A comprehensive dataset for WTI crude oil price prediction, combining key economic indicators and oil industry metrics from 2005-2024. Features include: - EUR/USD exchange rates - Oil inventory levels - Production volumes - Rig counts - Inflation rates - Technical indicators (rolling averages)

Data sourced from EIA and FRED APIs, processed and engineered for time series forecasting. Ideal for price prediction models and market analysis.

Dataset prepared with proper cleaning and feature engineering.

Crude Oil fell to 59.17 USD/Bbl on December 2, 2025, down 0.25% from the previous day. Over the past month, Crude Oil's price has fallen 3.08%, and is down 15.40% 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 December of 2025.

Crude Oil Production in the United States increased to 13844 BBL/D/1K in September from 13800 BBL/D/1K in August of 2025. This dataset provides the latest reported value for - United States Crude Oil Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Brent fell to 63.05 USD/Bbl on December 2, 2025, down 0.19% from the previous day. Over the past month, Brent's price has fallen 2.84%, and is down 14.36% 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 December of 2025.

The Value of exported petroleum.

Crude Oil Production in Saudi Arabia increased to 10002 BBL/D/1K in October from 9966 BBL/D/1K in September of 2025. This dataset provides the latest reported value for - Saudi Arabia Crude Oil Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Heating Oil rose to 2.35 USD/Gal on December 2, 2025, up 0.21% from the previous day. Over the past month, Heating Oil's price has fallen 2.25%, but it is still 6.31% 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 December of 2025.

The 2025 annual OPEC basket price stood at ***** U.S. dollars per barrel as of August. This would be lower than the 2024 average, which amounted to ***** U.S. dollars. The abbreviation OPEC stands for Organization of the Petroleum Exporting Countries and includes Algeria, Angola, Congo, Equatorial Guinea, Gabon, Iraq, Iran, Kuwait, Libya, Nigeria, Saudi Arabia, Venezuela, and the United Arab Emirates. The aim of the OPEC is to coordinate the oil policies of its member states. It was founded in 1960 in Baghdad, Iraq. The OPEC Reference Basket The OPEC crude oil price is defined by the price of the so-called OPEC (Reference) basket. This basket is an average of prices of the various petroleum blends that are produced by the OPEC members. Some of these oil blends are, for example: Saharan Blend from Algeria, Basra Light from Iraq, Arab Light from Saudi Arabia, BCF 17 from Venezuela, et cetera. By increasing and decreasing its oil production, OPEC tries to keep the price between a given maxima and minima. Benchmark crude oil The OPEC basket is one of the most important benchmarks for crude oil prices worldwide. Other significant benchmarks are UK Brent, West Texas Intermediate (WTI), and Dubai Crude (Fateh). Because there are many types and grades of oil, such benchmarks are indispensable for referencing them on the global oil market. The 2025 fall in prices was the result of weakened demand outlooks exacerbated by extensive U.S. trade tariffs.

Crude Assay Databases Market Outlook

According to our latest research, the global crude assay databases market size reached USD 1.12 billion in 2024, reflecting a robust demand for advanced data management solutions across the oil and gas sector. The market is anticipated to expand at a CAGR of 7.1% from 2025 to 2033, projecting a value of USD 2.09 billion by 2033. This growth is primarily driven by the increasing complexity of crude oil blends, stringent regulatory requirements, and the need for precise feedstock selection to optimize refining processes and trading strategies. As per our comprehensive analysis, the adoption of digitalization and data analytics in the oil and gas industry has significantly contributed to the expansion of the crude assay databases market.

The primary growth factor for the crude assay databases market is the rising complexity and variability of crude oil sources. As oil fields mature and new unconventional sources come online, the diversity of crude oil characteristics has increased, necessitating more granular and comprehensive assay data for accurate evaluation and processing. Refineries and trading entities are increasingly relying on sophisticated databases to compare, analyze, and select the most suitable crude blends for their operations, thereby reducing operational risks and maximizing profitability. Additionally, the growing trend of blending crude oils from multiple sources to meet specific market or regulatory requirements further amplifies the need for reliable and up-to-date assay data, propelling the demand for advanced crude assay databases.

Another significant driver is the rapid digital transformation within the oil and gas sector. The integration of big data analytics, artificial intelligence, and cloud computing has revolutionized the way data is collected, stored, and utilized. Modern crude assay databases are equipped with powerful analytical tools that enable users to perform real-time simulations, scenario analyses, and predictive modeling. This not only enhances operational efficiency but also supports strategic decision-making in trading, refining, and exploration activities. The shift towards digital platforms and cloud-based solutions has also improved accessibility, scalability, and security of critical assay data, further accelerating market growth.

Stringent environmental regulations and the increasing focus on sustainability are also shaping the crude assay databases market. Regulatory bodies across the globe are imposing stricter controls on emissions and product quality, compelling oil and gas companies to meticulously analyze the properties of crude oil before processing. Accurate assay data is essential for compliance with these regulations, as it enables refineries to optimize their processes, minimize waste, and produce cleaner fuels. Furthermore, the growing emphasis on carbon footprint reduction and energy efficiency is prompting industry stakeholders to invest in advanced database solutions that facilitate sustainable operations and transparent reporting.

From a regional perspective, North America continues to dominate the crude assay databases market, supported by the presence of major oil producers, technologically advanced refineries, and a robust digital infrastructure. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid industrialization, expanding refining capacity, and increasing investments in digital technologies. Europe and the Middle East & Africa are also witnessing steady growth, fueled by the need for compliance with stringent environmental standards and the ongoing modernization of oil and gas infrastructure. Latin America, while smaller in market share, is expected to register notable growth due to rising exploration activities and investments in data management solutions.

Database Type Analysis

The crude assay databases market is segmented by database type into public databases and proprietary databases. Public databases, often maintained by government agencies, industry associations, or international organizations, provide open access to a wide range of crude oil assay data. These databases play a crucial role in supporting academic research, regulatory compliance, and industry benchmarking. However, their scope and depth may be limited compared to proprietary offerings, as they often focus on widely traded or regionally significant crude grades. Public da

Integrity Data Visualization for Oil and Gas Market Outlook

According to our latest research, the global market size for Integrity Data Visualization for Oil and Gas reached USD 1.98 billion in 2024, advancing at a robust CAGR of 10.6% during the forecast period. The market is projected to reach USD 5.04 billion by 2033. This impressive growth is primarily driven by increasing digital transformation initiatives, stringent regulatory requirements, and the urgent need for real-time decision-making across the oil and gas sector. The adoption of advanced data visualization tools is enabling organizations to enhance operational efficiency, proactively manage asset integrity, and minimize risks associated with complex oil and gas infrastructures.

The Integrity Data Visualization for Oil and Gas Market is experiencing significant traction due to the rising complexity of oil and gas operations and the critical need for proactive asset management. As oil and gas infrastructure ages, the risk of failures and accidents escalates, compelling companies to invest in sophisticated visualization solutions that provide actionable insights from vast and disparate data sources. These solutions enable operators to monitor the health and performance of pipelines, refineries, and production assets in real time, facilitating predictive maintenance and reducing unplanned downtime. The integration of IoT devices and sensors further amplifies the volume of data generated, necessitating robust visualization platforms that can synthesize and present information in an intuitive, actionable format. This trend is particularly pronounced in regions with mature oil and gas assets, where the cost of failure can be catastrophic both financially and environmentally.

Another key growth driver for the Integrity Data Visualization for Oil and Gas Market is the increasing regulatory scrutiny and compliance requirements imposed by governments and industry bodies worldwide. Regulations governing pipeline integrity, environmental protection, and occupational safety are becoming more stringent, compelling oil and gas companies to adopt advanced monitoring and reporting tools. Data visualization platforms are instrumental in helping organizations track compliance metrics, document inspection and maintenance activities, and generate audit-ready reports. By automating these processes, companies can not only ensure compliance but also streamline operations and reduce administrative overhead. The ability to demonstrate transparency and accountability through clear, visual data representations is becoming a competitive differentiator in the industry.

Technological advancements such as artificial intelligence, machine learning, and cloud computing are further propelling the Integrity Data Visualization for Oil and Gas Market. These technologies enhance the capability of visualization tools to analyze large datasets, identify patterns, and predict potential failures before they occur. Cloud-based solutions, in particular, offer scalability, flexibility, and cost-effectiveness, making advanced data visualization accessible to organizations of all sizes. The convergence of these technologies is enabling oil and gas companies to move beyond reactive maintenance to a predictive and prescriptive approach, ultimately improving asset reliability and reducing operational costs. This shift is fostering a culture of data-driven decision-making across the industry, positioning data visualization as a cornerstone of digital transformation strategies.

The concept of the Digital Oilfield is revolutionizing the oil and gas industry by integrating advanced technologies to enhance operational efficiency and productivity. By leveraging digital tools, companies can optimize exploration and production processes, reduce costs, and improve safety. The Digital Oilfield encompasses a range of technologies, including data analytics, IoT, and automation, which work together to provide real-time insights into operations. This integration allows for better decision-making, predictive maintenance, and streamlined workflows. As the industry continues to embrace digital transformation, the Digital Oilfield is becoming a critical component in achieving sustainable growth and competitive advantage.

From a regional perspective, North America currently leads the Integrity Data Visualization for Oil and Gas Marke

Context

The crude oil price movements are subject to diverse influencing factors. This dataset was retrieved from the U.S. Energy Information Administration: Europe Brent Spot Price FOB (Dollars per Barrel)

Content

The aim of this dataset and work is to predict future Crude Oil Prices based on the historical data available in the dataset. The data contains daily Brent oil prices from 17th of May 1987 until the 13th of November 2022.

Acknowledgements

Dataset is available on U.S. Energy Information Administration: Europe Brent Spot Price FOB (Dollars per Barrel) which is updated on weekly bases.

Inspiration

The vast competition in the Data Science field and the availability of the new Prophet method made it easier to predict future prices, that is what you may find when predicting the oil prices with this dataset.

Index Time Series for iShares Oil & Gas Exploration & Production UCITS ETF USD (Acc). The frequency of the observation is daily. Moving average series are also typically included. NA

The WTI (West Texas Intermediate) crude dataset is a time series dataset that contains daily historical prices of WTI crude oil. It provides the market price of crude oil per barrel in USD for each day from the past till the present.

Oil prices trend

The data contains oil prices from 1998 from 8 countries.

Content

The data is self explanatory. The price is described in USD for the crude oil data and in local currencies for the fuel products.

Acknowledgements

The data is available from IEA website

This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is Table 12, and contains only the reference case. The dataset uses 2009 dollars per gallon. The data is broken down into crude oil prices, residential; commercial, industrial, transportation, electric power and refined petroleum product prices.

Day–price (PHP) dataset of petroleum products in Manila City, Philippines. The USD-PHP conversion rate is included in the third column.

Europe Brent Spot Price FOB (Dollars per Barrel)

This dataset contains daily historical prices for Europe Brent Spot Price FOB (Free On Board) measured in dollars per barrel. The data spans from May 20, 1987 to September 29, 2025, providing a comprehensive time series of one of the world's most important crude oil benchmarks.

Brent crude is a major trading classification of sweet light crude oil that serves as a major benchmark price for purchases of oil worldwide. This dataset is valuable for energy market analysis, economic research, commodity trading strategies, and understanding historical oil price trends.

The data was sourced from the U.S. Energy Information Administration (EIA), which collects, analyzes, and disseminates independent and impartial energy information.

License: CC0 1.0 Universal (Public Domain) Source: https://www.eia.gov/dnav/pet/hist/RBRTED.htm

You are members of the analytic department in one of the Alberta Oil Sands extraction companies. You are given a current project to collect and clean the data and choose, fit and validate the model for further continuous prediction of demand for the company's products. This will allow the company to assess profitability and to set the appropriate volumes of production.

In short, you need to use the historical data of https://www.eia.gov/dnav/pet/hist/rwtcW.htm in its weekly version, and to predict it for the available weeks of 2021, to evaluate the quality of your prediction and to compose a report for your management. Before working with real data, you first check the intended model on simulated data.

Below we suggest the specific steps of analysis for those who like the detailed instructions. However, these steps may be changed by those who prefer free creativity.

The moral that we are trying to learn in this assignment is that it is easy to forecast series generated by a certain family of models. However, it is hard to forecast the real cases.

We will study the necessary material for the whole semester. However, steps in italic, you can start immediately. I suggest you to start early, because the volume is high.

Part 1. Artificial data

1.1. Simulation.

1.1.1. For ARIMA(p, d, q), set p = 2
d = 1
q = 2
phi_1 = -.2
phi_2 = .15
theta_1 = .3
theta_2 = -.1
sd = 0.03
and generate a series of sample size n = 1000, using this model. 1.1.2. Add a linear trend y(t) = b0 + b1*t, using the coefficients intercept b0 = -1 and slope b1= 0.0015. 1.1.3. Apply an exponential function.

1.2. Exploratory analysis in the order, inverse to the simulation steps.

1.2.1. Divide the generated set into a training set head and a test set tail. 1.2.2. Logarithm the training set series. 1.2.3. Detect a linear trend by regression. Compare the estimated trend parameters to true ones. 1.2.4. Detrend the series. 1.2.5. In the same axes, plot the original ARIMA simulation and the current (trended, exponentiated, logarithmed and finally detrended) series. They should have the same shape, but differ by a bit of shift and stretch. 1.2.6. ARIMA fit. 1.2.6.1. By 3 nested loops over p, d and q between 0 and 3, print all values of AIC in 3 4-by-4-tables. Choose the triple, minimizing AIC. Compare it to the true (p, d, q) triple and comment. 1.2.6.2. Fit the model by auto.arima command. Comment on its choice of p, d and q, comparing to true values and those chosen by triple loop. 1.2.6.3. Leave out those attempts of order estimations and choose the true (p, d, q) triple. Fit ARIMA(2, 1, 2), using the function forecast::Arima, to the training data. 1.2.7. Compare the estimated ARIMA parameters to true ones. Comment on goodness of fit.

1.3. Forecasting the simulated series.

1.3.1. Forecast the testing part of the ARIMA, using forecast::forecast function. 1.3.2. Add the estimated trend. 1.3.3. Exponentiate that trended forecast.

1.4. Evaluation of the results

1.4.1. Plot the forecast values, prediction interval, and the real testing set in the same axes. 1.4.2. Plot acf of the testing set and its prediction, and ccf between them. 1.4.3. Plot the residuals and their acf. 1.4.4. Estimate the forecast error.

Part 2. Real data.

2.1. Data sourcing and visualization.

2.1.1. Read the dataset https://www.kaggle.com/statistics101guy/wti-spot-price-fob-dollars-per-barrel 2.1.2. Plot the series and its acf.

2.2. Exploratory analysis

2.2.1. Divide the series into a training set (up to 2020 inclusively) and testing set (2021). 2.2.2. Logarithm the series. 2.2.3. Estimate the linear trend by the least squares procedure. 2.2.4. Detrend the series. 2.2.5. By “auto.arima” command of “forecast” library, fit ARIMA(p, d, q) to the training data.

2.3. Forecasting in the order, inverse to the exploratory analysis steps

2.3.1. Using the “forecast” function of the “forecast” library, forecast your ARIMA model for the period of testing set. 2.3.2. Extrapolate your linear trend to this period and add it to your ARIMA forecast. 2.3.3. Exponentiate the result.

2.4. Evaluation of the forecast quality

2.4.1. Plot the forecast values, prediction interval, and the real testing set in the same axes. 2.4.2. Plot acf of the testing set and its prediction, and ccf between them. 2.4.3. Plot the residuals and their acf. 2.4.4. Estimate the forecast error. 2.4.5. Comment on the results

Part 3. Reporting

3.1. Compose a business report of about 10 pages length, following the approximate structure:

3.1.1. Title page, listing the group members, project title, school, course, submission date. 3.1.2. Executive summary, containing your view of the problem setting, brief description of the intended analysis and all that usually pertains to this section 3.1.3. Ana...

Context

Crude oil is the world's most critical energy source and one of the most actively traded commodities. Its price is a fundamental driver of the global economy, influencing everything from transportation costs and industrial production to inflation rates and geopolitical policy. This dataset tracks the price of West Texas Intermediate (WTI) crude oil, a high-quality grade that serves as a primary global benchmark.

Access to reliable, long-term historical data is crucial for economists, traders, and data scientists seeking to model market dynamics, analyze the impact of world events, and forecast economic trends. This dataset provides a comprehensive and daily-updated record of crude oil prices, sourced from the Crude Oil Futures (CL=F) market.

Content

This dataset contains daily price information for Crude Oil Futures (CL=F) in a clean, tabular format. Each row represents a single trading day and includes the following columns:

Date: The date of the trading session (YYYY-MM-DD).

Open: The price at which crude oil first traded for the day in USD per barrel.

High: The highest price reached during the trading day in USD per barrel.

Low: The lowest price reached during the trading day in USD per barrel.

Close: The closing price at the end of the trading day in USD per barrel.

Volume: The total number of futures contracts traded during the day.

AI in Oil and Gas Market Outlook

According to our latest research, the global AI in Oil and Gas market size reached USD 3.89 billion in 2024, reflecting robust adoption across the industry. The market is expected to grow at a CAGR of 12.7% from 2025 to 2033, reaching a forecasted value of USD 11.45 billion by 2033. This impressive growth trajectory is primarily driven by the sector’s increasing need for operational efficiency, cost reduction, and the optimization of complex processes. As per the latest research, the integration of artificial intelligence technologies is fundamentally transforming the oil and gas industry, with AI-driven solutions enabling companies to enhance productivity, improve safety, and mitigate operational risks.

One of the core growth factors for the AI in Oil and Gas market is the sector’s urgent demand for advanced data analytics. Oil and gas operations generate vast amounts of data from exploration, drilling, and production activities. Traditionally, much of this data remained underutilized due to the limitations of conventional data processing methods. AI-powered solutions, such as machine learning and deep learning algorithms, are now enabling companies to extract actionable insights from these massive datasets. This has led to significant improvements in decision-making, resource allocation, and the identification of new hydrocarbon reserves. The ability to analyze seismic data with AI, for example, allows geologists to pinpoint drilling locations with greater accuracy, thereby reducing exploration risks and costs.

Another significant driver is the adoption of AI for predictive maintenance and asset management. Oil and gas infrastructure, such as drilling rigs and pipelines, are subject to wear and tear, which can result in costly downtime and safety hazards. By leveraging AI-based predictive maintenance tools, operators can monitor equipment health in real time, detect anomalies, and forecast potential failures before they occur. This proactive approach not only extends the lifespan of critical assets but also minimizes unplanned outages and enhances overall safety. The resulting reduction in maintenance costs and increased uptime have been major incentives for companies to invest in AI technologies.

Furthermore, the ongoing digital transformation initiatives within the oil and gas sector are facilitating the rapid deployment of AI solutions. As companies strive to remain competitive in an era of fluctuating oil prices and tightening environmental regulations, digitalization has become a strategic imperative. The integration of AI with other digital technologies, such as the Internet of Things (IoT) and cloud computing, is enabling seamless data flow across the value chain. This synergy is fostering innovation in areas like supply chain optimization, reservoir management, and energy trading. The convergence of AI and digitalization is thus accelerating the pace of innovation and driving the sustained growth of the AI in Oil and Gas market.

Regionally, North America continues to hold the largest share of the global AI in Oil and Gas market, followed closely by Europe and Asia Pacific. The United States, in particular, has witnessed widespread adoption of AI-driven technologies in both upstream and downstream operations. This is attributed to the presence of major oil and gas companies, a mature digital infrastructure, and significant investments in research and development. Meanwhile, the Middle East and Africa are emerging as high-growth regions, driven by increased exploration activities and a growing focus on operational efficiency. The regional outlook for the AI in Oil and Gas market remains positive, with each region presenting unique opportunities and challenges for stakeholders.

Component Analysis

The AI in Oil and Gas market is segmented by component into software, hardware, and services, each playing a pivotal role in the industry’s digital transformation. Software solutions occupy the largest share, as they form the backbone of AI-driven analytics, predictive modeling, and process automation. AI software platforms are increasingly being adopted for tasks such as seismic data interpretation, drilling optimization, and production forecasting. These platforms leverage advanced algorithms to process complex data sets, enabling oil and gas companies to make data-driven decisions with greater speed and accuracy. The demand for customized AI software, tailored to specific ope