Artificial intelligence (AI) has emerged as one of the biggest long-term megatrends of our time. AI is driving the Fourth Industrial Revolution and is increasingly being seen as a key strategy for overcoming some of the biggest challenges of our time, such as climate change and pollution.Energy companies are adopting AI tools Digitize records, analyze vast amounts of data and geological maps, and potentially identify problems such as excessive equipment usage and pipeline corrosion. One such company is the Dutch energy giant shell company (NYSE: Shell).wednesday, shell announced plans It uses AI-based technology from big data analytics company SparkCognition for deep sea exploration and production, with the aim of improving the efficiency and speed of operations and increasing production.
”We are committed to finding new and innovative ways to reinvent the way exploration works.‘, said Gabriel Guerra, Shell’s vice president of innovation and performance, in a statement.
Generative AI for seismic imaging will have far-reaching implications, with the technology dramatically reducing exploration times from nine months to less than nine days, according to Bruce Porter, chief scientific officer at Texas-based SparkCognition. I added that you can. The company’s generative AI uses fewer seismic data scans than usual to generate subsurface imagery to help preserve the deep ocean. Fewer seismic surveys accelerate the exploration process, improve workflows, and save costs on high-performance computing.
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But this isn’t Shell’s first foray into AI technology. In 2018, the company partnered with: microsoft To incorporate the Azure C3 Internet of Things platform into offshore operations. The platform uses AI to improve efficiency across a company’s offshore infrastructure, from drilling and mining to employee empowerment and safety.
Shell is not the only major oil company to employ AI in its operations. Going back to 2019, BP (NYSE:BP) has invested in Houston-based technology startup Belmont Technology to help develop it. Cloud-based geoscience platform Nickname is “Sandy”. Sandy enables BP to interpret geological, geophysical and reservoir project information, creating a unique ‘knowledge graph’ containing a robust picture of BP’s underground assets. BP will be able to run simulations using the program’s neural networks and interpret the results.
March 2019, Aker Solutions has partnered with SparkCognition to power AI applications in its “Cognitive Operation” initiative. Aker SparkCognition’s AI system, called SparkPredict, monitored over 30 offshore structure surface and subsea installations.
Four years ago, the Oil and Gas Authority (OGA) launched the UK’s first Oil and Gas National Data Repository (NDR). This massive repository contains 130 terabytes of geophysical, infrastructure, field and well data, or about eight years’ worth of his HD movies. This data covers over 5,000 seismic surveys, 12,500 wells, and 3,000 pipelines. NDR is using AI to interpret this data, and OGA hopes to uncover new oil and gas potential and increase production from existing infrastructure. The platform will also be used for the country’s energy transition, with reservoir and infrastructure data used to support carbon capture, utilization and storage projects.
AI and renewable energy
AI technology is also starting to play a bigger role in the renewable energy sector, helping build smart grids.
One of the biggest barriers to realizing the dream of a 100% renewable power grid for the United States is the intermittency of renewable power sources. After all, our grid is designed to have a fairly constant power input/output while the wind doesn’t always blow and the sun doesn’t always shine. A successful transition to renewable energy will require a smarter power grid.
Fortunately, there is encouraging precedent.
years ago, Google announced that it has achieved 100% renewable energy across its global operations, including data centers and offices. Google is currently the largest corporate purchaser of renewable electricity. A total commitment of 7 gigawatts (7,000 megawatts) of wind and solar energy. Google worked with IBM to find a solution to the highly intermittent nature of wind power.use IBM’s DeepMind AI PlatformGoogle deployed ML algorithms on 700 megawatts of wind capacity in the central US. This is enough to power a medium-sized city.
Using neural networks trained on widely available weather forecasts and historical turbine data, IBM says DeepMind can now predict wind power output 36 hours ahead of actual power output. It is said that As a result, the value of Google’s wind energy increased by about 20%.
Other wind operators can use similar models to optimize power output in a smarter, faster and more data-driven way to better meet customer demand.
IBM’s DeepMind uses trained neural networks to predict wind power output 36 hours ahead of actual power generation
Source: Deepmind
Based in Houston, Texas Inowattsis a start-up company that has developed an automated toolkit for energy monitoring and management. The company’s eUtility platform ingests data from over 34 million smart energy meters across 21 million customers, including major U.S. utilities such as Arizona Public Service Electric, Portland General Electric, Avangrid, Gexa Energy, WGL and Mega Energy. I’m in. Innowatts says its machine learning algorithms can analyze data to predict several key data points, including short- and long-term load, variance, and weather sensitivity.
Innowatts found that without machine learning models, utility forecasts were more than 20% inaccurate during the peak of the crisis, resulting in a huge strain on operations and ultimately costly end-users. I’m assuming it was on the rise.
Additionally, AI and digital solutions can be used to make the grid more secure.
Back in 2018, California’s largest utility, Pacific Gas and Electric, I realized I was in serious trouble Found responsible for the tragic 2018 wildfires that killed 84 people, resulting in heavy penalties of $13.5 billion in compensation for those who lost homes and businesses, and the California Public Works Commissioner for negligence After being fined another $2 billion by the Board. Perhaps had PG&E invested in an AI-powered early detection system like Innowats, the loss of life and livelihood could have been avoided.
By embracing digital and AI models, the power grid will become smarter, more reliable, and a smoother transition to renewable energy.
By Alex Kimani, Oilprice.com
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