Dataset

This dataset was created by Ban Le Van

Contents

The Big Data in Oil and Gas Exploration & Production Market Report is Segmented by Component (Hardware, Software, and Services), Deployment Mode (On-Premise, Cloud, and Hybrid/Edge-Enabled), Data Type (Structured, Unstructured, and Semi-structured/Streaming), Application (Reservoir Management and EOR, Drilling and Well Planning, Predictive Maintenance, and More), and Geography (North America, Europe, Asia-Pacific, and More).

The Ministry of Energy and Energy Industries (MEEI) has published valuable information showing the annual statistics on Natural Gas Production for 1908 - 2018 and Crude Oil and Condensate Production for 1908 - 2018.

This is a large dataset of extracted text from public Oil and gas documents that was prepared in the run up to the FORCE 2023 Large Languagel model Hackathon in Stavanger, Norway

The dataset is uninque since it contains the largest public collection of extracted text from Ocr'ed oil and gas documents currently available. It has been created with the aim to make more oil and gas documents knowledge better embedded in language modelsAdditional the text has been classified in if the extracted pages are real text or mostly gibberish.Personal identifiable information has been removed as best as possibleA file with 1500 hand classified pages is part of the upload to further train text classifiers.

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Big Data Market In Oil And Gas Sector Size 2025-2029

The big data market in oil and gas sector size is forecast to increase by USD 31.13 billion, at a CAGR of 29.7% between 2024 and 2029.

In the Oil and Gas sector, the adoption of Big Data is increasingly becoming a strategic priority to optimize production processes and enhance operational efficiency. The implementation of advanced analytics tools and technologies is enabling companies to gain valuable insights from vast volumes of data, leading to improved decision-making and operational excellence. However, the use of Big Data in the Oil and Gas industry is not without challenges. Security concerns are at the forefront of the Big Data landscape in the Oil and Gas sector. With the vast amounts of sensitive data being generated and shared, ensuring data security is crucial. The use of blockchain solutions is gaining traction as a potential answer to this challenge, offering enhanced security and transparency. Yet, the implementation of these solutions presents its own set of complexities, requiring significant investment and expertise. Despite these challenges, the potential benefits of Big Data in the Oil and Gas sector are significant, offering opportunities for increased productivity, cost savings, and competitive advantage. Companies seeking to capitalize on these opportunities must navigate the security challenges effectively, investing in the right technologies and expertise to secure their data and reap the rewards of Big Data analytics.

What will be the Size of the Big Data Market In Oil And Gas Sector during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleIn the oil and gas sector, the application of big data continues to evolve, shaping market dynamics across various sectors. Predictive modeling and pipeline management are two areas where big data plays a pivotal role. Big data storage solutions ensure the secure handling of vast amounts of data, enabling data governance and natural gas processing. The integration of data from exploration and production, drilling optimization, and reservoir simulation enhances operational efficiency and cost optimization. Artificial intelligence, data mining, and automated workflows facilitate decision support systems and data visualization, enabling pattern recognition and risk management. Big data also optimizes upstream operations through real-time data processing, horizontal drilling, and hydraulic fracturing. Downstream operations benefit from data analytics, asset management, process automation, and energy efficiency. Sensor networks and IoT devices facilitate environmental monitoring and carbon emissions tracking. Deep learning and machine learning algorithms optimize production and improve enhanced oil recovery. Digital twins and automated workflows streamline project management and supply chain operations. Edge computing and cloud computing enable data processing in real-time, ensuring data quality and security. Remote monitoring and health and safety applications enhance operational efficiency and ensure regulatory compliance. Big data's role in the oil and gas sector is ongoing and dynamic, continuously unfolding and shaping market patterns.

How is this Big Data In Oil And Gas Sector Industry segmented?

The big data in oil and gas sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ApplicationUpstreamMidstreamDownstreamTypeStructuredUnstructuredSemi-structuredDeploymentOn-premisesCloud-basedProduct TypeServicesSoftwareGeographyNorth AmericaUSCanadaEuropeFranceGermanyRussiaAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Application Insights

The upstream segment is estimated to witness significant growth during the forecast period.In the oil and gas industry's upstream sector, big data analytics significantly enhances exploration, drilling, and production activities. Big data storage and processing facilitate the analysis of extensive seismic data, well logs, geological information, and other relevant data. This information is crucial for identifying potential drilling sites, estimating reserves, and enhancing reservoir modeling. Real-time data processing from production operations allows for optimization, maximizing hydrocarbon recovery, and improving operational efficiency. Machine learning and artificial intelligence algorithms identify patterns and anomalies, providing valuable insights for drilling optimization, production forecasting, and risk management. Data integration and data governance ensure data quality and security, enabling effective decision-making through advanced decision support systems and data visual

This pipeline dataset used in different sector like oil and gas sector and fertilizers. This dataset is about 1000 pipeline dataset with different pipe size, materials, material grade, corrosion impact etc

U.S. Federal Oil & Gas Monthly Production & Disposition (2015–2025)

🌎 Project Overview

This dataset provides a comprehensive, cleaned, and analysis-ready record of the monthly production and disposition volumes of U.S. federal oil and natural gas resources from January 2015 through September 2025. Compiled from the authoritative OGOR-B reporting forms and curated by the U.S. Department of the Interior (DOI), Office of Natural Resources Revenue (ONRR), it reflects the official federal and Native American natural resource production data.

The dataset captures not only raw production volumes but also detailed disposition categories, which indicate how these resources are sold, measured, or allocated, making it a crucial resource for energy policy analysis, market forecasting, and sustainability research.

🌐 Context & Source Information

• Publisher: U.S. Department of the Interior – Office of Natural Resources Revenue (ONRR)
• Original Dataset: Monthly Production Disposition of U.S. Natural Resources Revenue (OGOR-B)
• Coverage: Federal lands, federal waters (Offshore regions: Alaska, Gulf of Mexico, Pacific), and Native American lands. Location data is intentionally withheld for Native American production to protect privacy and sovereignty.
• Temporal Scope: January 2015 – September 2025, monthly frequency.
• License: Creative Commons CCZero (CC0) – Public Domain. Anyone can use, modify, and distribute this data.
• Official Landing Page: ONRR Monthly Production Disposition Data

This dataset is a cornerstone for those researching U.S. energy economics, resource management, climate impact studies, and policy development.

📊 Data Dictionary

🛠️ Data Cleaning & Preprocessing

To ensure the dataset is analysis-ready, the following preprocessing steps were applied:

• Missing Value Handling:

  • State / County / FIPS Code: Missing values primarily correspond to offshore records and were logically filled with the label Offshore.
  • Offshore Region: Missing values indicate onshore records and were filled with the label Onshore.

• Data Standardization:

  • Standardized date formats for seamless time-series analysis.
  • Converted numerical volumes from string with commas to clean numeric types.

• Quality Assurance:

  • Verified consistency of commodity labels and disposition codes.
  • Confirmed monthly frequency across the 10-year time span.

These enhancements enable immediate use in machine learning pipelines, econometric models, and visual analytics without additional preprocessing.

🚀 Potential Use Cases

📊 Energy Economics & Market Forecasting

• Model long-term trends in U.S. oil and natural gas production.
• Correlate production with market prices or global economic indicators.
• Forecast supply-demand dynamics using ARIMA, Prophet, or LSTM models.

🌍 Environmental & Policy Research

• Quantify the environmental impact of onshore versus offshore production.
• Assess how federal production contributes to greenhouse gas emissions when combined with emissions datasets.
• Evaluate the effectiveness of U.S. energy policies and their evolution over a decade.

📈 Financial & Business Intelligence

• Integrate production volumes into energy sector investment models.
• Build revenue projections and budget impact analyses for federal and tribal authorities.
• Use disp...

The size of the Big Data in Oil & Gas Exploration and Production Market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 10.20">> 10.20% during the forecast period. Recent developments include: Cloud-based technology and solutions have become an essential tool for the energy sector, especially in the Middle East, to store data and analyze it. The COVID-19 pandemic boosted the growing cloud computing in the oil and gas industry in recent years.. Key drivers for this market are: 4., Uninterrupted and Reliable Power Supply and Heavy Deployment of DG (diesel generator) Set4.; Improvement in Technology of Diesel Generator. Potential restraints include: 4., The Growing Trend of Renewable Power Generation. Notable trends are: Big Data Software to Dominate the Market.

Big Data in Oil & Gas Market By Size, Share, Trends, Opportunity, and Forecast, 2018-2028, Segmented By Components, By Application, By Data Type, By Region, Competition Forecast and Opportunities

This dataset contains information about world oil production for OPEC, OECD and the major non-OPEC producers. for 1983-2021. Data from Saudi Central Bank (SAMA). Follow datasource.kapsarc.org and it’s APIs to stay in sync and advance energy economics research.Note:* Including Condensates and Natural gas liquids

This data release contains several datasets that provide an overview of oil and gas well history and production of the United States, from 1817 to September 1, 2022. Well history data is aggregated into 1-mile and 10-mile squares indicating the total number of wells and counts of wells classified as oil, gas, dry, injection, hydraulically fractured, and/or horizontal wells. Well history is also separated into layers binned on 1-year increments from a well's spud date (date drilling commenced). Production data is aggregated in 2-mile and 10-mile squares that sum the total production of oil, gas, and water volumes. Production data is also separated into layers binned on 1-year increments to reflect the year of production. These aggregations are compiled from data from IHS Markit, which is a proprietary, commercial database. No proprietary data is contained in this release. This data release was updated May 2023 to reflect an offset of 1 year on the original release.

County-level data from oil and/or natural gas producing States—for onshore production in the lower 48 States only—are compiled on a State-by-State basis. Most States have production statistics available by county, field, or well, and these data were compiled at the county level to create a database of county-level production, annually for 2000 through 2011. Raw data for natural gas is for gross withdrawals, and oil data almost always include natural gas liquids. Note that State-provided natural gas withdrawals were not available for Illinois or Indiana; those estimates were produced using geocoded wells and State total production reported by the U.S. Department of Energy’s Energy Information Agency. In the data file, counties with increases or decreases in excess of $20 million in oil and/or natural gas production during 2000-11 are also identified. See the Documentation for more details. Currently, an ERS update to this data product is not planned.

The Oil and Gas fields (points) is a dataset which contains a point representation of the location of oil and gas fields for display on map products. Show full description

This dataset contains information about world's oil production for 1965-2020. Data from BP. Follow datasource.kapsarc.org for timely data to advance energy economics research.Notes:* Includes crude oil, shale oil, oil sands and NGLs (natural gas liquids - the liquid content of natural gas where this is recovered separately).** Excludes liquid fuels from other sources such as biomass and derivatives of coal and natural gas.*** Excludes Estonia, Latvia and Lithuania prior to 1985 and Slovenia prior to 1990.**** Annual changes and shares of total are calculated using million tonnes per annum figures.

This dataset contains annual production information of oil and gas wells in New York State from 2001 to present.

The Division of Mineral Resources publishes four datasets to the Open New York Data project regarding oil and gas. The datasets titled is updated daily and includes the same data fields as the downloadable well data provided directly from this website. Open Data provides additional tools to export the data in a variety of file formats and allows users to create custom visualizations. Dataset that contain geographic coordinates are also presented as maps.

According to Cognitive Market Research, the global Big Data in Oil and Gas Sector market size is projected to reach USD XX million by 2024 and is expected to expand at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.

The global Big Data in Oil and Gas Sector market is anticipated to grow significantly, with a projected CAGR of XX% between 2024 and 2031.
North America is expected to hold a major market share of more than XX%, with a market size of USD XX million in 2024, and is forecasted to grow at a CAGR of XX% from 2024 to 2031 due to the advanced technological infrastructure and the high adoption rate of digital technologies in the oil and gas sector.
The upstream application segment held the highest Big Data in Oil and Gas Sector market revenue share in 2024, attributed to the critical role of big data in exploration and production activities, optimizing reservoir performance, and minimizing risks.

Market Dynamics - Key Drivers of the Big Data in Oil and Gas Sector

Integration of Advanced Analytics for Enhanced Decision-Making Drives the Big Data in Oil & Gas Market

The Big Data in Oil & Gas market is driven by the adoption of advanced analytics, where cost efficiency is a major achievement. Big data analytics processes complex datasets for better predictions and optimisations. Its affordability relative to other precious metals like gold and platinum further amplifies its appeal. As Big Data is further integrated, the development of the Oil & Gas Sector is buoyed by enhancing decision-making, efficiency, and safety.

For instance, ExxonMobil, in their "2020 Energy & Carbon Summary" report, highlighted the use of advanced seismic imaging and data analytics to improve the accuracy of subsurface exploration, thereby reducing drilling risks and enhancing operational efficiency.

IoT Deployment for Real-Time Monitoring and Efficiency Further Propel the Big Data in Oil & Gas Market

The rising demand for monitored infographics and data analytics is to fuel the Big Data in the Oil & Gas market. The deployment of IoT devices facilitates real-time monitoring and operational efficiency. This development aligns with the broader shift towards self-sufficiency and positive capital allocations. As IoT sensors on equipment and in operations provide critical data for predictive maintenance and decision-making, contributing to the shift from capital expenditure to operational expenditure in multiple outsourced activities for the businesses.

Schlumberger, in their "Digital Transformation in the Oil and Gas Industry" report, discussed implementing IoT solutions to monitor well operations, which has led to significant improvements in maintenance strategies and operational efficiencies.

Market Dynamics - Key Restraints of the Big Data in Oil and Gas Sector

Data Security and Privacy Concerns is a Challenge for the Big Data in Oil & Gas Market

With the companies storing all the its data on every aspect of business for a more efficient future working, there is still room for avoidable threats. The rising demand for big data might come with the threat of Data security and privacy are significant concerns with the increasing use of big data analytics, given the oil and gas sector's sensitive nature. Cyber threats limit the adoption of big data solutions, limiting the demand for Big data in the Oil & Gas market.

The International Energy Agency (IEA), in its "Digitalization & Energy" report, highlighted the cybersecurity challenges facing the energy sector, emphasizing the need for robust security measures in the adoption of digital technologies, including big data analytics.

Integration and Interoperability Challenges will Restraint the Big Data in Oil & Gas Market

Data access, analysis, and storage are becoming more and more of an issue for businesses. Compatibility and interoperability issues arise when big data technologies are integrated with legacy systems. The integration process is made more difficult by the diversity of data sources and formats. Most firms are finding it necessary to evaluate new technologies and legacy infrastructure as the needs of Big Data outpace those of traditional relational databases.

A study by Deloitte, titled "Digital Transformation: Shaping the Future of the Oil and Gas Industry", identified integration of new technologies with existin...

🛢️ Predictive Maintenance for Oil & Gas Pipelines – 1000

📌 Dataset Overview

This dataset contains 1,000 samples of pipeline data collected from the oil and gas industry, intended for use in predictive maintenance modeling. Each record represents sensor and operational data from pipelines, with corresponding labels indicating whether maintenance was required.

The goal is to develop models that can predict potential failures or maintenance needs before they occur, ensuring pipeline safety, reducing downtime, and minimizing operational costs.

🧾 Data Features

Each row in the dataset corresponds to a specific pipeline segment or instance and includes the following:

Pipe Size: Diameter of the pipeline

Thickness: Measured wall thickness of the pipe

Material: Type of material used (e.g., steel, composite)

Maximum Pressure: Peak pressure experienced (psi)

Temperature: Internal fluid temperature (°C)

Corrosion Impact Percentage: Estimated corrosion level (%)

**Thickness Loss: **Loss of wall thickness due to wear or corrosion

Material Loss Percentage: Percentage of overall material loss

Year Times: Age or time in service (years)

**Conditions: **Operational condition category (Normal, Moderate, Critical)

Maintenance_Required (Target): Binary label (1 = maintenance needed, 0 = no maintenance)

⚠️ This is synthetic data generated to reflect realistic conditions in oil and gas operations. It is suitable for training and testing machine learning models for predictive maintenance purposes.

🎯 Use Cases

Predictive maintenance modeling

Classification and anomaly detection

Feature importance and sensor optimization

Exploratory data analysis (EDA) for oil and gas operations

📊 Ideal For

Data scientists working on industrial or IoT data

Researchers focused on fault detection or reliability engineering

ML practitioners developing predictive maintenance systems

🧠 Suggested ML Tasks

Binary Classification

Time-Series Analysis (if timestamped versions available)

Feature Engineering for sensor-based data

Model Interpretability (e.g., SHAP, LIME)

This dataset comprises a collection of tabular data and graphical images supporting the U.S. Geological Survey's National Oil and Gas Assessment (NOGA) for San Joaquin Basin Province (010). The dataset includes detailed information on crude oil and natural gas production, including volumetric and descriptive data such as cumulative production, remaining reserves, and known recoverable volumes. Historical data covering field-discovery dates, well completion dates, exploration objectives, and well depths are also provided. Data sources include commercial databases along with supplemental information from various federal and state agencies. No proprietary data is included in this. The dataset is presented in multiple formats, including .pdf files for graphical images and .tab files for tabular data, encompassing eco-regional, federal land, ownership parcels, and state-wise data distributions.

U.S. Crude Oil Imports

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8734253%2Fa7802f9fbe1ee0095b2940d3b0fdccd9%2Fcrude-oil-banner.png?generation=1714313995249268&alt=media" alt=""> Background: Crude oil is a naturally occurring, unrefined petroleum product composed of hydrocarbon deposits and other organic materials. It is a fossil fuel that is refined to produce usable products such as gasoline, diesel, and various forms of petrochemicals. The United States imports crude oil from various countries to supplement its domestic production.

Dataset Description

This dataset provides detailed information about U.S. crude oil imports by month for every year from 2009 to 2024. The data includes the country of origin, the U.S. port of entry, the name of the oil company, the type of crude oil, and the volume imported (in thousands of barrels).

File Structure

The dataset is provided in a CSV format with the following columns:

Potential Use Cases

This dataset can be used for various purposes, including: 1. Analyzing U.S. crude oil import patterns: The data can help identify the major countries exporting crude oil to the United States, the most common grades of crude oil imported, and the primary ports of entry. 2. Investigating the impact of crude oil imports on the U.S. economy: By combining this data with other economic indicators, researchers can explore the relationship between crude oil imports and various aspects of the U.S. economy, such as GDP, employment, and inflation. 3. Optimizing supply chain management: Oil companies and refineries can use this data to better understand their supply chains and make informed decisions about sourcing, transportation, and storage of crude oil. 4. Forecasting future trends: By analyzing historical import data, researchers can develop models to forecast future trends in U.S. crude oil imports, which can help inform policy decisions and business strategies. 5. Environmental impact assessment: The data can be used to estimate the environmental impact of crude oil imports, such as the carbon footprint associated with transportation and refining processes.

Overall, this dataset provides a comprehensive overview of U.S. crude oil imports for January 2009, offering valuable insights for researchers, policymakers, and industry professionals interested in the energy sector and its impact on the U.S. economy.

Credits

• This data was sourced from the U.S. Energy Information Administration.
• Image credits: ProGas LLC, Forbes Advisor

This dataset contains crude oil production for Bahrain from 2000-2019. Data from National Oil & Gas Authority (NOGA). Follow datasource.kapsarc.org and it’s APIs for timely data to advance energy economics research. Note: Production is for Bahrain Field.