Refinery Operations Data
Throughput, crack spreads, turnaround schedules, and unit availability -- the operational data that crude traders and energy investors use to forecast product supply.
No listings currently in the marketplace for Refinery Operations Data.
Find Me This Data →Overview
What Is Refinery Operations Data?
Refinery Operations Data encompasses the critical metrics that drive crude oil processing and product supply forecasting: throughput volumes, crack spreads (the profit margin between crude input and refined product output), turnaround schedules (planned maintenance windows), and unit availability rates. This data is essential for crude oil traders, energy investors, and refinery operators who need to understand real-time and projected supply dynamics across gasoline, diesel, jet fuel, and other refined products. The global oil refining market is valued at USD 1.92 trillion in 2025 and projected to reach USD 2.81 trillion by 2035, with operational efficiency and capacity utilization driving market performance across all regions and end-use sectors including transportation, aviation, petrochemical, and marine bunker operations.
Market Data
USD 1,917.52 billion
Global Oil Refining Market Size (2025)
Source: Precedence Research
USD 2,800.91 billion
Projected Market Size (2035)
Source: Precedence Research
4.30%
Expected CAGR (2026–2035)
Source: Precedence Research
~39% of market
Gasoline Share of Oil Refining Output
Source: Fortune Business Insights
37% of global revenue
Asia Pacific Regional Share (2025)
Source: Precedence Research
Who Uses This Data
What AI models do with it.do with it.
Crude Oil Traders
Use refinery throughput, crack spreads, and turnaround schedules to forecast product supply, time entry and exit points, and manage portfolio risk across regional refining capacity shifts.
Energy Investors & Hedge Funds
Monitor unit availability, capacity utilization, and maintenance calendars to predict margin compression or expansion; inform long-term positions in refiner equities and energy commodities.
Refinery Operators & Planners
Track operational metrics (energy intensity, yield, unplanned downtime) and integrate data analytics with digital twins to optimize production schedules, reduce costs, and improve compliance with environmental regulations.
Petrochemical & Transportation Suppliers
Use feedstock availability and refinery output forecasts to plan production runs, secure contracts, and align supply chain logistics with expected product yields.
Pricing depends on the proposed terms
We do not have a verified, comparable price for your dataset. Consider the permitted uses, license term, exclusivity, provenance, coverage and quality. A seller asking price is a proposal, not an appraisal or guaranteed sale. Compare prices only when the source, date, currency, unit and license scope are known.
What Buyers Expect
What makes it valuable.valuable.
Accuracy & Timeliness
Real-time or near-real-time throughput and unit availability data; any delays in turnaround notifications reduce trader value. Crack spread calculations must align with published benchmarks.
Granular Coverage
Facility-level or unit-level operational metrics; aggregated data has limited utility for precise supply forecasting and margin analysis. Regional and facility-specific breakdowns critical for hedging decisions.
Compliance & Regulatory Alignment
Data must reflect adherence to environmental regulations, fuel quality standards, and emissions controls. Buyers validate that refinery operations meet stringent local and international standards.
Historical & Contextual Data
Multi-year operational trends, seasonal patterns, and maintenance cycle history enhance analytical value. Buyers integrate legacy data with current operational metrics to model forecasts and stress scenarios.
Integration-Ready Format
APIs, structured feeds, and compatibility with analytics platforms (digital twin systems, trading models) are essential. Data must feed seamlessly into financial modeling and risk management workflows.
Potential applications and organizations
Who's buying.buying.
Major integrated energy companies that operate global refining networks; use internal and external operational data to optimize capacity utilization, manage turnarounds, and forecast product yields across regions.
Large state-owned and national oil companies managing substantial refining capacity in Asia and emerging markets; rely on operational metrics to coordinate regional supply and meet domestic fuel demand.
Deploy refinery operational data within digital twin ecosystems to model process improvements, predict maintenance needs, and quantify ROI from process optimization and reliability enhancements.
FAQ
Common questions.questions.
What specific operational metrics comprise refinery operations data?
Core metrics include crude throughput (barrels per day), crack spreads (the price differential between crude input and refined product outputs like gasoline and diesel), unit availability rates (percentage of time major processing units operate), planned turnaround schedules (maintenance windows), yield rates (product output per barrel of crude), and energy intensity. These metrics are tracked at facility or unit level to enable supply forecasting and margin analysis.
How do traders and investors use crack spread data?
Crack spreads represent the profit margin a refinery earns from processing crude oil. Traders monitor these spreads to anticipate refinery run rates, predict product supply tightness or oversupply, and time commodity positions. Narrowing spreads signal margin compression and potential production cuts; widening spreads encourage refinery utilization. This data directly informs crude and product futures strategies.
Why are turnaround schedules valuable to the market?
Planned refinery turnarounds (major maintenance) temporarily remove capacity from the market, reducing throughput and tightening product supply. Advance knowledge of turnaround timing allows traders to position for supply tightness, helps logistics planners adjust inventory and distribution, and enables refiners to coordinate production across facilities. Early visibility into industry turnaround calendars is a significant competitive advantage.
How does refinery operations data connect to digital twin and process optimization investments?
Operational data (energy intensity, yield, unplanned downtime) feeds digital twin models that simulate refinery performance and test optimization scenarios. These integrations have demonstrated ROI of 300% with payback periods of six months, according to case studies. Refinery operators license operational data and analytics to optimize process control, reduce energy costs, improve safety, and maximize output quality.
Sell yourrefinery operationsdata.
Describe your refinery operations data and the uses you are authorized to offer. Price, legal suitability, and buyer interest require separate evaluation. No match or sale is guaranteed.
Prepare a Listing