Energy sector AI spending is set to soar to $13B, report says

eyes on ai

Deloitte predicts AI will represent 57 percent of IT spending by U.S. oil and gas companies in 2029. Photo via Unsplash.

Get ready for a massive increase in the amount of AI spending by oil and gas companies in the Houston area and around the country.

A new report from professional services firm Deloitte predicts AI will represent 57 percent of IT spending by U.S. oil and gas companies in 2029. That’s up from the estimated share of 23 percent in 2025.

According to the analysis, the amount of AI spending in the oil and gas industry will jump from an estimated $4 billion in 2025 to an estimated $13.4 billion in 2029—an increase of 235 percent.

Almost half of AI spending by U.S. oil and gas companies targets process optimization, according to Deloitte’s analysis of data from market research companies IDC and Gartner. “AI-driven analytics adjust drilling parameters and production rates in real time, improving yield and decision-making,” says the Deloitte report.

Other uses for AI in the oil and gas industry cited by Deloitte include:

Integrating infrastructure used by shale producers
Monitoring pipelines, drilling platforms, refineries, and other assets
Upskilling workers through AI-powered platforms
Connecting workers on offshore rigs via high-speed, real-time internet access supplied by satellites
Detecting and reporting leaks

The report says a new generation of technology, including AI and real-time analytics, is transforming office and on-site operations at oil and gas companies. The Trump administration’s “focus on AI innovation through supportive policies and investments could further accelerate large-scale adoption and digital transformation,” the report adds.

Chevron and ExxonMobil, the two biggest oil and gas companies based in the Houston area, continue to dive deeper into AI.

Chevron is taking advantage of AI to squeeze more insights from enormous datasets, VentureBeat reported.

“AI is a perfect match for the established, large-scale enterprise with huge datasets—that is exactly the tool we need,” Bill Braun, the company’s now-retired chief information officer, said at a VentureBeat event in May.

Meanwhile, AI enables ExxonMobil to conduct autonomous drilling in the waters off the coast of Guyana. ExxonMobil says its proprietary system improves drilling safety, boosts efficiency, and eliminates repetitive tasks performed by rig workers.

ExxonMobil is also relying on AI to help cut $15 billion in operating costs by 2027.

“There is a concerted effort to make sure that we’re really working hard to apply that new technology … to drive effectiveness and efficiency,” Darren Woods, executive chairman and CEO of ExxonMobil, said during a 2024 earnings call.

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Houston researchers land four projects in DOE's Genesis Mission

federal funding

The U.S. Department of Energy has named the nearly 300 projects selected under the Trump Administration's Genesis Mission, which aims to address some of the largest science and technology challenges in the country. The group features four projects from Houston universities and companies.

The initiative aims to unite government, industry, academia and philanthropy to lead to breakthroughs in energy, scientific discovery and national security, according to a release.

The selected projects feature 87 led by DOE and National Nuclear Security Administration (NNSA) National Laboratories, 168 led by universities, 19 led by companies and 4 led by nonprofit organizations—totalling 342 participating institutions.

“America has no shortage of bold ideas or talented scientists, and the response to the Genesis Mission proves that,” U.S. Secretary of Energy Chris Wright said in a news release. “The 278 projects selected today represent the very best of our nation’s scientific enterprise. The remarkable number of high-quality proposals we received demonstrates that America’s innovation pipeline is strong, and it points to even greater opportunities for future investment and continued expansion of the Genesis Mission portfolio.”

Twelve Texas-based projects were selected among the 278. The Houston projects and their researchers include:

Caroline Ajo-Franklin

Ajo-Franklin recieved a Phase I grant for her project "Predictive AI to Map Point Mutation Effects on Protein Function: Measurement and Biosynthesis of Isoprenoids." Ajo-Franklin is a professor of biosciences at Rice University, a CPRIT Scholar in Cancer Research and member of the Rice Synthetic Biology Institute. The project aims to accelerate the engineering of microbes that can produce isoprenoids, which are natural compounds that could replace petroleum-derived fuels, solvents and materials.

“This project creates a continuous feedback loop in which AI guides experiments and each experiment generates more detailed data to better hone the AI model,” Ajo-Franklin said in a news release. “In addition, it demonstrates the extraordinary star power Rice has recruited in protein engineering and synthetic biology.”

Anastasios Kyrillidis

Kyrillidis recived a Phase I grant for his project "Cracking the VQA Optimization Bottleneck: AI Methods for Quantum Chemistry and Materials." Kyrillidis is the Noah Harding Associate Professor of Computer Science at Rice and a member of the Ken Kennedy Institute. The project aims to develop AI tools to resolve bottlenecks in quantum computing for chemistry and materials research.

“Our goal is to replace fragile, hand-tuned optimization methods with intelligent systems that can learn from quantum computations while still operating within frameworks that provide strong mathematical guarantees,” Kyrillidis added in the release.

Myoungkyu Lee

Lee received a nearly $750,000 Phase I grant for his project "Physics-Informed AI Surrogates for Turbulent Forced Convection in Energy System." Lee is an assistant professor of mechanical aerospace engineering at the University of Houston. Lee will collaborate with researchers from Lawrence Livermore National Laboratory and University of Pennsylvania on the project and develop artificial intelligence to accelerate the design of materials for advanced nuclear fission and fusion reactors.

“The goal is to develop a tool that runs much faster than today’s most detailed simulations while keeping errors small,” Lee said in a news release. “If successful, the approach could support better heat-transfer predictions for molten-salt reactor design and provide a starting point for studying heat removal in fusion blankets. This is one contribution among many toward reliable, carbon-free energy.”

Amit Padhi

Padhi recieved a grant for his project "Probabilistic Inference of Subsurface Fracture Connectivity for Stimulation Control with Physics-Informed AI." Padhi is a scientific advisor for Halliburton.

The DOE first called for applications for the Genesis Mission in March. At the time, the DOE shared that it would grant approximately $293 million to the selected teams via Phase I awards, ranging from $500,000 to $750,000 for nine-month project periods, and Phase II awards, for $6 million to $15 million over a three-year project period.

Since then, however, the initiative has grown with 15 federal agencies now granting research awards and funding opportunities under the Genesis umbrella. The White House announced this week that it had secured more than $5 billion in federal commitments to expand the initiative.

SLB teams with Liberty Energy on modular power for AI data centers

ai alliance

Houston-headquartered SLB and Denver-based Liberty Energy Inc. announced a strategic agreement this month to support the rapid growth of new data center capacity.

Under the agreement, SLB will supply modular data center infrastructure and oversee large-scale execution, while Liberty will provide modular power generation systems and behind-the-meter power management technology for developers looking to add capacity. According to Reuters, the power will come from natural gas generation.

“The bottleneck in AI infrastructure is no longer just compute. It is the ability to deliver infrastructure and power on the timelines the market now demands,” Gavin Rennick, president of SLB’s New Energy and Industrial business, said in a news release. “By bringing together complementary infrastructure and power capabilities, we will help developers accelerate deployment of new data center capacity.”

The companies seek to specifically offer the modular technologies in areas without traditional grid connections or where grid capacity is limited.

They also aim to improve the "efficiency, flexibility and environmental performance of future data center energy systems," potentially through solutions like hybrid power systems and digital energy management, according to the news release.

Goldman Sachs estimates that U.S. data center capacity will more than double from 31 gigawatts in 2025 to 66 gigawatts in 2027. Other reports predict that Houston and Texas will be home to a significant portion of the data center boom, with capacity in the city and the state also expected to double in the next few years.

“The scale and complexity of AI energy infrastructure is fundamentally changing how power systems are built and deployed,” Ron Gusek, CEO of Liberty Energy, added in the release. “Liberty’s comprehensive power service platform is engineered to meet this transition, as customers increasingly prioritize tailored, integrated solutions. Building on our long-standing relationship with SLB, we are excited to bring power solutions that address immediate capacity constraints while supporting the next generation of energy systems.”

SLB sold its onshore hydraulic fracturing business in the United States and Canada to Liberty Energy in December 2020 in exchange for a 37 percent equity interest in the company.

New Rice study details how carbon capture could reduce AI data center emissions

by the numbers

A new study out of Rice University points to carbon capture and storage methods as pivotal solutions to addressing emissions from AI-driven data centers.

The study was authored by Hon Chung Lau, an adjunct professor in the Department of Chemical and Biomolecular Engineering at Rice University and founder of Low Carbon Energies LLC, and Steve C. Tsai, an energy transition consultant at Low Carbon Energies LLC, and published in the journal Energy & Fuels.

According to the study, U.S. data center power capacity could more than quadruple in five years, growing from 40 gigawatts in 2025 to 169 gigawatts by 2030. Without proper regulation of emissions, the report estimates that carbon dioxide produced by fossil-fuel power plants supplying electricity to data centers could grow at the same scale, increasing from 90 million metric tons to more than 404 million metric tons over the same time period.

The researchers analyzed publicly available data on announced U.S. data centers, which included energy sources, locations, and projected power capacity before estimating data center-related carbon emissions based on each state’s electricity mix. From there, they examined whether those emissions could be captured and stored underground in saline aquifers.

The team estimates that 34 states have enough saline aquifer storage capacity to store more than 100 years of projected data center-related carbon dioxide emissions beyond 2030. Aquifers could store an estimated 59 million metric tons of data center-related carbon dioxide, or about 66 percent of the sector’s emissions in 2025. However, that calculation could grow to 299 million metric tons, or about 74 percent of projected data center-related emissions by 2030.

The researchers found that more than 90 percent of data center-related carbon dioxide emissions could potentially be mitigated through carbon capture and storage when out-of-state storage options are included, even though they note that carbon capture isn’t the only solution.

“It does show that the geology exists to make a meaningful impact, especially in states where data center growth is strongest,” Lau said in a news release.

Rapid growth in states including Texas, Virginia, Pennsylvania, Ohio, Arizona, Colorado, Utah and Illinois was considered in the study. According to the findings, Texas would need to add 25 gigawatts of power capacity by 2030 to meet projected data center demand, as data centers require reliable electricity 24/7.

“Data centers are becoming one of the defining energy challenges of the AI era,” Lau added in the news release. “The question is not only whether we can build enough computing infrastructure, but whether we can power it in a way that is reliable, affordable and compatible with decarbonization goals.”

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