The growing role of generative AI in the oil and gas industry

This audio was created using Microsoft Azure Speech Services

The struggle to reduce energy consumption continues to present difficult challenges for industries across the globe. After falling by 4.5% due to the global pandemic, global energy consumption is now increasing by 5%. Moving forward, global energy consumption is expected to grow by 1 to 2% per year.

While reducing energy demand is one part of the challenge, the availability of optimized energy solutions and diversified energy sources also help address the global energy offer gap.

According to the US Energy Information Administration (EIA), power generation will reach nearly 45 trillion kilowatt-hours (kWh) by 2050, almost 20 trillion kWh more than in 2018. Key industries such as Oil and Gas, are under intense pressure from regulators, shareholders, customers, and even their workforce to optimize upstream and midstream business models, while decarbonizing their operations. The energy transition is driving sustainable innovation across all streams of oil and gas operations. The majority of energy companies are supplementing their four business cornerstones of exploration, production, transportation, and distribution with a fifth fundamental pillar: environmental management. A new technology in particular, generative AI, is poised to revolutionize the industry by improving efficiency and driving profitability while addressing these broader sustainability goals.

Generative AI creates new data out of existing data. Sophisticated machine learning enables computers to create new image, video, and/or text content similar to what a human would produce. The generative AI models are based on probabilistic algorithms and neural networks that draw from large amounts of data to learn and generate useful statistical data quickly.

If you’re impressed by what ChatGPT can do for your daily tasks, wait until you learn what generative AI can do for commercial and industrial segments.

7 Oil and Gas generative AI application areas

Below are several use cases where Generative AI can improve the Oil and Gas industry’s operational efficiency, overall productivity, and profitability.

1. Predictive maintenance – Generative AI can analyze historical maintenance and sensor data surrounding pumps, compressors, turbines, and other infrastructure equipment to generate predictive maintenance models. These models then forecast potential equipment failures before they occur. These early warnings minimize downtime, reduce maintenance costs, and optimize the utilization of technician resources.
2. Reservoir modeling and simulation – By creating accurate reservoir models and simulations and by analyzing geological and geophysical data, generative AI provides insights into reservoir porosity, permeability, and fluid behavior characteristics. This helps engineers and operators optimize drilling strategies, estimate hydrocarbon reserves, simulate reservoir performance under various production scenarios, and maximize production efficiency.
3. Environmental monitoring – By analyzing numerous data sources, including satellite imagery, climate data, and regulatory guidelines, generative AI assists Oil and gas companies in conducting environmental impact assessments. Based on that data, predictive models determine the potential impact of operations on air quality, water resources, and ecosystems. This information then helps mitigate environmental risks and ensure regulatory compliance.
4. Scenario planning and decision support – Oil and gas companies can also use generative AI models to evaluate the impact of changing oil prices, geopolitical factors, or operational decisions on their financial performance, production levels, and risk exposure. Such information helps stakeholders make more informed decisions and develop robust strategies.
5. Data analysis – Generative AI models use natural language processing techniques to extract valuable insights from unstructured data sources like drilling logs, production reports, and sensor readings. Companies can generate more efficient data-driven decisions when performing tasks like sentiment analysis, trend identification, and anomaly detection, helping to drive up optimization and performance.
6. Exploration – By analyzing vast amounts of geological and geophysical data, generative AI algorithms quickly identify patterns and trends that indicate the presence of oil and gas deposits. These insights enable companies to make more informed decisions about where they drill and how they allocate exploration budgets. The simulations illustrate the potential risks and rewards associated with different drilling locations.
7. PLC Code generation – Oil and gas companies use Programmable Logic Controllers (PLCs) across their refining, production process control, pipeline control, and monitoring operations. Software developers and automation engineers can use generative AI to speed up PLC code generation. By generating PLC code through natural language inputs, engineering teams shorten development timelines and reduce the probability of errors.

Start leveraging generative AI

These generative AI cases represent just a few examples of how oil and gas companies can drive operational efficiencies while lowering carbon emissions. While using generative AI capabilities is the way to unlock cost and emissions savings, powering and securing the IT infrastructure to deploy the model presents a separate challenge by itself, especially in harsh environments. By partnering with infrastructure and energy management companies like Schneider Electric, oil and gas stakeholders can expand their generative AI capabilities by deploying edge computing systems protected by the new NEMA 4 outdoor enclosures for harsh environments. 

To learn more, visit our edge infrastructure solutions such as the micro data center and the modular data center.