This dataset contains information about Nigeria's petroleum products for 2010-2021. Data from National Bureau of Statistics, Nigeria.

Version: GOGI_V10_2This data was downloaded as a File Geodatabse from EDX at https://edx.netl.doe.gov/dataset/global-oil-gas-features-database. This data was developed using a combination of big data computing, custom search and data integration algorithms, and expert driven search to collect open oil and gas data resources worldwide. This approach identified over 380 data sets and integrated more than 4.8 million features into the GOGI database.Access the technical report describing how this database was produced using the following link: https://edx.netl.doe.gov/dataset/development-of-an-open-global-oil-and-gas-infrastructure-inventory-and-geodatabase” Acknowledgements: This work was funded under the Climate and Clean Air Coalition (CCAC) Oil and Gas Methane Science Studies. The studies are managed by United Nations Environment in collaboration with the Office of the Chief Scientist, Steven Hamburg of the Environmental Defense Fund. Funding was provided by the Environmental Defense Fund, OGCI Companies (Shell, BP, ENI, Petrobras, Repsol, Total, Equinor, CNPC, Saudi Aramco, Exxon, Oxy, Chevron, Pemex) and CCAC.Link to SourcePoint of Contact: Jennifer Bauer email:jennifer.bauer@netl.doe.govMichael D Sabbatino email:michael.sabbatino@netl.doe.gov

The Oil and Gas Rights dataset contains the digital boundaries for existing exploration licences, significant discovery licences, production licences, former permits, former leases and the Norman Wells Proven Area. These boundaries are available for download on the Northern petroleum pesources Website at https://www.rcaanc-cirnac.gc.ca/eng/1100100036087/1538585604719. The Oil and Gas Rights dataset is Crown-Indigenous Relations and Northern Affairs Canada (CIRNAC) and Indigenous Services Canada (ISC) primary source for northern petroleum titles geographic location on maps.

Oil, gas and water produced, and water used for hydraulic fracturing treatments for wells in and near the Permian Basin during 2000-2019 was estimated using data reported in IHS Markit (TM) (2020). Hydraulic fracturing treatment data from IHS Markit (TM) (2020) may include volumes in a variety of measurement units, and they may include multiple treatments per well. All listed treatments within the study area were converted to gallons and summed on a per-well basis, discounting any treatments for which the specified measurement units were unclear (for example, “sacks”, or “feet”), which were minor. The per-well treatment volumes and oil, gas, and water production were then aggregated via summation to a 1-mile grid using ArcGIS functions. The annual aggregated hydraulic fracturing treatment data were exported as annual GeoTIFF images with a resolution of 1 square mile per pixel and bundled into a archive file. This data is not part of the USGS Aggregated Water Use Database (AWUDS) or the National Water Information System (NWIS).

Big Data in Oil & Gas Market is estimated to reach USD 10.1 billion By 2034, Riding on a Strong 12.5% CAGR throughout the forecast period.

Big Data in Oil and Gas Market Outlook

As of 2023, the global big data in oil and gas market size is estimated to be approximately USD 21.5 billion. With a projected compound annual growth rate (CAGR) of 12.6%, the market is expected to surge to an impressive USD 55.2 billion by 2032. This considerable expansion is driven by the industry's increasing reliance on data analytics to enhance operational efficiency, optimize resource management, and minimize environmental impacts. The oil and gas sector is witnessing transformative changes propelled by technological advancements, necessitating the deployment of big data platforms to harness vast amounts of data generated across the value chain.

One of the primary growth drivers for the big data market in oil and gas is the urgent need for operational efficiency and cost reduction. With fluctuating oil prices and mounting environmental concerns, companies are under pressure to extract resources more efficiently and sustainably. Big data analytics helps in predicting equipment failures before they occur, optimizing drilling processes, and improving reservoir management. By leveraging predictive analytics and data-driven insights, oil and gas companies can make informed decisions that significantly reduce downtime, enhance production rates, and lower operational costs. This technological adoption is further accelerated by the integration of artificial intelligence and machine learning, which enable more sophisticated data analysis capabilities.

Another pivotal factor contributing to the market's growth is the increasing adoption of Internet of Things (IoT) solutions within the industry. IoT devices, such as sensors and drones, collect massive volumes of data from oil rigs, pipelines, and refineries. These data points are essential for monitoring equipment health, environmental conditions, and energy consumption. Big data analytics platforms process and analyze this information in real-time, allowing companies to quickly respond to operational challenges and optimize asset performance. The convergence of IoT and big data is revolutionizing the oil and gas sector by enhancing safety protocols, reducing emissions, and facilitating more efficient resource allocation.

The growing emphasis on environmental sustainability and regulatory compliance is also fueling market growth. Governments and environmental organizations worldwide are imposing stringent regulations on the oil and gas industry to minimize its ecological footprint. Big data analytics plays a crucial role in helping companies adhere to these regulations by monitoring emissions, tracking energy usage, and ensuring compliance with environmental standards. Furthermore, data-driven insights assist in planning and executing cleaner production techniques and exploring renewable energy alternatives. As environmental awareness increases, the demand for big data solutions in the oil and gas industry is expected to rise, further propelling market expansion.

From a regional perspective, North America currently holds a significant share of the market, primarily due to the early adoption of advanced technologies and the presence of leading oil and gas companies. The United States, in particular, is a frontrunner in implementing big data solutions across its extensive oil exploration and production activities. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest growth rate owing to rapid industrialization, increasing energy demand, and substantial investments in digital technologies within countries such as China and India. Europe and Latin America are also witnessing steady growth as companies in these regions increasingly recognize the benefits of big data analytics in driving efficiency and sustainability in their operations.

Component Analysis

In the big data in oil and gas market, the component segment is categorized into software, hardware, and services. Each component plays a pivotal role in facilitating the comprehensive adoption and integration of big data solutions across the oil and gas value chain. The software segment is anticipated to account for the largest market share, driven by the need for advanced analytics tools and platforms that enable complex data processing and visualization. Software solutions such as data management systems, predictive analytics platforms, and visualization tools are crucial for extracting actionable insights from vast datasets, thereby enhancing decision-making processes and operational efficiency.

The hardware segment encompasses the physical infrastructure required to support big data analytics, includ

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 Western Oregon-Washington Province (004). 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.

There are 487 onshore oil and gas fields in California encompassing 3,392 square miles of aggregated area. The California State Water Resources Control Board (State Water Board) initiated a Regional Monitoring Program (RMP) in July 2015, intended to determine where and to what degree groundwater quality may be at potential risk to contamination related to oil and gas development activities including well stimulation, well integrity issues, produced water ponds, and underground injection. The first step in monitoring groundwater in and near oil and gas fields is to prioritize the 487 fields using consistent statewide analysis of available data that indicate potential risk of groundwater to oil and gas development. There were limited existing data on potential groundwater risk factors available for oil and gas fields across the state. During 2014-2016, the U.S. Geological Survey (USGS) extracted and compiled data from various sources, including the California Division of Oil, Gas, and Geothermal Resources (DOGGR) and the California Department of Water Resources (DWR). During 2014-2016, the depth to top of perforated intervals and depth to base of freshwater for oil and gas production wells in California were extracted from well records maintained by the DOGGR. Well records including geophysical logs, well history, well completion reports, and correspondences were viewed on DOGGR's Well Finder website at https://maps.conservation.ca.gov/doggr/wellfinder/. This digital dataset contains 3,505 records for production wells, of which 2,964 wells have a recorded depth to top of perforated intervals and 1,494 wells have a recorded depth to base of freshwater. Wells were attributed with American Petroleum Institute (API) numbers, oil and gas field, and well location, well status and type, and nearest oil and gas field for wells that plotted outside field boundaries using the DOGGR All Wells geospatial data included in this data release. Wells were attributed with land surface elevations using the California National Elevation Dataset. Due to limited time and resources to analyze well records for the most recent well configuration, wells spatially distributed throughout the state and accounting for about 2 percent of the more than 185,000 production wells (new, active, idle, or plugged well status) were attributed with depth data.

Graph and download economic data for All Employees, Oil and Gas Extraction (CES1021100001) from Jan 1972 to Jun 2025 about extraction, logging, oil, mining, gas, establishment survey, employment, and USA.

The Oil and Gas Infrastructure Mapping (OGIM) database is a global, spatially explicit, and granular dataset of oil and gas infrastructure. It is developed by Environmental Defense Fund (EDF) (www.edf.org) and MethaneSAT, LLC (www.methanesat.org), a wholly owned subsidiary of EDF. The OGIM database helps fill a crucial geospatial data need, by supporting the quantification and source characterization of oil and gas methane emissions. The database is developed via acquisition, analysis, curation, integration, and quality-assurance (performed at EDF) of publicly available geospatial data sources. These oil and gas facility datasets are reported by governments, industry, academics, and other non-government entities.

OGIM is a collection of data tables within a GeoPackage. Each data table within the GeoPackage includes locations and facility attributes of oil and gas infrastructure types that are important sources of methane emissions, including: oil and gas production wells, offshore production platforms, natural gas compressor stations, oil and natural gas processing facilities, liquefied natural gas facilities, crude oil refineries, and pipelines. OGIM v2.7 includes approximately 6.7 million features, including 4.5 million point locations of oil and gas wells and over 1.2 million kilometers of oil and gas pipelines.

Please see the PDF document in the “Files” section of this page for more information about this version, including attribute column definitions, key changes since the previous version, and more. Full details on database development and related analytics can be found in the following Earth System Science Data (ESSD) journal paper. Please cite this paper when using any version of the database:

Omara, M., Gautam, R., O'Brien, M., Himmelberger, A., Franco, A., Meisenhelder, K., Hauser, G., Lyon, D., Chulakadabba, A., Miller, C., Franklin, J., Wofsy, S., and Hamburg, S.: Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-15-3761-2023, 2023.

Important note: While the results section of this manuscript is specific to v1 of the OGIM, the methods described therein are the same methods used to develop and update v2.7. Additionally, while we describe our data sources in detail in the manuscript above, and include maps of all acquired datasets, this open-access version of the OGIM database does not include the locations of about 300 natural gas compressor stations in Russia. Future updates may include these locations when appropriate permissions to make them publicly accessible are obtained.

OGIM v2.7 is based on public-domain datasets reported in February 2025 or prior. Each record in OGIM indicates a date (SRC_DATE) when the original source of the record was published or last updated. Some records may contain out-of-date information, for example, if a facility’s status has changed since we last visited a data source. We anticipate updating the OGIM database on a regular cadence and are continually including new public domain datasets as they become available.

Point of Contact at Environmental Defense Fund and MethaneSAT, LLC: Madeleine O’Brien (maobrien@methanesat.org) and Mark Omara (momara@edf.org).

A cells polygon feature class was created by the U. S. Geological Survey (USGS) to illustrate the degree of exploration, type of production, and distribution of production in the State of Illinois. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. Data were retrieved from the Illinois State Geological Survey (ISGS) oil and gas wells database. Cells were developed as a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. The data are current as of 2006.

The United States Documented Unplugged Orphaned Oil and Gas Well (DOW) dataset contains 117,672 wells in 27 states. The definition of an orphaned oil or gas well varies across data sources; the dataset includes oil or gas wells where the state indicates that the well is an unplugged orphan, or the following criteria are met: 1) no production for an average of 12 months (6 to 24 months depending on the state), 2) the well is unplugged, 3) there is no responsible party to manage the well for future re-use or for plugging and abandonment, and 4) the location of the well is documented. The dataset includes location coordinates, American Petroleum Institute (API) number, or other identification number, well type, well status, and additional information for each unplugged orphaned well. All data were collected by direct requests to the respective state agency overseeing oil and gas wells or data downloads from their online databases. Location format conversion was performed on wells without coordinate locations using tools provided by the Bureau of Land Management and some state agencies. No other data manipulations were performed to the source data aside from reformatting or the addition of explanatory notes.

A cells polygon feature class was created by the U. S. Geological Survey (USGS) to illustrate the degree of exploration, type of production, and distribution of production in the State of Indiana. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. Cells were developed as a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. The data are current as of 2006.

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.

This dataset contains Saudi Arabia Oil Database for 2002-2021. Data from Joint Organisations Data Initiative. Follow datasource.kapsarc.org for timely data to advance energy economics research.

The data business in the oil & gas market is anticipated to expand its roots at a steady CAGR of 16.7% during the forecast period. The market is likely to hold a revenue of US$ 36.43 billion in 2023 while it is anticipated to cross a value of US$ 171.18 billion by 2033.

Attributes	Details
Data Business in Oil & Gas Market CAGR (2023 to 2033)	16.7%
Data Business in Oil & Gas Market Size (2023)	US$ 36.43 billion
Data Business in Oil & Gas Market Size (2033)	US$ 171.18 billion

Country-wise Insights

Countries	Revenue Share % (2023)
The United States	19.7%
Germany	6.3%
Japan	4.3%
Australia	3.3%

Countries	CAGR % (2023 to 2033)
China	18.6%
India	18.3%
The United Kingdom	16.4%

Category-Wise Landscape

Category	By Component Type
Leading Segment	Data Management
Market Share (2022)	45.4%

Category	By oil companies Type
Leading Segment	Independent Oil Companies
Market Share (2022)	40.3%

Data and insights on the Oil & Gas sector in India - production, consumption, imports, reserves, pricing, and comparison with global peers.

This project represents the data used in “Influences of potential oil and gas development and future climate on sage-grouse declines and redistribution.” The data sets describe greater sage-grouse (Centrocercus urophasianus) population change, summarized in different boundaries within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Population changes were based on different scenarios of oil and gas development intensities, projected climate models, and initial sage-grouse population estimates. Description of data sets pertaining to this project: Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with and without effects of climate change. 1. Greater sage-grouse population change (percent change) over 50-years in a high oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 2. Greater sage-grouse population change (percent change) in a low oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 3. Greater sage-grouse population change (percent change) over 50-years in a low oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 4. Greater sage-grouse population change (percent change) in a moderate oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 5. Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with no effects of climate change (2006-2062) The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

No. of Worker: Petroleum & Natural Gas Mining: Indonesian data was reported at 21,402.000 Person in 2017. This records an increase from the previous number of 20,752.000 Person for 2015. No. of Worker: Petroleum & Natural Gas Mining: Indonesian data is updated yearly, averaging 21,385.000 Person from Dec 2007 (Median) to 2017, with 10 observations. The data reached an all-time high of 25,946.000 Person in 2008 and a record low of 20,468.000 Person in 2012. No. of Worker: Petroleum & Natural Gas Mining: Indonesian data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Energy Sector – Table ID.RBA005: Energy Statistics: Worker: Oil & Gas.