Humans have long used tools to increase their efficiency. From time immemorial, we have broken our bones with sticks and stones for defense and nourishment. Bits and bytes have replaced these rudimentary tools today, but the goal remains the same.
Processing intractable data streams is a huge task requiring advanced computing systems with artificial intelligence (AI) and genius level algorithms. These applications consume unimaginable amounts of data and provide a wealth of insight into the inner workings of machines. The result is significantly lower business costs, improved sustainability, and faster processes.
AI is changing the face of the oil and gas industry, bringing new possibilities to sectors once thought to be slow-moving. According to a recent study by Ernst & Young, 92% of oil and gas companies worldwide are investing in AI or plan to do so in the next five years. And the impact of AI is already evident, with 50% of oil and gas executives using AI to solve challenges across their organizations.
By embracing AI, industry companies are transforming their operations, from optimizing exploration and drilling to streamlining production and logistics. Advances in machine learning, big data analytics, and automation have enabled the oil and gas sector to make impressive strides in efficiency, safety, and environmental sustainability.
Despite the critical role of AI in future oil and gas operations and production, AI adoption must keep up with the speed of AI development and application. However, despite AI’s huge impact, its adoption rate remains low.
Kumar Lakshmipathi, Principal Solutions Architect at Amazon Web Services, said at the SPE Gulf Coast Section Data Science Convention in Houston on April 20th. “Compared to other industries, this is not what we see.”
A recent study by consulting firm Gartner found that only about half (54%) of all AI projects make it from the pilot stage to production. This represents a significant improvement from the 85% failure rate for such projects in 2018, but slow adoption is a major challenge. Gartner points out that risk and confusion are often the main reasons behind these failures.
AI is reshaping the future of operations and production, but adapting quickly to the rapidly changing technological landscape is essential for the industry to fully harness its power. Lakshmipathi highlighted several applications, one trend, and the troubling concerns he sees regarding the future of AI and energy.
application
In addition to applying AI to monitor and improve safety, improve operational performance and enhance customer engagement, Lakshmipathi cited forecasting and maintenance as two areas where he sees growth opportunities.
AI for forecasting is one application he noted that has many uses, from inventory planning to workflow planning, but “using some form of mathematical and statistical [forecasting]”
He cites research conducted by McKinsey & Company, which found that AI models clearly outperform traditional spreadsheet-based analysis methods. For example, AI-driven forecasting applied to supply chain management reduces errors by 20-50%.
Lakshmipathi said he sees limitless possibilities for AI in maintaining equipment, facilities and platforms.
“There is a question that someone asked me. They said, ‘Do you know what oil industry executives are worrying about staying up late? Not the price of crude oil. It’s a potential oil spill,” he said. “We want to prevent this from happening, so predictive maintenance is absolutely key.”
Research shows that predictive maintenance, a data-driven approach that analyzes the condition of equipment to predict when maintenance will be required, could save businesses $630 billion by 2025.
“There is so much to gain from doing that, and you can use AI to do that. We change the oil in the car every three months, which is great, but very inefficient,” he said.
Using machine learning, the model “can predict when a breakdown is going to occur, work orders for repairs to people there, and order parts,” he said.
He explained that AI-powered machine learning predictive models are sensor data coming in and data coming out.
“And along the way, you can create custom physics-based models powered by machine learning.
“AWS has something called Lookout for Equipment, which is a machine-learning industrial equipment monitoring service that detects abnormal behavior in equipment.It is unsupervised learning,” he said. , to flag when an anomaly occurs. “
tendencies and troubling concerns
Over the past decade, the merry-go-round of digital terms has spawned some truly memorable terms. From Bitcoin to blockchain, big data to machine learning, nanobots to drones, there is no shortage of unique and interesting names. The latest to appear in everyday parlance and take the spotlight is ChatGPT.
Generative AI is a subset of artificial intelligence that trains algorithms to generate new and original content rather than simply processing existing data. This typically involves deep his learning models that combine unsupervised and supervised learning techniques to create new data similar to the training data.
This form of AI is used for a variety of purposes, from creating realistic images and videos to generating text and speech. It is useful for tasks that require creativity and for generating new ideas and designs based on existing data.
According to Lakshmipathi, his opinion of generative AI is “big, it’s here, and it will hopefully impact everything in a positive way.”
He said there are many use cases for generative AI. As an example, we introduced how to generate images of equipment with rust using AI. These images can be used to train a model to learn what rusty equipment looks like and flag it for inspection.
But he has one major concern with generative AI systems.
“It has a dual role. We can use it to predict climate change. We can scale smart grids. We can know exactly how much we produce and how much we consume,” he said. rice field. “You get a balance. You can track your greenhouse gas emissions. You can do it building by building, block by block, city by city. But what’s the catch? There’s a huge carbon footprint.”
In a 2019 paper, researchers at the University of Massachusetts Amherst described a lifecycle assessment for training several standard large-scale AI models. From training, they found that one common natural language processing model emitted 626,155 lbm of CO.2 This is almost five times the lifetime emissions of a single vehicle.
AI has the potential to be a game-changer in the oil and gas industry, with enormous potential to improve efficiency. Companies that invest in AI can reap the rewards of cost savings, sustainability, and faster processes.
Generative AI, such as ChatGPT, has a heavy carbon footprint. AI offers a wide range of possibilities for the oil and gas sector, but human ingenuity must also be used to mitigate risks and ensure sustainable development. Integrating solutions from humans and AI is essential for the industry to meet future challenges.
