Heating Oil fell to 2.29 USD/Gal on September 23, 2025, down 0.30% from the previous day. Over the past month, Heating Oil's price has fallen 2.64%, but it is still 4.64% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Heating oil - values, historical data, forecasts and news - updated on September of 2025.

As of the fourth quarter of 2024, oil prices in the United Kingdom stood at 74 dollars per barrel, with prices expected to rise to 76.6 dollars a barrel in early 2025, before gradually falling in subsequent quarters.

Get the latest insights on price movement and trend analysis of Fuel Oil in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).

Heating oil price in the United States has peaked in winter 2022/23 at 4.31 U.S. dollars per gallon and has decreased ever since. Heating oil is a liquid petroleum product that is, among other things, used in residential buildings as a fuel oil in furnaces or boilers. Chemically, most heating oils are similar to motor diesel fuels and are often sold interchangeably. Forecast heating price in the U.S. The average price of heating oil in the United States in the winter of 2024/25 is expected to reach 3.44 U.S. dollars per gallon. Energy prices are projected to see a decrease this winter, because of increased production of heating fuels. The number of heating degree days, which are the days in which the average temperature is below 18 degrees Celsius (65 degrees Fahrenheit), also helps quantify the energy demand required to heat a building. What determines heating oil price? Generally, heating oil prices are collected during the heating season between October and March. In the U.S., the greatest determining factor for heating oil prices is the WTI crude oil price. Consumers can lower heating oil bills by considering when they purchase, reducing consumption, and through government assistance programs.

Crude Oil fell to 62.27 USD/Bbl on September 22, 2025, down 0.21% from the previous day. Over the past month, Crude Oil's price has fallen 3.91%, and is down 11.51% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on September of 2025.

United States EIA Forecast: Retail Price incl Tax: Heating Oil data was reported at 305.688 0.01 USD/gal in Dec 2019. This records an increase from the previous number of 300.541 0.01 USD/gal for Nov 2019. United States EIA Forecast: Retail Price incl Tax: Heating Oil data is updated monthly, averaging 281.432 0.01 USD/gal from Mar 2016 (Median) to Dec 2019, with 46 observations. The data reached an all-time high of 312.013 0.01 USD/gal in Jan 2019 and a record low of 196.745 0.01 USD/gal in Apr 2016. United States EIA Forecast: Retail Price incl Tax: Heating Oil data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.P003: Energy Price: Forecast: Energy Information Administration.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is the Heating Oil Index a Reliable Indicator of Market Trends?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Provides annual, quarterly, and monthly prices back to 1976 for gasoline , heating oil, and diesel fuel. Also provides annual prices from 1968 and quarterly and monthly prices from 1974 for imported crude oil. Based on Form EIA-878 data.

United States EIA Forecast: Refiner Price: Resale: Heating Oil data was reported at 211.592 0.01 USD/gal in Dec 2019. This records an increase from the previous number of 208.432 0.01 USD/gal for Nov 2019. United States EIA Forecast: Refiner Price: Resale: Heating Oil data is updated monthly, averaging 189.427 0.01 USD/gal from Mar 2016 (Median) to Dec 2019, with 46 observations. The data reached an all-time high of 214.758 0.01 USD/gal in Sep 2018 and a record low of 106.399 0.01 USD/gal in Mar 2016. United States EIA Forecast: Refiner Price: Resale: Heating Oil data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.P003: Energy Price: Forecast: Energy Information Administration.

Snapshot img { margin: 10px !important; } The fuel oil market size will decrease by USD 84.77 billion during 2020-2024. This report provides a detailed analysis of the market by application (marine, industrial, and others) and geography (APAC, Europe, MEA, North America, and South America). Also, the report analyzes the market’s competitive landscape and offers information on several market vendors, including BP Plc, Chevron Corp., Exxon Mobil Corp., JXTG Holdings Inc., PJSC LUKOIL, PT Pertamina(Persero), Qatar Petroleum, Reliance Industries Ltd., Royal Dutch Shell Plc, and SK Innovation Co. Ltd.

Market Overview

Browse TOC and LoE with selected illustrations and example pages of Fuel Oil Market

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Market Competitive Analysis

The fuel oil market is fragmented with numerous vendors that produce and supply fuel oil to customers. Vendors need to make high capital investments to remain competitive in the market. BP Plc, Chevron Corp., and Exxon Mobil Corp. are some of the major market participants. Although the rise in world energy demand will offer immense growth opportunities, the fluctuations in crude oil prices will challenge the growth of the market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

To help clients improve their market position, this fuel oil market forecast report provides a detailed analysis of the market leaders and offers information on the competencies and capacities of these companies. The report also covers details on the market’s competitive landscape and offers information on the products offered by various companies. Moreover, this fuel oil market analysis report also provides information on the upcoming trends and challenges that will influence market growth. This will help companies create strategies to make the most of future growth opportunities.

This report provides information on the production, sustainability, and prospects of several leading companies, including:

BP Plc
Chevron Corp.
Exxon Mobil Corp.
JXTG Holdings Inc.
PJSC LUKOIL
PT Pertamina(Persero)
Qatar Petroleum
Reliance Industries Ltd.
Royal Dutch Shell Plc
SK Innovation Co. Ltd.

Fuel Oil Market: Segmentation by Application

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The primary requirement of any marine engine is to propel the ship or generate onsite power by using the energy obtained from burning fuel oil. The mega marine engines of ships burn tons of fuel every day to propel the massively loaded ships. The rise in demand for bunker fuel oil due to the growing seaborne trade and growing naval activities will drive the demand for fuel oil for marine.

However, market growth in this segment will be slower than the growth of the market in the industrial and other segments. This report provides an accurate prediction of the contribution of all the segments to the growth of the fuel oil market size.

Fuel Oil Market: Segmentation by Geography

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North America will offer several growth opportunities to market vendors during the forecast period. The strong consumption of space heating fuel, growing refinery capacity, and proliferating marine trade will significantly influence fuel oil market growth in this region over the forecast period. The US is a key market for fuel oil in North America.

Fuel Oil Market: Key Drivers and Trends

The fluctuation in oil prices has affected the business of several oil and gas companies and refinancing companies. As a result, crude oil processing projects generate less revenue and many oil and gas companies suspend or postpone their exploration and production projects. Fluctuations in crude oil prices also impact investments in E&P and refining projects. Such factors will result in a slowdown in the growth of the global fuel oil market during the forecast period.

The adoption of blockchain in the oil and gas industry helps in overcoming several issues including the complexity of logistics, high fuel prices, and environmental pollution. Blockchain platforms facilitate secure and faster transactions between the entities and maintain transparency. Blockchain also helps in reducing cash cycle time and intermediary costs. These benefits will result in an increase in the adoption of blockchain to enhance the overall operational efficiency of the existing refineries. As a result of such factors, the fuel oil market will register a CAGR of (13)% during the forecast period.

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Fuel Oil Market: Key Highlights of the Report for 2020-2024

CAGR of the market during the forecast period 2020-2024
Detailed information on factors that will drive fuel oil market growth during the next five years
Precise estimation of the f

This dataset provides values for HEATING OIL reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The heating oil market, while facing challenges, presents a robust investment opportunity. Driven by consistent demand in colder climates and a growing preference for reliable and efficient home heating solutions, the market exhibits a stable trajectory. Although the exact market size is not provided, a reasonable estimation considering similar markets and reported CAGRs (assuming a CAGR of 3-5% based on industry trends) would place the 2025 market value at approximately $5 billion USD, with projections suggesting continued growth through 2033. Key market drivers include the ongoing need for reliable home heating, particularly in regions with harsh winters, and the availability of advanced heating oil technologies that improve efficiency and reduce emissions. However, restraints such as environmental regulations promoting cleaner energy sources, fluctuating crude oil prices, and increasing competition from alternative heating solutions like natural gas and heat pumps, pose challenges to sustained rapid growth. Market segmentation is likely to include residential, commercial, and industrial sectors, each with its own unique growth drivers and challenges. The leading companies mentioned (Tevis Energy, Certas Energy, Crown Oil, etc.) are likely competing primarily on pricing, service quality, and efficient supply chain management. The forecast period of 2025-2033 will likely see a shift toward more sustainable heating oil practices and a greater focus on blending and the development of biofuels. Furthermore, regional variations will be significant; regions with colder climates and limited access to natural gas will continue to exhibit stronger demand than those with milder winters and readily available alternatives. Companies that effectively adapt to environmental regulations, enhance supply chain resilience, and invest in advanced technologies will be best positioned for success. A thorough understanding of the evolving regulatory landscape and consumer preferences will be critical for market players looking to secure a leading position. Strategic partnerships and mergers and acquisitions are anticipated in this dynamic market.

Brent rose to 67.60 USD/Bbl on September 23, 2025, up 1.55% from the previous day. Over the past month, Brent's price has fallen 1.74%, and is down 10.07% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Brent crude oil - values, historical data, forecasts and news - updated on September of 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Heating Oil Index Forecast: Slight Uptick Predicted

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Avg Retail Price: Heating Fuel: Newfoundland and Labrador: St. John's data was reported at 149.700 0.01 CAD/l in Mar 2025. This records a decrease from the previous number of 150.600 0.01 CAD/l for Feb 2025. Avg Retail Price: Heating Fuel: Newfoundland and Labrador: St. John's data is updated monthly, averaging 77.600 0.01 CAD/l from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 235.000 0.01 CAD/l in May 2022 and a record low of 31.300 0.01 CAD/l in Dec 1998. Avg Retail Price: Heating Fuel: Newfoundland and Labrador: St. John's data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.P015: Fuel Oil Price at Self Service Filling Stations. [COVID-19-IMPACT]

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Heating Oil TR/CC CRB Index Anticipates Seasonal Price Volatility

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Heating Oil TR/CC CRB Spot Market Signals Potential Price Volatility.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Heating Oil TR/CC CRB Forecast Predicts Volatile Trading for Coming Months

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

On September 15, 2025, the Brent crude oil price stood at 67.45 U.S. dollars per barrel, compared to 63.3 U.S. dollars for WTI oil and 71.01 U.S. dollars for the OPEC basket. Oil prices rose slightly that week.Europe's Brent crude oil, the U.S. WTI crude oil, and OPEC's basket are three of the most important benchmarks used by traders as reference for global oil and gasoline prices. Lowest ever oil prices during coronavirus pandemic In 2020, the coronavirus pandemic resulted in crude oil prices hitting a major slump as oil demand drastically declined following lockdowns and travel restrictions. Initial outlooks and uncertainty surrounding the course of the pandemic brought about a disagreement between two of the largest oil producers, Russia and Saudi Arabia, in early March. Bilateral talks between global oil producers ended in agreement on April 13th, with promises to cut petroleum output and hopes rising that these might help stabilize the oil price in the coming weeks. However, with storage facilities and oil tankers quickly filling up, fears grew over where to store excess oil, leading to benchmark prices seeing record negative prices between April 20 and April 22, 2020. How crude oil prices are determined As with most commodities, crude oil prices are impacted by supply and demand, as well as inventories and market sentiment. However, as oil is most often traded in future contracts (where a contract is agreed upon while product delivery will follow in the next two to three months), market speculation is one of the principal determinants for oil prices. Traders make conclusions on how production output and consumer demand will likely develop over the coming months, leaving room for uncertainty. Spot prices differ from futures in so far as they reflect the current market price of a commodity.

The global fuel oil market, valued at approximately $150 billion in 2025, is projected to experience robust growth, driven primarily by increasing energy demand in developing economies and the persistent reliance on fuel oil in certain industrial sectors like maritime transport and power generation. The market's compound annual growth rate (CAGR) is estimated at 3.5% from 2025 to 2033, indicating a steady expansion despite the global push towards cleaner energy sources. Key drivers include the ongoing need for heavy fuel oil in shipping, particularly in emerging markets with less stringent emission regulations, and its continued use in certain industrial processes where it remains a cost-effective energy source. However, stringent environmental regulations aimed at reducing sulfur emissions, coupled with the increasing adoption of cleaner alternatives such as natural gas and renewable energy sources, pose significant restraints on market growth. The market is segmented by application (transportation, petrochemical, petroleum refineries, building) and type (distillate and residual fuel oil), with residual fuel oil holding a larger market share due to its widespread use in the maritime and power generation sectors. Geographic analysis reveals that Asia-Pacific, particularly China and India, will be a major growth engine due to their expanding industrial sectors and rising energy consumption. North America and Europe, while mature markets, will continue to contribute significantly, although their growth rates will likely be slower than those in developing regions. The competitive landscape is dominated by major integrated oil and gas companies such as Gazprom, Rosneft, ExxonMobil, PetroChina, BP, Royal Dutch Shell, Chevron, Petrobras, Lukoil, Total, and Equinor. These companies wield significant influence over production, distribution, and pricing, shaping the market dynamics. Future growth hinges on the balance between the persistent demand for cost-effective energy solutions and the increasing pressure to transition towards cleaner and more sustainable energy alternatives. The market's trajectory will be significantly influenced by government policies, technological advancements in emission control, and the evolving global energy landscape. Companies are investing in cleaner fuel technologies and diversifying their portfolios to adapt to the changing market conditions.