELLS
Recent advances in search, machine learning, and natural language processing(NLP) have made it possible to extract structured information from free text, providing a new and largely untapped source of insights for well and reservoir planning. Via NLP, patterns that can find a correlation between efficiency and inefficiency of production oil have been identified, and The NLP model classifies sentences present in drilling reports as EVENTS, SYMPTOMS, ACTIONS etc. Identifying these anomalies reduces downtime and improve production efficiency.
As the price of oil is estimated to decrease by 40% in the US, more and more oil and gas platforms have been inclining towards modern technology to achieve a more profitable outcome for them, and maximize their efficiency and revenue.