Graph and download economic data for Producer Price Index by Commodity: Mining Services: Drilling Oil and Gas Wells Services (WPU60110301) from Jul 1991 to Jun 2025 about wells, drilling, oil, mining, gas, services, commodities, PPI, inflation, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Industry: Drilling Oil and Gas Wells: Primary Services (PCU213111213111P) from Dec 1985 to May 2025 about wells, drilling, primary, oil, gas, services, PPI, industry, inflation, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Industry: Support Activities for Oil and Gas Operations: Primary Services (PCU213112213112P) from Dec 1985 to Jun 2025 about support activities, operating, primary, oil, gas, services, PPI, industry, inflation, price index, indexes, price, and USA.

United States New York Stock Exchange: Index: S&P Oil & Gas Equipment & Services Select Industry Index data was reported at 596.890 NA in Apr 2025. This records a decrease from the previous number of 742.680 NA for Mar 2025. United States New York Stock Exchange: Index: S&P Oil & Gas Equipment & Services Select Industry Index data is updated monthly, averaging 985.680 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 4,989.000 NA in Jun 2014 and a record low of 233.940 NA in Mar 2020. United States New York Stock Exchange: Index: S&P Oil & Gas Equipment & Services Select Industry Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: S&P: Monthly.

United States PPI: Misc Pdts: Mining Services: Oil & Gas Field Services nec data was reported at 199.300 Dec1985=100 in Jun 2018. This records a decrease from the previous number of 200.200 Dec1985=100 for May 2018. United States PPI: Misc Pdts: Mining Services: Oil & Gas Field Services nec data is updated monthly, averaging 137.100 Dec1985=100 from Dec 1985 (Median) to Jun 2018, with 391 observations. The data reached an all-time high of 203.700 Dec1985=100 in Oct 2013 and a record low of 90.400 Dec1985=100 in Feb 1987. United States PPI: Misc Pdts: Mining Services: Oil & Gas Field Services nec data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I017: Producer Price Index: By Commodities.

United States - Producer Price Index by Commodity: Inputs to Industries: Net Inputs to Mining and Oil and Gas Field Machinery Manufacturing Industry, Transportation and Warehousing Services was 122.06600 Index April 2014=100 in May of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Inputs to Industries: Net Inputs to Mining and Oil and Gas Field Machinery Manufacturing Industry, Transportation and Warehousing Services reached a record high of 135.02200 in June of 2022 and a record low of 93.60000 in September of 2016. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Inputs to Industries: Net Inputs to Mining and Oil and Gas Field Machinery Manufacturing Industry, Transportation and Warehousing Services - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for Producer Price Index by Commodity: Inputs to Industries: Net Inputs to Mining and Oil and Gas Field Machinery Manufacturing Industry, Services Less Trade, Transportation, and Warehousing (WPUIP33313023) from Apr 2014 to Jun 2025 about warehousing, machinery, oil, mining, gas, trade, transportation, services, commodities, manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

PPI: Svcs: OH: PS: Oilfield & Well Drilling Eqpt Rental & Leasing data was reported at 99.554 Dec2003=100 in Mar 2025. This stayed constant from the previous number of 99.554 Dec2003=100 for Feb 2025. PPI: Svcs: OH: PS: Oilfield & Well Drilling Eqpt Rental & Leasing data is updated monthly, averaging 106.800 Dec2003=100 from Dec 2003 (Median) to Mar 2025, with 256 observations. The data reached an all-time high of 143.300 Dec2003=100 in Dec 2014 and a record low of 93.600 Dec2003=100 in Oct 2020. PPI: Svcs: OH: PS: Oilfield & Well Drilling Eqpt Rental & Leasing data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I104: Producer Price Index: by Industry: Services: Real Estate and Rental Leasing.

Dow Jones U.S. Select Oil Equipment & Services index is expected to experience a moderate increase due to rising demand for oil and gas services as global economies recover from the pandemic and energy consumption increases. However, uncertainty in the geopolitical landscape, supply chain disruptions, and macroeconomic factors could pose risks to this prediction.

The Dow Jones U.S. Select Oil Equipment & Services index forecasts a positive trend. However, there are risks associated with this prediction, including a potential downturn in the oil and gas industry, supply chain disruptions, and geopolitical uncertainties.

United States - Private fixed investment in equipment and software: Nonresidential: Other equipment - furniture and fixtures, agricultural, construction, mining and oilfield, service industry machinery, electrical equipment n.e.c. (chain-type price index) was 129.85500 Index 2009=100 in January of 2024, according to the United States Federal Reserve. Historically, United States - Private fixed investment in equipment and software: Nonresidential: Other equipment - furniture and fixtures, agricultural, construction, mining and oilfield, service industry machinery, electrical equipment n.e.c. (chain-type price index) reached a record high of 129.85500 in January of 2024 and a record low of 8.98400 in January of 1946. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Private fixed investment in equipment and software: Nonresidential: Other equipment - furniture and fixtures, agricultural, construction, mining and oilfield, service industry machinery, electrical equipment n.e.c. (chain-type price index) - last updated from the United States Federal Reserve on June of 2025.

Kazakhstan Real GDP: Volume Index: GVA: Oil & Gas Sector: PS: Crude Oil & Natural Gas Extraction, Services data was reported at 109.800 Prev Year=100 in 2017. This records an increase from the previous number of 97.000 Prev Year=100 for 2016. Kazakhstan Real GDP: Volume Index: GVA: Oil & Gas Sector: PS: Crude Oil & Natural Gas Extraction, Services data is updated yearly, averaging 100.700 Prev Year=100 from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 109.800 Prev Year=100 in 2017 and a record low of 97.000 Prev Year=100 in 2016. Kazakhstan Real GDP: Volume Index: GVA: Oil & Gas Sector: PS: Crude Oil & Natural Gas Extraction, Services data remains active status in CEIC and is reported by The Agency of Statistics of the Republic of Kazakhstan. The data is categorized under Global Database’s Kazakhstan – Table KZ.A014: Real GDP: Volume Index: Oil and Gas Sector.

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 Oil Equipment & Services Index Drilling for Growth?

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

United States - Producer Price Index by Commodity: Mining Services: Drilling Oil and Gas Wells Services was 328.15100 Index Dec 1985=100 in November of 2021, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Mining Services: Drilling Oil and Gas Wells Services reached a record high of 470.80000 in February of 2014 and a record low of 87.80000 in July of 1992. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Mining Services: Drilling Oil and Gas Wells Services - last updated from the United States Federal Reserve on June of 2025.

United States - Real private fixed investment in equipment and software: Nonresidential: Other equipment - furniture and fixtures, agricultural, construction, mining and oilfield, service industry machinery, electrical equipment n.e.c. (chain-type quantity index) was 104.76900 Index 2009=100 in January of 2023, according to the United States Federal Reserve. Historically, United States - Real private fixed investment in equipment and software: Nonresidential: Other equipment - furniture and fixtures, agricultural, construction, mining and oilfield, service industry machinery, electrical equipment n.e.c. (chain-type quantity index) reached a record high of 114.54500 in January of 2014 and a record low of 12.07300 in January of 1946. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Real private fixed investment in equipment and software: Nonresidential: Other equipment - furniture and fixtures, agricultural, construction, mining and oilfield, service industry machinery, electrical equipment n.e.c. (chain-type quantity index) - last updated from the United States Federal Reserve on July 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.

Oil Services Sector Outlook: Moderate Growth Predicted for U.S. Select Oil Equipment & Services Index.

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

United States PPI: Svcs: TI: Crude Oil Pipeline Transportation: Dec 2003=100 data was reported at 312.767 Dec2003=100 in Apr 2025. This stayed constant from the previous number of 312.767 Dec2003=100 for Mar 2025. United States PPI: Svcs: TI: Crude Oil Pipeline Transportation: Dec 2003=100 data is updated monthly, averaging 226.500 Dec2003=100 from Dec 2003 (Median) to Apr 2025, with 257 observations. The data reached an all-time high of 313.200 Dec2003=100 in Dec 2024 and a record low of 100.000 Dec2003=100 in Dec 2003. United States PPI: Svcs: TI: Crude Oil Pipeline Transportation: Dec 2003=100 data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I: Producer Price Index: by Industry: Services: Transportation & Warehousing.

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.

Oil Equipment & Services Sector Poised for Moderate Growth, Analyst Forecasts for Dow Jones U.S. Select Oil Equipment & Services index.

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.

Oil Equipment & Services Index Poised for Steady Growth Amidst Global Demand

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 dataset is a compilation of available oil and gas pipeline data and is maintained by BSEE. Pipelines are used to transport and monitor oil and/or gas from wells within the outer continental shelf (OCS) to resource collection locations. Currently, pipelines managed by BSEE are found in Gulf of Mexico and southern California waters.

© MarineCadastre.gov This layer is a component of BOEMRE Layers.

This Map Service contains many of the primary data types created by both the Bureau of Ocean Energy Management (BOEM) and the Bureau of Safety and Environmental Enforcement (BSEE) within the Department of Interior (DOI) for the purpose of managing offshore federal real estate leases for oil, gas, minerals, renewable energy, sand and gravel. These data layers are being made available as REST mapping services for the purpose of web viewing and map overlay viewing in GIS systems. Due to re-projection issues which occur when converting multiple UTM zone data to a single national or regional projected space, and line type changes that occur when converting from UTM to geographic projections, these data layers should not be used for official or legal purposes. Only the original data found within BOEM/BSEE’s official internal database, federal register notices or official paper or pdf map products may be considered as the official information or mapping products used by BOEM or BSEE. A variety of data layers are represented within this REST service are described further below. These and other cadastre information the BOEM and BSEE produces are generated in accordance with 30 Code of Federal Regulations (CFR) 256.8 to support Federal land ownership and mineral resource management.

For more information – Contact: Branch Chief, Mapping and Boundary Branch, BOEM, 381 Elden Street, Herndon, VA 20170. Telephone (703) 787-1312; Email: mapping.boundary.branch@boem.gov

The REST services for National Level Data can be found here: http://gis.boemre.gov/arcgis/rest/services/BOEM_BSEE/MMC_Layers/MapServer

REST services for regional level data can be found by clicking on the region of interest from the following URL: http://gis.boemre.gov/arcgis/rest/services/BOEM_BSEE

Individual Regional Data or in depth metadata for download can be obtained in ESRI Shape file format by clicking on the region of interest from the following URL: http://www.boem.gov/Oil-and-Gas-Energy-Program/Mapping-and-Data/Index.aspx

Currently the following layers are available from this REST location:

OCS Drilling Platforms -Locations of structures at and beneath the water surface used for the purpose of exploration and resource extraction. Only platforms in federal Outer Continental Shelf (OCS) waters are included. A database of platforms and rigs is maintained by BSEE.

OCS Oil and Natural Gas Wells -Existing wells drilled for exploration or extraction of oil and/or gas products. Additional information includes the lease number, well name, spud date, the well class, surface area/block number, and statistics on well status summary. Only wells found in federal Outer Continental Shelf (OCS) waters are included. Wells information is updated daily. Additional files are available on well completions and well tests. A database of wells is maintained by BSEE.

OCS Oil & Gas Pipelines -This dataset is a compilation of available oil and gas pipeline data and is maintained by BSEE. Pipelines are used to transport and monitor oil and/or gas from wells within the outer continental shelf (OCS) to resource collection locations. Currently, pipelines managed by BSEE are found in Gulf of Mexico and southern California waters.

Unofficial State Lateral Boundaries - The approximate location of the boundary between two states seaward of the coastline and terminating at the Submerged Lands Act Boundary. Because most State boundary locations have not been officially described beyond the coast, are disputed between states or in some cases the coastal land boundary description is not available, these lines serve as an approximation that was used to determine a starting point for creation of BOEM’s OCS Administrative Boundaries. GIS files are not available for this layer due to its unofficial status.

BOEM OCS Administrative Boundaries - Outer Continental Shelf (OCS) Administrative Boundaries Extending from the Submerged Lands Act Boundary seaward to the Limit of the United States OCS (The U.S. 200 nautical mile Limit, or other marine boundary)For additional details please see the January 3, 2006 Federal Register Notice.

BOEM Limit of OCSLA ‘8(g)’ zone - The Outer Continental Shelf Lands Act '8(g) Zone' lies between the Submerged Lands Act (SLA) boundary line and a line projected 3 nautical miles seaward of the SLA boundary line. Within this zone, oil and gas revenues are shared with the coastal state(s). The official version of the ‘8(g)’ Boundaries can only be found on the BOEM Official Protraction Diagrams (OPDs) or Supplemental Official Protraction described below.

Submerged Lands Act Boundary - The SLA boundary defines the seaward limit of a state's submerged lands and the landward boundary of federally managed OCS lands. The official version of the SLA Boundaries can only be found on the BOEM Official Protraction Diagrams (OPDs) or Supplemental Official Protraction Diagrams described below.

Atlantic Wildlife Survey Tracklines(2005-2012) - These data depict tracklines of wildlife surveys conducted in the Mid-Atlantic region since 2005. The tracklines are comprised of aerial and shipboard surveys. These data are intended to be used as a working compendium to inform the diverse number of groups that conduct surveys in the Mid-Atlantic region.The tracklines as depicted in this dataset have been derived from source tracklines and transects. The tracklines have been simplified (modified from their original form) due to the large size of the Mid-Atlantic region and the limited ability to map all areas simultaneously.The tracklines are to be used as a general reference and should not be considered definitive or authoritative. This data can be downloaded from http://www.boem.gov/uploadedFiles/BOEM/Renewable_Energy_Program/Mapping_and_Data/ATL_WILDLIFE_SURVEYS.zip

BOEM OCS Protraction Diagrams & Leasing Maps - This data set contains a national scale spatial footprint of the outer boundaries of the Bureau of Ocean Energy Management’s (BOEM’s) Official Protraction Diagrams (OPDs) and Leasing Maps (LMs). It is updated as needed. OPDs and LMs are mapping products produced and used by the BOEM to delimit areas available for potential offshore mineral leases, determine the State/Federal offshore boundaries, and determine the limits of revenue sharing and other boundaries to be considered for leasing offshore waters. This dataset shows only the outline of the maps that are available from BOEM.Only the most recently published paper or pdf versions of the OPDs or LMs should be used for official or legal purposes. The pdf maps can be found by going to the following link and selecting the appropriate region of interest. http://www.boem.gov/Oil-and-Gas-Energy-Program/Mapping-and-Data/Index.aspx Both OPDs and LMs are further subdivided into individual Outer Continental Shelf(OCS) blocks which are available as a separate layer. Some OCS blocks that also contain other boundary information are known as Supplemental Official Block Diagrams (SOBDs.) Further information on the historic development of OPD's can be found in OCS Report MMS 99-0006: Boundary Development on the Outer Continental Shelf: http://www.boemre.gov/itd/pubs/1999/99-0006.PDF Also see the metadata for each of the individual GIS data layers available for download. The Official Protraction Diagrams (OPDs) and Supplemental Official Block Diagrams (SOBDs), serve as the legal definition for BOEM offshore boundary coordinates and area descriptions.

BOEM OCS Lease Blocks - Outer Continental Shelf (OCS) lease blocks serve as the legal definition for BOEM offshore boundary coordinates used to define small geographic areas within an Official Protraction Diagram (OPD) for leasing and administrative purposes. OCS blocks relate back to individual Official Protraction Diagrams and are not uniquely numbered. Only the most recently published paper or pdf