Artificial Intelligence (AI) has emerged as some of the biggest secular megatrends of our time. AI is powering the fourth industrial revolution and is increasingly being viewed as a key strategy for mastering some of the greatest challenges of our time including climate change and pollution. Energy companies are employing AI tools to digitize records, analyze vast troves of data and geological maps, and potentially identify problems such as excessive equipment use or pipeline corrosion. One such company is Dutch energy giant Shell Plc (NYSE:SHEL). On Wednesday, Shell announced plans to use AI-based technology from big-data analytics firm SparkCognition in its deep sea exploration and production in a bid to improve operational efficiency and speed as well as boost production.
"We are committed to finding new and innovative ways to reinvent our exploration ways of working," Gabriel Guerra, Shell's vice president of innovation and performance, said in a statement.
According to Bruce Porter, chief science officer for Texas-based SparkCognition, Generative AI for seismic imaging has broad and far-reaching implications, adding that the technology can dramatically shorten explorations to less than nine days from nine months. The company’s Generative AI will generate subsurface images using fewer seismic data scans than usual and thus help with deep sea preservation. Fewer seismic surveys will in turn accelerate the exploration process, improve workflow and save costs in high-performance computing.
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But this is not Shell’s first foray into AI tech. Back in 2018, the company partnered with Microsoft to incorporate the Azure C3 Internet of Things platform in its offshore operations. The platform uses AI to drive efficiencies across the company’s offshore infrastructure, from drilling and extraction to employee empowerment and safety.
Shell is not the only Big Oil company employing AI in its operations. Back in 2019, BP Plc (NYSE:BP) invested in Houston-based technology start-up Belmont Technology which helped the company develop a cloud-based geoscience platform nicknamed “Sandy.” Sandy allows BP to interpret geology, geophysics and reservoir project information thus creating unique “knowledge-graphs” including robust images of BP’s subsurface assets. BP is then able to perform simulations and interpret results using the program’s neural networks.
In March 2019, Aker Solutions partnered with SparkCognition to enhance AI applications in its ‘Cognitive Operation’ initiative. Aker SparkCognition’s AI systems called SparkPredict to monitor topside and subsea installations for more than 30 offshore structures.
Four years ago, the Oil and Gas Authority (OGA) launched the UK’s first oil and gas National Data Repository (NDR). The massive repository contains 130 terabytes of geophysical, infrastructure, field and well data--the equivalent of around eight years’ worth of HD movies. This data covers more than 5,000 seismic surveys, 12,500 wellbores and 3,000 pipelines. NDR employs AI to interpret this data, with OGA hoping to uncover new oil and gas prospects as well as enable more production from existing infrastructure. The platform will also be used in the country’s energy transition, with reservoir and infrastructure data used to support carbon capture, usage and storage projects.
AI And Renewable Energy
AI tech is also starting to play a big role in the renewable energy sector and aiding in the creation of smart grids.
One of the biggest barriers to the United States realizing its dream of having a 100% renewable grid is the intermittency of renewable power sources. After all, our grids are designed for near-constant power input/output whereas the wind doesn’t always blow and the sun doesn’t always shine. For the transition to renewable energy to be successful, our power grids have to become a lot smarter.
Luckily, there’s an encouraging precedent.
A few years back, Google announced that it had reached 100% renewable energy for its global operations including its data centers and offices. Today, Google is the largest corporate buyer of renewable power, with commitments totalling 7 gigawatts (7,000 megawatts) of wind and solar energy. Google teamed up with IBM to search for a solution to the highly intermittent nature of wind power. Using IBM’s DeepMind AI platform, Google deployed ML algorithms to 700 megawatts of wind power capacity in the central United States--enough to power a medium-sized city.
IBM says that by using a neural network trained on widely available weather forecasts and historical turbine data, DeepMind is now able to predict wind power output 36 hours ahead of actual generation. Consequently, this has boosted the value of Google’s wind energy by roughly 20 percent.
A similar model can be used by other wind farm operators to make smarter, faster and more data-driven optimizations of their power output to better meet customer demand.
IBM’s DeepMind uses trained neural networks to predict wind power output 36 hours ahead of actual generation
Houston, Texas-based Innowatts, is a startup that has developed an automated toolkit for energy monitoring and management. The company’s eUtility platform ingests data from more than 34 million smart energy meters across 21 million customers including major U.S. utility companies such as Arizona Public Service Electric, Portland General Electric, Avangrid, Gexa Energy, WGL, and Mega Energy. Innowatts says its machine learning algorithms are able to analyze the data to forecast several critical data points including short- and long-term loads, variances, weather sensitivity, and more.
Innowatts estimates that without its machine learning models, utilities would have seen inaccuracies of 20% or more on their projections at the peak of the crisis thus placing enormous strain on their operations and ultimately driving up costs for end-users.
Further, AI and digital solutions can be employed to make our grids safer.
Back in 2018, California’s biggest utility, Pacific Gas & Electric, found itself in deep trouble after being found culpable for the tragic 2018 wildfire accident that left 84 people dead and, consequently, was slapped with hefty penalties of $13.5 billion as compensation to people who lost homes and businesses and another $2 billion fine by the California Public Utilities Commission for negligence. Perhaps the loss of lives and livelihood could have been averted if PG&E had invested in some AI-powered early detection system like Innowats.
By employing digital and AI models, our power grids will become increasingly smarter and more reliable and make the shift to renewable energy smoother.
By Alex Kimani for Oilprice.com
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Be careful what you ask for even if you don’t think you asked for it
This is especially so as true as the US electrical grid becomes more "distributed" or pushed out to the end user becoming a steady state supplier. This absolutely requires a very robust communication and computer modeling system to create a proper "flow back" into the grid...but certainly not something revolutionary by way of example managing the flow from Niagra Falls into the New York State energy grid or managing flow from Canada into same said. If a nuclear power generating Station suddenly go off line for say maintenance this also creates issues that can be absolutely material. Also there are seasonal trends and factors, weather...any number of inputs that need to be entered into in order to provide for again i think the term is "steady state." As for geology and mapping yes this is also a great matter so too is the modeling of an actual drilling system or even equally important financial modelling of say a deep water drilling platform "financial cost estimate."
So much modeling be done however at times the importance of having the model be "proved out" be lost. That might be where artificial intelligence makes its presence felt...to enhance estimate methods for say what a specific geology will yield as an "output" of energy product. This is all very important to "estimate" as there is no such thing as free shipping ("offtake"?) yes, absolutely.