It has been a decade since the Paris Agreement established the goal of limiting global warming to below 2°C relative to pre‑industrial levels. Since then, investment in renewable energy has grown from $1.1 trillion to more than $2 trillion, representing nearly twice the amount directed toward fossil fuel projects, according to the International Energy Agency’s (IEA) World Energy Investment report.
This spending trend has been supported by global policy mandates, growing participation of private sector, and technological advancements from the scientific and engineering community.
Forecasts from the IEA’s Renewables report estimate renewable energy will account for almost 20% of final energy consumption by 2030, up from 13% in 2023, with solar, wind, and hydroelectric power accounting for much of this expansion.
Despite this momentum, adoption remains uneven. The IEA also points out that outside of China, emerging markets contribute only 15% of global renewable energy investment. Limited access to financial capital and infrastructure were cited as key hurdles to more progress.
One overlooked factor is public resistance. As highlighted in a recent report by the Institute of Development Studies, when solar and wind farms compete with agricultural resources, these projects are often subject to public opposition from those who depend on the land for their livelihoods.
Renewable energy is not without its drawbacks. It is inherently intermittent, which is further compounded by limitations in energy storage technologies. This often requires utilizing traditional fossil fuel power plants as backup when solar or wind output is unavailable.
Geographic location and land availability pose additional development challenges. One example of this is the Asia-Pacific region outside of China, where key elements such as solar irradiation, wind density, and hydrological resources are limited. As noted in an article in the Center for Strategic & International Studies (CSIS), these limitations are a big reason why we are seeing varying degrees of renewable uptake in the region.
In parallel, global energy demand is steadily rising thanks to population growth, the spread of industrialization, and the energy-intensive buildout of data centers to support artificial intelligence (AI).
Although many have predicted that oil and coal will be phased out, we cannot forget about gas demand. The Energy Institute’s Statistical Review of World Energy expects natural gas use to grow by 32% in the power sector by 2050, largely due to the ongoing shift in many countries toward liquefied natural gas which has created a growing global gas trade.
Why Oil and Gas Still Matter
Beyond meeting energy demand, oil and gas will be essential to advance emerging net-zero technologies that require technical expertise and infrastructure long established in the domain.
One of the best examples is geothermal energy which stands out as being able to provide continuous baseload power at higher utilization rates compared to intermittent wind and solar resources. The IEA’s Future of Geothermal Energy report estimates global investment in geothermal to exceed $2.5 trillion and supply up to 15% of electricity demand by 2050 if technology development continues.
Realizing this potential requires drilling wells as deep as 7000 m to reach favorable geothermal conditions. Fortunately, drilling systems and other technologies from the oil and gas industry have a decades-long track record in reaching high-temperature and high-pressure conditions at extreme depths.
Another emerging pillar in the transition is low-emissions hydrogen due to its versatility and near-zero emissions. The IEA’s Global Hydrogen Overview projects that production of low-emissions hydrogen could reach up to 49 million mtpa by 2030, nearly 30% more than projected in the 2023 report.
Today, most of the hydrogen produced is through the downstream industry’s use of steam methane reforming (SMR) which extracts hydrogen from natural gas. This type of hydrogen is known as gray hydrogen because it often generates significant emissions. Blue hydrogen is a cleaner alternative that also relies on SMR and natural gas, but involves carbon capture, utilization, and storage (CCUS) to cancel out the high emissions.
A study published in Sustainable Energy and Fuels estimates CCUS technologies can prevent the release of 80 to 90% of the emissions that otherwise would escape into the atmosphere. By contrast, green hydrogen is produced via electrolysis which splits water molecules into oxygen and hydrogen and is completely powered by renewable energy to generate the least volume of emissions.
Despite the apparent upsides, major limitations remain for expanding the hydrogen market. Persistent issues include high capital costs, energy losses during conversion, and insufficient infrastructure.
The oil and gas industry remains likely to play a role in however the hydrogen market shapes up because it can provide the feedstock and CCUS-capabilities. Additionally, the industry can leverage its substantial infrastructure, capital, and decades of logistics expertise in transportation.
Beyond its role in blue hydrogen production, CCUS remains one of the most promising solutions for decarbonizing hard-to-abate sectors such as cement and steel manufacturing, shipping, and aviation. The DNV’s Energy Transition Outlook report expects global CCUS capacity to quadruple by 2030 and capture 6% of global emissions by 2050, largely driven by deployments in hard-to-abate sectors.
Similarly, the oil and gas industry is well positioned to advance CCUS deployment at scale, given its technical expertise in subsurface storage characterization, drilling and completion for injection, and the use of CO2 for enhanced oil recovery, while existing midstream infrastructure offers pathways for the transportation and handling of captured carbon.
Each of these examples highlight the role of oil and gas domains to be a significant driver of emerging net-zero technologies. However, global progress will require more cross-industrial collaboration that leverages each sector’s unique expertise to reduce emissions and meet climate change goals.
Big Data and LLM Deployment
As the oil and gas industry supports new pathways toward net zero, there is a growing need for it to focus on optimizing its own operations. The industry is defined by large-scale and energy-intensive processes across all value chains. This spans drilling, completion, and production in upstream, to transportation in midstream, and refining and petrochemicals in downstream.
The industry is also defined by the abundance of data generated from decades of operations, including numerical datasets from sensors, operational reports compiled by engineers, and a rich body of domain literature. Yet much of this data remains underutilized, leaving an untapped reservoir of actionable insights.
But if the trend lines continue to head where many believe, the oil and gas data management sector is expected to grow from $27 billion in 2024 to approximately $86 billion by 2034, according to a Global Market insights report.
Disruptive technology is commonly defined as an innovation that fundamentally reshapes existing markets, forcing industries to adapt or lose competitive advantage. Large language models (LLMs), built at unprecedented scale with trillions of parameters and petabytes of training data, are emerging as a clear example.
Before the advent of LLMs, the utilization of unstructured data, which a MIT Sloan study said represents about 80% of data generated, was limited due to the inability of traditional machine-learning approaches to capture semantics and extract insights from natural language. But now, the vast mountain of unstructured data presents a unique opportunity to leverage the new technology to drive efficiencies further.
What oil and gas companies are realizing is that the deployment of LLMs makes it possible to uncover hidden insights within operational records, industry best practices, and domain literature. One overarching idea is that this new knowledge, generated with the help of AI, will guide the future of engineering design, enable faster incident detection, improve overall performance, and reduce nonproductive time.
Nevertheless, developing LLMs from scratch requires enormous computational resources, substantial data, and specialized technical expertise. For oil and gas, that means the near-term opportunity is to harness existing pre-trained models using fine-tuning, retrieval frameworks, agentic systems, and vector embeddings to turn general AI capability into domain-driven performance gains and yes, lower emissions.
To a large degree, this represents an unexplored area of research with significant potential, much like the advances in traditional machine learning on structured data.
The Road to Net Zero
Reaching net zero certainly cannot be achieved through renewables alone. It is critical to consider all activities that produce emissions and work collaboratively to achieve required climate goals.
At some stage, the oil and gas industry will shift from being the world’s primary energy provider to an enabler of emerging net-zero technologies. Its technical expertise in subsurface environments and fluids transportation positions it as the primary driver for geothermal energy, hydrogen production, and CCUS technologies. Together, these efforts will play a pivotal role in decarbonization and ensuring a sustainable energy mix.
However, the industry’s carbon footprint remains a concern which will demand solutions built on efficiency and data-driven decision-making. The abundance of data creates an opportunity to deploy disruptive LLM technologies that can unlock the hidden potential of underutilized datasets.
The industry that long fueled the world’s energy demand is set to enable emerging technologies and drive the world toward the path to net zero.
For Further Reading
World Energy Investment 2024 by IEA (2024).
Renewables 2024: Global Overview by IEA (2024).
Resistance to Clean Energy Transitions in Low- and Middle-Income Countries by M. Zaidan and J. Georgalakis. Brighton Institute of Development Studies (2024).
The Role of Fossil Fuels in the Pursuit of Decarbonization by K. Shizawa. Center for Strategic & International Studies (2023).
2025 Statistical Review of World Energy by The Energy Institute (2025).
The Future of Geothermal Energy by the IEA (2024).
Global Hydrogen Review 2024 by the IEA (2024).
On the Climate Impacts of Blue Hydrogen Production by C. Bauer, et al. Sustainable Energy & Fuels (2022).
Energy Transition Outlook CCS to 2050 by S. Andersen, A. Boulet, A. Bove, X. Cao, S. Cochet, P. Fanailoo, and M. Garrido. DNV (2025).
Oil Gas Data Management Market Size—By Solution, By Deployment, By Application, By End Use, Growth Forecast, 2025–2034 by P. Wadhwani and S. Jaiswal. Global Market Insights (2025).
Tapping the Power of Unstructured Data by T. Harbert. MIT Sloan (2021).
Odai Elyas, SPE, is pursuing a PhD in civil and environmental engineering at the Massachusetts Institute of Technology, where his research applies advanced artificial intelligence on multimodal data structures to optimize large-scale drilling operations, with a focus on lowering costs and reducing carbon footprint. He previously worked for Saudi Aramco as a research engineer in drilling technologies and has experience as a gas drilling engineer and a drilling rig foreman. Elyas holds a MSc in petroleum engineering from The University of Texas at Austin and dual BSc degrees in petroleum engineering and energy business and finance from The Pennsylvania State University.