scholarly journals Experimental Modelling of Black Carbon Emissions from Gas Flares in the Oil and Gas Sector

2020 ◽  
Author(s):  
Parvin Mehr
Keyword(s):  
Black Carbon ◽  
Carbon Emissions ◽  
Oil And Gas ◽  
Experimental Modelling ◽  
Gas Flares ◽  
Oil And Gas Sector

Experimental Modelling of Black Carbon Emissions from Gas Flares in the Oil and Gas Sector

2020 ◽  
Author(s):  
Parvin Mehr ◽  
Bradley M. Conrad ◽  
Matthew R. Johnson
Keyword(s):  
Black Carbon ◽  
Carbon Emissions ◽  
Oil And Gas ◽  
Diffusion Flames ◽  
Software Tool ◽  
Monte Carlo Analysis ◽  
Significant Advance ◽  
Accurate Estimation ◽  
Experimental Modelling ◽  
Gas Flares

<p>Flares in the upstream oil and gas (UOG) industry are an important and poorly quantified source of black carbon (BC) emissions and may be a dominant source of black carbon deposition in sensitive Arctic regions (Stohl et al. 2013).  Accurate estimation of flare BC emissions to support informed policy decisions, accurate climate modeling, and new international reporting regulations under the Gothenburg protocol is a critical challenge.  To date few studies have focussed on the primarily buoyancy-dominated turbulent non-premixed flames typical of upstream oil and gas flares, such that existing emission factor models are highly uncertain (see (McEwen and Johnson 2012)).  Although recent progress has been made in measuring black carbon from flares in the field (e.g. (Conrad and Johnson 2017; Johnson et al. 2013), data have also shown that emissions of individual flares may vary by more than 4 orders of magnitude. </p><p>The objective of the current study is to develop a robust data-backed model to predict black carbon emissions from flares considering variations in flare gas composition, flow rates, and stack diameters.  Laboratory measurements of black carbon (soot) for a range of turbulent non-premixed jet diffusion flames of up to 3 m in length were performed at the Carleton University Flare Facility in Ottawa, Canada.  Two hundred and thirty cases spanning five flare stack diameters (25.4 to 76.2 mm), exit velocities from 0.16 to 15.15 m/s, and a broad range of industrially-relevant multicomponent (C1-C7 hydrocarbons, CO<sub>2</sub>, N<sub>2</sub>) flare gas compositions were studied.  Emissions were captured in a large (~3.1 m diameter) sampling hood and forwarded to gas- and particulate phase analyzers. </p><p>Black carbon concentrations were measured via a Sunset Labs thermal-optical instrument using the OCECgo software tool (Conrad and Johnson 2019) to quantify uncertainties via Monte Carlo analysis.  BC yields were subsequently calculated using a mass-balance methodology (Corbin and Johnson 2014).  Variability in BC yield was well-predicted by an empirical model incorporating both the aerodynamic and chemistry effects.  For this range of conditions, it was observed that primary independent variables (such as exit velocity and higher heating value) act as reasonable surrogates for sooting propensity.  Further experiments are underway to test the proposed model over a broader range of conditions.  However, results to date represent a significant advance in our ability to predict black carbon emissions from flares.</p>

Improving Offshore Platform Production With Artificial Intelligence

2021 ◽  
Author(s):  
Philippe Herve
Keyword(s):  
Artificial Intelligence ◽  
Carbon Emissions ◽  
Oil And Gas ◽  
New Technologies ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Carbon Neutrality ◽  
Oil And Gas Companies ◽  
Oil And Gas Sector

Abstract The oil and gas sector is facing a changing market with new pressures to which it must learn to adapt. One of the biggest changes in expectations is the increased focus being placed on carbon emissions. Many consumers, investors, and lawmakers see reforms to the oil and gas industry as one of the most important avenues toward reducing carbon emissions and curbing climate change, and accordingly, a large number of companies have already made ambitious pledges towards carbon neutrality. New technologies may offer the best avenue for oil and gas companies to reduce their carbon emissions and meet those neutrality goals. Digital technologies—and in particular, artificial intelligence—can aid in decarbonization even with relatively small investments, primarily by enabling large increases in efficiency and reducing unscheduled downtime and the need for flaring. This paper discusses how artificial intelligence-powered predictive maintenance can be applied to reduce carbon emissions, and a case study illustrating a real-world deployment of this technology.

scholarly journals Gas flaring activity and black carbon emissions in 2017 derived from Sentinel-3A SLSTR

2019 ◽  
Author(s):  
Alexandre Caseiro ◽  
Berit Gehrke ◽  
Gernot Rücker ◽  
David Leimbach ◽  
Johannes W. Kaiser
Keyword(s):  
Black Carbon ◽  
Carbon Emissions ◽  
Land Surface ◽  
Hot Spot ◽  
Data Availability ◽  
Atmospheric Pollutants ◽  
Atmospheric Composition ◽  
Data Set ◽  
Gas Flaring ◽  
Gas Flares

Abstract. Gas flares are a regionally and globally significant source of atmospheric pollutants. They can be detected by satellite remote sensing. We calculate the global flared gas volume and black carbon emissions in 2017 by (1) applying a previously developed hot spot detection and characterisation algorithm to all observations of the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on-board the Copernicus satellite Sentinel-3A in 2017 and (2) applying newly developed filters for identifying gas flares and corrections for calculating flared gas volumes (Billion Cubic Meters, BCM) and black carbon emission estimates. The filter to discriminate gas flares from other hot spots combines the unique flaring characteristics in terms of persistence and temperature. The comparison of our results with those of the Visible Infrared Imaging Radiometer Suite (VIIRS) nightfire data set indicates a good fit between the two methods. The calculation of black carbon emissions using our gas flaring data set and published emission factors show good agreement with recently published black carbon inventories. The data presented here can therefore be used e.g. in atmospheric dispersion simulations. The advantage of using our algorithm with Sentinel-3A data lies in the previously demonstrated ability to detect and quantify small flares and the foreseen long term data availability from the Copernicus program. Our data (GFlaringS3, flaring activity and the related black carbon emissions) are available on the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) web site (https://eccad3.sedoo.fr/#GFlaringS3, DOI https://doi.org/10.25326/19 (Caseiro and Kaiser, 2019)) for use in, e.g., atmospheric composition modelling studies.

scholarly journals Gas flaring activity and black carbon emissions in 2017 derived from the Sentinel-3A Sea and Land Surface Temperature Radiometer

2020 ◽  
Vol 12 (3) ◽  
pp. 2137-2155
Author(s):  
Alexandre Caseiro ◽  
Berit Gehrke ◽  
Gernot Rücker ◽  
David Leimbach ◽  
Johannes W. Kaiser
Keyword(s):  
Confidence Interval ◽  
Surface Temperature ◽  
Black Carbon ◽  
Land Surface Temperature ◽  
Carbon Emissions ◽  
Land Surface ◽  
Hot Spot ◽  
Data Set ◽  
Gas Flares ◽  
Gas Volume

Abstract. Gas flares are a regionally and globally significant source of atmospheric pollutants. They can be detected by satellite remote sensing. We calculate the global flared gas volume and black carbon emissions in 2017 by applying (1) a previously developed hot spot detection and characterisation algorithm to all observations of the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board the Copernicus satellite Sentinel-3A and (2) newly developed filters for identifying gas flares and corrections for calculating both flared gas volumes (billion cubic metres, BCM) and black carbon (BC) emissions (g). The filter to discriminate gas flares from other hot spots uses the observed hot spot characteristics in terms of temperature and persistence. A regression function is used to correct for the variability of detection opportunities. A total of 6232 flaring sites are identified worldwide. The best estimates of the annual flared gas volume and the BC emissions are 129 BCM with a confidence interval of [35, 419 BCM] and 73 Gg with a confidence interval of [20, 239 Gg], respectively. Comparison of our activity (i.e. BCM) results with those of the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire data set and SWIR-based calculations show general agreement but distinct differences in several details. The calculation of black carbon emissions using our gas flaring data set with a newly developed dynamic assignment of emission factors lie in the range of recently published black carbon inventories, albeit towards the lower end. The data presented here can therefore be used e.g. in atmospheric dispersion simulations. The advantage of using our algorithm with Sentinel-3 data lies in the previously demonstrated ability to detect and quantify small flares, the long-term data availability from the Copernicus programme, and the increased detection opportunity of global gas flare monitoring when used in conjunction with the VIIRS instruments. The flaring activity and related black carbon emissions are available as “GFlaringS3” on the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) website (https://doi.org/10.25326/19, Caseiro and Kaiser, 2019).

Carbon dioxide, methane and black carbon emissions from upstream oil and gas flaring in the United States

2016 ◽  
Vol 13 ◽  
pp. 119-123 ◽  
Author(s):  
David T Allen ◽  
Denzil Smith ◽  
Vincent M Torres ◽  
Felipe Cardoso Saldaña
Keyword(s):  
United States ◽  
Carbon Dioxide ◽  
Black Carbon ◽  
Carbon Emissions ◽  
Oil And Gas ◽  
The United States ◽  
Gas Flaring

Black Carbon Emissions from the Bakken Oil and Gas Development Region

2015 ◽  
Vol 2 (10) ◽  
pp. 281-285 ◽  
Author(s):  
Joshua P. Schwarz ◽  
John S. Holloway ◽  
Joseph M. Katich ◽  
Stuart McKeen ◽  
Eric A. Kort ◽  
...  
Keyword(s):  
Black Carbon ◽  
Carbon Emissions ◽  
Oil And Gas ◽  
Oil And Gas Development ◽  
Development Region

Ways to improve tax administration of major taxpayers of oil and gas sector

2020 ◽  
Vol 23 (9) ◽  
pp. 1064-1076
Author(s):  
O.V. Ovchar
Keyword(s):  
System Analysis ◽  
Oil And Gas ◽  
Import Substitution ◽  
State Regulation ◽  
Tax Administration ◽  
Problems And Solutions ◽  
Original Interpretation ◽  
Instrumental Methods ◽  
Oil And Gas Sector ◽  
Tax Regulation

Subject. Under rapid changes in the external economic environment, new forms and methods of State regulation of oil and gas industries, especially, improving the taxation and tax regulation instruments become relevant. Objectives. The study aims to provide an original interpretation of methods of improving the tax administration of major taxpayers in the oil and gas sector applied at the present stage. Methods. I employ normative and holistic approaches to examine taxation efficiency in the oil and gas sector, general scientific and special methods of scientific cognition, i.e. retrospective, system analysis, observation, classification, instrumental methods of grouping, sampling, comparison and synthesis, as well as evolutionary and dynamic analysis. Results. I consider basic problems and solutions in the sphere of tax administration of major taxpayers of Russian oil and gas industries. The paper offers a package of measures and recommendations aimed at improving the efficiency of tax regulation, underpins the applied approach to tax administration of organizations operating in the oil and gas sector. Conclusions and Relevance. Our country needs a comprehensive program for tax administration of the entire technological cycle: from upstream operations to full-scale import substitution of consumer goods.

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