Energy AI startup chooses Houston for first U.S. office after $20M raise

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The company is known for its Orbital AI platform, which is tailored for the energy sector. Photo via solutions.kbr.com

London-based AI firm Applied Computing has announced a $20 million Series A round and a new office in Houston.

The new Bayou City office is Applied Computing’s first in the United States and part of its North American expansion. The company is known for its Orbital AI platform, which is tailored for energy operations.

The funding round was led by Houston-based KBR Inc., with participation from San Francisco-based Databricks Ventures. KBR’s investment was first announced in March.

KBR and Applied Computing have also entered into a multi-year agreement to deliver exclusive AI products for the energy sector. KBR already has integrated Orbital into its INSITE 3.0 platform for energy projects, and is also using the product for ammonia production.

Applied Computing’s Orbital platform combines physics-grounded intelligence with models across chemical engineering, time-series forecasting and language, according to the company. The system analyzes sensor readings and can recognize a facility’s equipment constraints and operator activity. The platform can also allow technicians to run simulations of how a change to a facility could affect the rest of its operations.

According to TechCrunch, Applied Computing will use the $20 million to further explore projects and deployments with the energy sector, hire engineering and research positions, and continue to expand internationally, potentially into the Middle East.

The company is also working on deals with a major U.S. stream operator, TechCrunch reports. And Applied Computing shared on LinkedIn that it plans to announce its first partnership with a major European oil company in the coming weeks.

“Yesterday we showed Orbital live in deployments at our demo day at the Energy Institute in London,” Callum Adamson, CEO and co-founder of Applied Computing, posted on LinkedIn on July 16. “Today, we're announcing the capital to scale it globally as well as the launch of our new offices in Houston and Bangalore. In the weeks following, there will be more announcements on our progress, partnerships and deployments.”

The company opened its Bangalore offices in December.

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This article originally appeared on our sister site, InnovationMap.com.

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Scotty Nyquist discuss the growth in AI data centers and the strain on the system. Photo via HARC report

Houston energy expert asks: Who pays when AI outruns the power grid?

Guets Column

For most of the past 20 years, U.S. electricity policy relied on predictable trends in demand. Electricity use, in most regions, increased gradually, forecasts were stable, and utilities adjusted the system in small steps. Power plants, transmission lines, and substations were generally added to reflect shifts in load, rather than growth, and costs were recovered through modest adjustments to customer bills.

Growth in AI data centers has disrupted this model. A single facility can add as much electricity demand as a small town. That demand comes all at once, runs continuously, and has little tolerance for outages. If electricity service drops even briefly, computation stops, and services shut down. Ironically, data centers need reliable service, a point that their emergence is driving concern around for the rest of the grid.

What the numbers say

The International Energy Agency projects global electricity consumption from data centers to double by 2030, reaching roughly 945 TWh, nearly 3 percent of global electricity demand, with consumption growing about 15 percent per year this decade. McKinsey projects that U.S. data center demand alone could grow 20–25 percent per year, with global capacity demand more than tripling by 2030.

After years of roughly 0.5 percent annual demand growth, many forecasts now place total U.S. electricity demand growth closer to 2–3 percent per year through the mid-2030s, with much higher growth in specific regions. In Texas, some forecasters are saying electricity demand could double over the next five years, a staggering 10 percent per year growth rate. What sounds incremental on paper translates into a major challenge on the ground. Meeting this pace of growth is estimated to require $250–$300 billion per year in grid investment, about double what the system has been absorbing.

Where the system starts to strain

The strain appears first in the interconnection queue. It shows up as long waits, backlogs, and delays for connecting new loads and new generation.

Before new generators or large load customers can be connected, a study is required to assess their impact on the grid, whether it can physically handle the added load, and whether upgrades are required. With AI-driven data centers, utilities face far more connection requests than they can realistically support. In ERCOT, large-load interconnection requests exceed 200 gigawatts, most tied to data centers. That amount exceeds historical norms, and it is several times larger than what can be practically studied or built in the near term.

To be clear, public utility commissions are required to study these requests because they must manage system capabilities to ensure minimal disruption. This means engineers spend time evaluating projects that may never be built, while other more commercially viable projects may wait longer for approvals. This extends timelines and makes infrastructure planning less reliable.

Why policymakers are rethinking the rules

Utilities and their regulators must decide how much generation, transmission, and substation capacity to build years before it comes online. Those decisions are based on expected demand at the time projects are approved. When it comes to data centers, by the time infrastructure is completed, they may end up deploying newer, more efficient chips that use less power than originally assumed. This can result in grid infrastructure built for a higher load than what actually materializes, leaving excess capacity that still must be paid for through system-wide rates.

That’s the central dilemma. If utilities build too little capacity, the system operates with less reserve margin. During periods of grid stress, operators have fewer options, increasing the likelihood of curtailments or outages. However, if utilities build too much, customers may be asked to pay for infrastructure that is not fully used.

In response, policymakers are adjusting the rules. In some regions, regulators are moving toward bring-your-own-power approaches that require large data centers to supply or fund part of the capacity needed to serve them or reduce demand during system stress. At the federal level, permitting reforms tied to datacenter infrastructure increasingly treat electricity as a strategic economic input.

As Ken Medlock, senior director at the Baker Institute Center for Energy Studies (CES), explains:

“Many of the planned data centers are now also adding behind-the-meter options to their development plans because they do not anticipate being able to manage their needs solely from the grid, and they certainly cannot do so with only intermittent power sources.”

Behind-the-meter (BTM) refers to power that a consumer controls on its side of the utility meter, such as on-site gas generation or a dedicated power plant. These resources allow data centers to keep operating during grid-related service. Most facilities remain connected to the grid, but the backup BTM generation serves as insurance for operating their core business.

This shifts responsibility. Utilities traditionally manage reliability across all customers by maintaining an operating reserve margin, or spare capacity. Increasingly, large-load customers manage part of their own electricity reliability needs, which changes how infrastructure is planned and how risk is distributed.

Bottom line

AI-driven load growth is arriving faster and in more concentrated places than the power system was built to accommodate. Utilities and regulators are being forced to make decisions sooner than planned about where to build, how fast to build, and which customers get priority when capacity is limited. The effects extend beyond data centers, showing up in system costs, reliability margins, competition for grid access, and pressure on communities and industries that depend on affordable and dependable power. The issue is not whether electricity can be generated, but how the costs and risks of rapid demand growth are distributed as the system tries to keep up. How regulators balance these decisions will determine who pays as AI demand outruns the power grid.

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Scott Nyquist is a senior advisor at McKinsey & Company and vice chairman, Houston Energy Transition Initiative of the Greater Houston Partnership. The views expressed herein are Nyquist's own and not those of McKinsey & Company or of the Greater Houston Partnership. This article originally appeared on LinkedIn.

Collide has rolled out RIGGS, a large language model for energy professionals. Photo via Getty Images

Houston AI startup rolls out platform to reshape oil and gas workflows

AI for energy

Houston-based Collide is looking to solve AI issues in the energy industry from within.

Co-founded by former oil roughneck Collin McLelland, the company has developed AI software for operators and field teams, shaped by firsthand oilfield experience. Its AI-native platform “retrieves and synthesizes data from authoritative sources to deliver accurate, cited, and energy-focused insights to oil and gas professionals,” according to the company.

“Oil and gas has a graveyard full of technology that was technically impressive and operationally useless,” McLelland tells Energy Capital. “The reason is almost always the same: the people who built it didn't understand what they were actually solving for. When you're an outsider, you see workflows and try to automate them. When you're an insider, you understand why those workflows exist—the regulatory constraints, the physical realities, the liability concerns, the trust dynamics between operators and service companies.”

Collide’s large language model, known as RIGGS, performed well in recent benchmarking results when taking a standardized petroleum engineering (SPE) exam, the company reports. The exam assesses understanding from conceptual terminology to complex mathematical problem-solving.

According to Collide, RIGGS achieved a score of 67.5 percent on a 40-question subset of the SPE petroleum engineering exam, outperforming other large language models like Grok 4 (62.5 percent), Claude Sonnet 4.5 (52.5 percent) and GPT 5.1 (4 percent).

RIGGS completed the test in 15 minutes, while Grok took two hours. Collide hopes over the next few months, RIGGS will receive a score between 75 percent to 80 percent accuracy.

The software could potentially help oil and gas companies produce accurate outputs and automate trivial workflows, which can open up valuable time for engineers and teams to work on other pressing matters, according to McLelland.

“Collide exists because we sat in those seats — we were the engineers, the operators, the field guys,” he says. ”RIGGS scoring higher on the PE exam versus the frontier labs isn't a party trick. It's evidence that the model understands petroleum engineering the way a petroleum engineer does, because it was built by people who do.”

RIGGS was trained on Collide’s Spindletop hardware and is supported by a vast library of information, as well as a reasoning engine and validation layer that uses logic to solve problems.

“Longer term, we see RIGGS as the intelligence layer that sits underneath every operator's workflow — not a chatbot you open in a browser, but something embedded in the tools engineers already use,” McLelland says. “The goal is to give every engineer the knowledge and pattern recognition of a 30-year veteran, on demand."

According to McLelland, Collide is already building toward reservoir analysis and production optimization, automated regulatory compliance (Railroad Commission filings, W-10s, G-10s), workover report generation, and engineering decision support in the field for near-term use cases. In March, Collide and Texas-based oil and gas operator Winn Resources announced a collaboration to automate the time-intensive process of filing monthly W-10 and G-10 forms with the Texas Railroad Commission, completing what’s normally a multi-hour task in under 30 minutes. Collide reports that Winn’s infrastructure now automates regulatory filings and provides real-time visibility into data gaps, which has reduced processing time by over 95 percent.

“Before Collide, I'd spend hours manually keying in filings,” Buck Crum, director of operations, said in a news release. “(In March), we had 50 wells to file and I was done in 20 minutes. It does the majority of the heavy lifting while keeping me in control. That human-in-the-loop approach saves meaningful time and gives us greater confidence in our compliance and reporting.”

Collide was originally launched by Houston media organization Digital Wildcatters as “a professional network and digital community for technical discussions and knowledge sharing.” After raising $5 million in seed funding led by Houston’s Mercury Fund last year, the company said it would shift its focus to rolling out its enterprise-level, AI-enabled solution.

Hadi Ghasemi, a University of Houston professor, has uncovered a method to release heat from data centers and electronics at record performance. Photo courtesy UH.

Houston researcher develops efficient method to cool AI data centers

cool findings

A University of Houston professor has developed a new cooling method that can remove heat at least three times more effectively from AI data centers than current technologies.

Hadi Ghasemi, a distinguished professor of Mechanical & Aerospace Engineering at UH, published his findings in two articles in the International Journal of Heat and Mass Transfer. The findings solve a critical issue in the growing AI sector, according to UH.

High-powered AI data centers generate huge amounts of heat due to the GPU and operating systems they use with extreme power densities, which introduce complex thermal challenges. Traditionally, cooling methods, like microchannels, which use flow and spray cooling, have had limitations when exposed to extreme heat flux, according to UH.

Ghasemi’s research, however, found a more effective way to design thin-film evaporation structures to release heat from data centers and electronics at record performance.

Ghasem’s solution coupled topology optimization and AI modeling to determine the best shapes for thin film efficiency, ultimately landing on a branch-like structure—resembling a tree.

The model found that the “branches” needed to be about 50 percent solid and 50 percent empty space for optimum efficiency, and that they could sustain high heat fluxes with minimal thermal resistance.

“These structures could achieve high critical heat flux at much lower superheat compared to traditionally studied structures,” Ghasemi said in a news release. “The new structures can remove heat without having to get as hot as previous removal systems.

Ghasemi’s doctoral candidates, Amirmohammad Jahanbakhsh and Saber Badkoobeh Hezave, also worked on the project. The team believes their results show the impact of a physics-aware, AI design and can help ensure reliability, longevity and stability of AI data centers.

“Beyond achieving record performance, these new findings provide fundamental insight into the governing heat-transfer physics and establishes a rational pathway toward even higher thermal dissipation capacities,” Ghasemi added in the release

Merab Momen, founder of AI CTO Services. Courtesy Photo

How this Houston expert helps startups turn AI hype into real impact

now streaming

Artificial intelligence is now everywhere. It is mentioned in every startup pitch deck, and every corporate roadmap claims to use it. However, many early-stage businesses struggle with the simple question, “What does AI actually mean for my business?”

In a recent podcast episode of EnergyTech Startups, Merab Momen, founder of AI CTO Services and a long time AI practitioner, explains why most founders misunderstand AI, how startups can practically apply it and why Houston is quietly becoming a serious hub for AI-driven innovation.

Filling the AI Leadership Gap

Merab’s career has spanned decades of technology transitions. He worked on neutral networks in the 1990s, constructed computer vision systems long before they were common, and helped install AI solutions inside huge industrial companies. However, he noticed a huge problem when generative AI started to explode into the mainstream-The requirement of a real partner by the founders for AI integration but inability to rely on a full-time CTO and project-based consultants.

“I really needed something which is much more engaging where I can give that partner-level advice to the founders,” he said. By giving firms on-demand access to high-level AI knowledge and expertise, his methodology enables them to analyse tools, steer clear of cost blunders and eventually transition to a permanent technology leader when the time is right.

AI is Older than Most People Think

Despite its recent rise in popularity, AI is nothing new. AI actually began in the 1950s. Merab in his conversation explained how he worked on his first AI project back in the year 1996 that worked perfectly, but the processing power wasn’t just there to make it practical. He continued how he utilized the swarm intelligence models to optimize supply chains, now referred to as MLPOs and data engineering.

From Language Models to Physical World

Much of the public conversation about AI revolves around chatbots and text generation. But Merab sees far greater potential in AI’s interaction with the physical world, especially in industrial settings. He emphasized edge computing and vision language models (VLMs) as significant advances in manufacturing and energy. This physical shift is opening doors for new opportunities for robotics, automated inspections, and industrial safety applications. Merab added that Houston is uniquely positioned for this transition.

Why Houston has an AI Advantage

Silicon Valley may dominate the AI headlines, but Merab believes Houston’s advantage lies beneath the surface. The city doesn’t lag in AI utilization; it just operates in industries where results show differently.

Machine learning isn’t new to Houston’s core industries. Energy companies, manufacturers, logistics providers, and healthcare systems have been using advanced analytics for decades. The difference lies in them innovating in industrial sectors rather than consumer technology.

What’s Next

With the AI CTO Services growing, Merab is working with startups across industries to deploy AI in practical, business-first ways.

He is more interested in assisting founders in finding answers to critical issues than following new trends.

For Houston’s energy and climate tech community, it needs to transform AI enthusiasm into real-world impact.

Listen to the full conversation with Mehrab Momin on the Energy Tech Startups Podcast to learn more.

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Energy Tech Startups Podcast is hosted by Jason Ethier and Nada Ahmed. It delves into Houston's pivotal role in the energy transition, spotlighting entrepreneurs and industry leaders shaping a low-carbon future.

A new report shows the role Texas could play as the data-center sector enters "hyperdrive." Photo via JLL.com.

Texas could topple Virginia as biggest data-center market by 2030, JLL report says

data analysis

Everything’s bigger in Texas, they say—and that phrase now applies to the state’s growing data-center presence.

A new report from commercial real estate services provider JLL says Texas could overtake Northern Virginia as the world’s largest data-center market by 2030. Northern Virginia is a longtime holder of that title.

What’s driving Texas’ increasingly larger role in the data-center market? The key factor is artificial intelligence.

Companies like Google and Microsoft need more energy-hungry data centers to power AI innovations. In a 2023 article, Forbes explained that AI models consume a lot of energy because of the massive amount of data used to train them, as well as the complexity of those models and the rising volume of tasks assigned to AI.

“The data-center sector has officially entered hyperdrive,” Andy Cvengros, executive managing director at JLL and co-leader of its U.S. data-center business, said in the report. “Record-low vacancy sustained over two consecutive years provides compelling evidence against bubble concerns, especially when nearly all our massive construction pipeline is already pre-committed by investment-grade tenants.”

Dallas-Fort Worth has long dominated the Texas data-center market. But in recent years, West Texas has emerged as a popular territory for building data-center campuses, thanks in large part to an abundance of land and energy. Nearly two-thirds of data-center construction underway now is happening in “frontier markets” like West Texas, Ohio, Tennessee and Wisconsin, the JLL report says.

Northern Virginia, the current data-center champ in the U.S., boasted a data-center market with 6,315 megawatts of capacity at the end of 2025, the report says. That compares with 2,423 megawatts in Dallas-Fort Worth, 1,700 megawatts in the Austin-San Antonio corridor, 200 megawatts in West Texas, and 164 megawatts in Houston.

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San Antonio company breaks ground on 347MW solar project outside of Houston

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Crews have broken ground on the forthcoming 347-megawatt direct-current SunRoper Solar project in Wharton County, Texas, that will add capacity to the ERCOT grid.

The solar project, which is slated to begin operations in December 2027, will provide electricity to an undisclosed Fortune 100 company under a long-term power purchase agreement, according to a news release.

San Antonio’s OCI Energy and Israel's Arava Power are developing the project. It's being financed by ING Capital and constructed by Louisiana-based WHC Inc. The project received $394 million in construction financing in February.

"SunRoper demonstrates how strategic partnerships can help meet Texas' growing demand for electricity through investments in critical energy infrastructure," Sabah Bayatli, president of OCI Energy, said in the release.

Project partners, landowners and company executives attended a groundbreaking event for SunRoper on Sept. 1 at the site outside of the Houston metro area. The companies say they are advancing this energy project to strengthen grid reliability and to help deliver affordable power to one of the highest-demand areas in the state.

“WHC is proud to serve as EPC contractor on the SunRoper Solar project, bringing our construction expertise to bear on a facility that will deliver meaningful power to the Houston region,” Randel Badeaux, president of power North America for WHC, added in the news release. “This groundbreaking reflects months of careful planning and coordination with OCI Energy, Arava Power and our project partners, and we look forward to executing a safe, high-quality build through to completion in 2027.”

OCI Energy currently operates several utility-scale solar and battery energy storage system projects outside of the San Antonio area, and has five other projects under construction outside of San Antonio and Waco, with more than 30 under development in Arkansas, Mississippi, Georgia, Colorado and Alberta, Canada. The company also has existing projects in New Jersey and Georgia.

In $2 billion deal, NVIDIA takes 20% stake in Woodlands-based Lancium

power play

With an initial investment of $2 billion, AI chip manufacturer NVIDIA just acquired a 20 percent stake in The Woodlands-based Lancium, which develops large-scale campuses that combine AI data centers and onsite power supplies.

Lancium recently announced the investment but didn’t disclose the dollar amount. The Information news website reported NVIDIA’s investment totaled $2 billion, with the possibility of an additional $1 billion if Lancium achieves certain milestones.

Dealroom.co calls NVIDIA’s investment a “form of supply-chain insurance.”

NVIDIA “is gaining exposure to the scarce physical assets that determine whether its chips can be deployed,” Dealroom.co says. “The move makes Nvidia look less like a pure chip company and more like an allocator of infrastructure capacity.”

Investment precedes possible IPO in 2027

Thanks to NVIDIA’s cash infusion, Lancium and its portfolio of land and power connections carry an enterprise value of about $10 billion, according to The Information.

The investment should enable Lancium to expand as it explores a potential IPO next year, The Information reported.

Neither Lancium nor NVIDIA is responding to requests for comment about the deal.

Lancium’s marquee project is a 1,000-acre data center and power generation campus in West Texas for the $500 billion Stargate initiative. Stargate, a joint venture comprising MGX, OpenAI, Oracle and SoftBank, is building data centers equipped to handle AI-level workloads.

“Epicenter of energy and AI infrastructure”

Founded in 2017, Lancium has 4 gigawatts of leased capacity and a more than 15-gigawatt development pipeline. In 2024, Blackstone Energy Transition Partners invested about $500 million in Lancium, giving Blackstone a roughly 50 percent stake.

“This partnership with NVIDIA is a strong testament to Lancium’s position at the epicenter of energy and AI infrastructure … . We look forward to continuing to partner with these leading companies to help power the next generation of AI innovation,” Bilal Khan, senior managing director at Blackstone, said in a release.

Through the NVIDIA partnership, Lancium’s data center and power generation campuses will use the tech company’s “AI factory” platform, including software, computing, and networking capabilities. This will give NVIDIA customers and partners access to power capacity that supports heavy AI workloads.

“We have spent years assembling the power, the land, and the infrastructure expertise needed to deliver AI data center capacity at a scale the world has never seen,” Michael McNamara, co-founder and CEO of Lancium, said in the release.

“Partnering with NVIDIA — the definitive technology platform for AI computing — ensures that every campus in our portfolio will be deployed with the industry’s most advanced technology and that NVIDIA’s customers will have access to the capacity they need to compete and lead in the AI era.”

U.S. oil giant Chevron confirms it will expand operations in Venezuela

O&G News

Oil giant Chevron confirmed that it will expand operations in Venezuela after President Donald Trump announced an ambitious deal to develop the nation’s oil reserves and give the Pentagon a stake in the profits.

Chevron, the only U.S. oil company with a major presence in Venezuela, said Wednesday that it has been assigned additional acreage in the Orinoco Belt, where it has active operations. The company plans to invest more than $7 billion over the next five years, with the goal of more than doubling its current production to about 600,000 barrels a day.

“Chevron’s history in Venezuela spans more than a century, and our expanded position reflects our confidence in the country’s deep resource potential,” CEO Mike Wirth said in a prepared statement.

Venezuela holds the world's largest proven reserves, totaling more than 303 billion barrels of crude oil, according to OPEC's 2025 Annual Statistical Bulletin. Saudi Arabia is a distant second with 267 billion barrels.

Yet because Venezuela's energy infrastructure is severely degraded and the nation is operating under international sanctions, its daily production is just over 1 million barrels, compared with the 10 million to 11 million barrels that Saudi Arabia produces each day. The U.S. produces almost 14 million barrels per day.

Chevron, the second-largest U.S. oil company, has had a presence in Venezuela since 1923.

“President Trump’s mission in Venezuela is straightforward. The mission is to bring peace, freedom, opportunity and prosperity to the people of Venezuela,” Energy Secretary Chris Wright said Wednesday in Caracas, Venezuela. “I believe the deals that are signed today – tens of billions of dollars of investment, ultimately many thousands of jobs – are critical in starting this ball rolling of peace, opportunity and prosperity for everyone in Venezuela.”

The White House confirmed Monday that it is partnering with North American Blue Energy Partners, NABEP, as part of Trump ’s push to tap into Venezuela’s oil industry.

Yet the agreement has been met with skepticism from energy experts who say it will take years to revive Venezuela’s oil industry, which is in disarray after years of neglect.

There are also questions about whether Venezuela’s acting president, Delcy Rodríguez, has the authority to give NABEP 100-year rights over 17 oil fields with reserves of 65 billion barrels — and whether future Venezuelan or American administrations would overturn the agreement.

Venezuela's constitution states that arrangements like the one that the United States announced this week must be approved by the National Assembly, which has not happened, wrote Ian Vásquez, vice president for international studies at the Cato Institute.

“The deal lacks legitimacy since it was agreed to with a dictatorship that has clung to power for decades through violence and by committing what was probably the largest electoral fraud in Latin American history in 2024,” Vásquez wrote. “The agreement was also reached under overwhelming pressure, military and otherwise, from the United States. As such, any future Venezuelan democracy will question the deal, thus undermining confidence in the current arrangement.”

Wright on Wednesday told reporters during a joint press conference with Rodríguez pushed back on criticism.

“This is a deal that’s a massive win and benefit for the people of the United States of America and a massive win for the people of Venezuela," he said. "Because what it’s going to do is take resources that are underground, not helping anyone, and invest capital and money and technology and bring them to the surface to better the lives of Venezuelans, better supply energy to Americans.”

Trump has eyed Venezuela’s oil since the January capture of then-President Nicolás Maduro and has pressed to get U.S. businesses back into the country. “We have Exxon going in, we have Chevron going in. We have our big oil companies going in,” he said that same month.

He suggested again on Monday that other U.S. oil majors were preparing for a return, though other than Chevron, there is no evidence of that.

Exxon Mobil CEO Darren Woods said in January that Venezuela was “ uninvestable.” An Exxon spokesman said this week that “nothing has changed.”

The history of U.S. oil majors in Venezuela explains the hesitation.

Venezuela nationalized its oil industry in 1976 and created the state-owned company Petróleos de Venezuela S.A. A second nationalization occurred in 2007, when President Hugo Chávez pushed foreign oil companies into state-controlled joint ventures and seized the assets of companies that refused. Chevron agreed to a joint venture. Others, including Exxon and ConocoPhillips, refused, and Venezuela took their assets.

Trump has said that the agreement with Venezuela would “substantially lower” gasoline prices in the U.S. However, analyst have repeatedly warned that Venezuela’s dilapidated oil infrastructure will require years of restoration work and tens of billions of dollars to resuscitate.

“It could take 2 to 4 years to get new greenfield facilities online in the Orinoco region,” Amy Jaffe, director of the Global Energy, Climate, and Sustainability Lab at New York University, said in an email. "Other places where there is no pipeline and other kinds of support infrastructure could take longer.”

Meanwhile, the national average price for a gallon of regular gasoline jumped overnight to $4.12, according to the motor club AAA. That is 93 cents more than it cost at this point last year.

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