Upstream oil and gas operations rely on high-performance computing (HPC) to analyze subsurface data, simulate reservoir behavior, plan wells, and optimize production. These activities inform high-value decisions, so faster computation and broader scenario coverage can create substantial value. Despite decades of progress in HPC, several upstream problems remain unsolved at the desired scale or detail level. Quantum computing may eventually help address some of these bottlenecks, but its value, will depend on whether it can complement or outperform continually improving classical methods under realistic industry conditions.
To identify the most promising opportunities, the Quantum Economic Development Consortium (QED-C) and the Research and Development Technical Section of the Society of Petroleum Engineers (SPE) convened a two-day symposium in May 2026. The event brought together oil and gas specialists, many with expertise in HPC, and quantum computing experts to identify computational challenges suited to quantum or hybrid quantum-classical approaches. QED-C and SPE organized the discussions around three areas central to upstream operations: seismic imaging, modeling and simulation, and production optimization and flow assurance. Participants evaluated candidate applications based on technical suitability, potential business value, and readiness for further development.
The symposium identified five principal use cases across these three areas. In seismic imaging, participants identified elastic waveform simulation as the principal use case and examined seismic acquisition planning. Elastic waveform simulation is a computationally demanding method for modeling how seismic waves propagate through heterogeneous subsurface formations. Quantum simulation may eventually improve the scaling or time-to-solution of waveform calculations. Optimization methods could improve the placement of seismic sources and receivers under geological, operational, and cost constraints. Elastic waveform simulation is a longer-term opportunity that depends on advances in gate-based quantum computing, whereas acquisition planning may offer an earlier opportunity for hybrid optimization.
In modeling and simulation, participants identified faster porous-media flow simulation and materials design as candidate applications. Hybrid quantum-classical methods could accelerate selected calculations within existing reservoir simulators, allowing operators to evaluate more reservoir models, wider uncertainty ranges, and more development scenarios. Quantum computational chemistry may also help identify materials and chemicals that can withstand harsh subsurface conditions, improve recovery, or support flow assurance. These applications could create value, but quantum-enabled high-fidelity reservoir and materials simulations will depend on further advances in quantum hardware, algorithms, and data transfer methods between classical and quantum systems.
In production optimization and flow assurance, participants examined multi-well production optimization and hydraulic fracturing design. Field-level production decisions require choosing interventions under capital, infrastructure, crew availability, and network constraints. Hydraulic fracturing design involves parameters including stage and cluster spacing, fluid and proppant volumes, pressures, and injection rates. Because these problems are discrete, constrained, and often nonlinear, they may suit for experiments with hybrid optimization or quantum annealing. Demonstrating value will require comparison with leading classical optimization methods and, ultimately, validation with field data and scaled pilots.
Symposium participants viewed quantum computing as augmenting, not replacing, the HPC systems that currently support upstream operations. The strongest opportunities involve specific computational problems with defined performance measures and operational relevance. In most cases, quantum capabilities will likely be integrated into existing simulators, optimization platforms, cloud or HPC environments, or service-provider offerings. New computational technologies typically reach upstream operations through an established ecosystem of operators, oilfield service companies, software vendors, equipment manufacturers, and internal technology teams.
Deploying new HPC hardware, software, and methods at scale requires supplier qualification, technical validation, pilot testing, workflow integration, cybersecurity and data governance review, user acceptance, and ongoing operational support. Established vendors and partnerships may therefore play a role in translating quantum capabilities into deployable and supportable tools that fit existing industry workflows. Oil and gas companies will judge these capabilities based on operational performance, including accuracy, reliability, computational cost, ease of integration, and effects on decision-making, rather than on technological novelty.
The symposium highlighted steps that industry and government should consider to accelerate the application of quantum computing to oil and gas computational challenges. This report offers four recommendations for industry, government, and technology stakeholders:
The next stage for quantum computing in oil and gas should focus on precise problem formulation, access to representative data, rigorous comparisons with leading classical methods, and collaborative development of applications where better computation could affect operational decisions and field economics.