Saudi Aramco, bp, and Eni deploy quantum computing algorithms in seismic exploration workflows to improve subsurface imaging accuracy
Over the past two years, Saudi Aramco, bp, and Eni have moved quantum computing out of the research lab and into active seismic exploration workflows. That’s one of the biggest computational shifts upstream oil and gas has seen in decades. Working with quantum tech firms Quantinuum and D-Wave Quantum, these energy majors are now applying quantum algorithms to real subsurface imaging and reservoir analysis — tasks that classical supercomputers are increasingly struggling to handle.
The signal is clear: quantum seismic processing has crossed from promising experiment to operational tool.
Energy majors adopt quantum seismic processing in exploration operations
Saudi Aramco, bp, and Eni didn’t just run pilots and walk away. Over the past two years, each company established dedicated quantum subsidiaries and direct partnerships with Quantinuum and D-Wave Quantum, focused on geophysical optimization and subsurface mapping. These aren’t proof-of-concept arrangements — they’re operational collaborations producing results inside live exploration workflows.
The companies deploying these tools today — Saudi Aramco, bp, and Eni among them — are building capabilities that will compound in value as quantum hardware matures.
What’s being deployed are hybrid classical-quantum systems. Classical high-performance computers handle data prep and preprocessing, while quantum processors take on the heaviest tasks: seismic migration, well-placement refinement, reservoir monitoring. That division of labor reflects where the technology actually stands today, not where the marketing suggests it might be. Quantum computing has moved from something energy companies studied to something they’re actively using.
Classical computing limits in seismic imaging drive the shift to quantum systems
The push toward new approaches isn’t coming from novelty. It’s coming from geology. As exploration pushes into sub-salt formations and deepwater reservoirs, the seismic data volumes needed for accurate imaging have simply outgrown what classical supercomputers can practically handle.
Traditional seismic algorithms manage that overload through approximations — simplifying the physics to cut computational cost. Those shortcuts degrade imaging accuracy, especially in high-velocity-contrast environments like thick salt layers where the seismic signal gets heavily distorted. The result is uncertainty, and uncertainty in exploration is expensive.
Qubits change the math. Because they can exist in superposition — representing multiple states simultaneously — quantum systems perform the matrix operations at the core of seismic imaging across many possible solutions at once. That’s a genuine speedup for problems classical systems solve sequentially and imperfectly. A single deepwater exploration well can cost over $100 million, so even modest gains in imaging accuracy translate directly into fewer dry holes and smarter capital allocation. Better subsurface pictures aren’t an academic win — they’re a cost-reduction lever.
Quantum processing reduces reservoir analysis time and improves discovery rates
Speed is one of the most immediate practical benefits. Quantum seismic processing can compress the time needed to produce a processed seismic volume from months to days — a compression that matters enormously for exploration teams working under budget and schedule pressure.
Accuracy gains are equally significant. Because quantum algorithms can simulate seismic wave propagation with higher fidelity than classical approximations allow, geophysicists can identify features — fracture networks, fluid boundaries — that conventional methods might miss entirely. Those features often determine whether a reservoir is commercially viable.
The benefits extend to 4D seismic processing, which tracks how a reservoir changes over time rather than capturing a single static snapshot. Quantum-processed 4D data produces more accurate models of fluid movement within formations, supporting better decisions about enhanced oil recovery — where and when to inject water or gas to sustain production pressure. Large acreage blocks that classical systems could only evaluate at reduced resolution can now be assessed at higher fidelity, enabling more thorough prospect evaluation before anyone commits to drilling.
Hardware limitations and near-term workarounds shape current deployment
The technology has real constraints, and the companies deploying it aren’t pretending otherwise. Current quantum computers are small in scale and susceptible to noise, which can introduce errors into calculations. Both factors limit the size of seismic problems that quantum hardware can fully solve today.
The hybrid model is the practical answer. By offloading only the most computationally intensive tasks to quantum processors — while classical systems handle everything else — companies extract real value from quantum capabilities without waiting for fault-tolerant hardware to arrive. It’s an unglamorous workaround, but it works.
Quantum-inspired algorithms offer another near-term path forward. These apply quantum mathematical principles to classical hardware, delivering performance improvements over standard classical methods without requiring actual quantum processors. They don’t provide the full speedup of true quantum computing, but they let companies build relevant software expertise right now. Energy companies are also actively shaping the hardware roadmap by supplying real-world geophysical datasets and specific industrial use cases to quantum hardware developers — helping direct the development of processors optimized for the kinds of problems exploration actually generates.
What this means for upstream digitalization—and what comes next
Quantum seismic processing doesn’t exist in isolation. It’s part of a broader upstream digitalization push applying data-driven technologies across the full oil and gas value chain, from exploration through production.
One concrete expression of that push is the reservoir digital twin — a continuously updated computational model of a subsurface formation. Quantum computing serves as the simulation engine for these dynamic models, enabling real-time responsiveness that static classical models simply can’t support.
The competitive logic is straightforward. Faster appraisal and development timelines increase the net present value of any discovery, and speed to first oil is a genuine competitive advantage. Quantum processing shortens the timeline between finding a reservoir and producing from it. That’s why national oil companies and international majors alike are securing access to quantum hardware and developing proprietary algorithms tailored to their specific geological challenges. The companies deploying these tools today — Saudi Aramco, bp, and Eni among them — are building capabilities that will compound in value as quantum hardware matures. The computational bottleneck in subsurface imaging is breaking open, and the race to exploit that opening has already started.
Kelly is an experienced writer with 15 years of experience exploring the big stories that shape our world, from tech breakthroughs and space exploration to climate, energy, and the fascinating quirks of science. She has a talent for turning complex ideas into sharp, memorable insights that stay with readers long after they’ve finished reading.
