Methane Emission Estimation of Oil and Gas Sector: A Review of Measurement Technologies, Data Analysis Methods and Uncertainty Estimation

The emission estimation of the oil and gas sector, which involves field test measurements, data analysis, and uncertainty estimation, precedes effective emission mitigation actions. A systematic comparison and summary of these technologies and methods are necessary to instruct the technology selection and for uncertainty improvement, which is not found in existing literature. In this paper, we present a review of existing measuring technologies, matching data analysis methods, and newly developed probabilistic tools for uncertainty estimation and try to depict the process for emission estimation. Through a review, we find that objectives have a determinative effect on the selection of measurement technologies, matching data analysis methods, and uncertainty estimation methods. And from a systematic perspective, optical instruments may have greatly improved measurement accuracy and range, yet data analysis methods might be the main contributor of estimation uncertainty. We suggest that future studies on oil and gas methane emissions should focus on the analysis methods to narrow the uncertainty bond, and more research on uncertainty generation might also be required.

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Data systems and data analysis methods to support on-board in-flightoperations

10.2514/6.1967-911 ◽

1967 ◽

Author(s):

D. PRONGAY

Keyword(s):

Data Analysis ◽

Data Systems ◽

Analysis Methods ◽

Data Analysis Methods

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Advanced Data Analysis Methods for Analyte Recognition from Optical Sensor Arrays

10.21236/ada388633 ◽

2001 ◽

Author(s):

Carey E. Priebe

Keyword(s):

Data Analysis ◽

Optical Sensor ◽

Sensor Arrays ◽

Analysis Methods ◽

Data Analysis Methods

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Investigation of metered data analysis methods for commercial and related buildings

10.2172/6261458 ◽

1989 ◽

Cited By ~ 2

Author(s):

J M MacDonald ◽

D M Wasserman

Keyword(s):

Data Analysis ◽

Analysis Methods ◽

Data Analysis Methods

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An evaluation of data analysis methods for optical intrinsic signal imaging

10.1117/12.546565 ◽

2003 ◽

Cited By ~ 3

Author(s):

Pengcheng Li ◽

Weihua Luo ◽

Qingming Luo ◽

Shangbin Cheng

Keyword(s):

Data Analysis ◽

Intrinsic Signal ◽

Optical Intrinsic Signal ◽

Analysis Methods ◽

Evaluation Of Data ◽

Data Analysis Methods

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Objective assessment of SERS thin films: comparison of silver on copper via galvanic displacement with commercially available fabricated substrates

Analytical Methods ◽

10.1039/c7ay01584d ◽

2017 ◽

Vol 9 (33) ◽

pp. 4783-4789 ◽

Cited By ~ 8

Author(s):

Samuel Mabbott ◽

Yun Xu ◽

Royston Goodacre

Keyword(s):

Thin Films ◽

Data Analysis ◽

Objective Assessment ◽

Galvanic Displacement ◽

Sers Signal ◽

Analysis Methods ◽

Data Analysis Methods

Reproducibility of SERS signal acquired from thin films developed in-house and commercially has been assessed using seven data analysis methods.

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Omaha System Public Health Data: Analysis Methods

Nursing Outlook ◽

10.1016/j.outlook.2010.02.125 ◽

2010 ◽

Vol 58 (2) ◽

pp. e22-e23

Author(s):

Karen A. Monsen ◽

Karen S. Martin ◽

Bonnie L Westra

Keyword(s):

Public Health ◽

Data Analysis ◽

Health Data ◽

Public Health Data ◽

Analysis Methods ◽

Omaha System ◽

Data Analysis Methods

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Peak detection in sediment - charcoal records: impacts of alternative data analysis methods on fire-history interpretations

International Journal of Wildland Fire ◽

10.1071/wf09134 ◽

2010 ◽

Vol 19 (8) ◽

pp. 996 ◽

Cited By ~ 182

Author(s):

Philip E. Higuera ◽

Daniel G. Gavin ◽

Patrick J. Bartlein ◽

Douglas J. Hallett

Keyword(s):

Data Analysis ◽

Fire History ◽

Threshold Value ◽

Decomposition Methods ◽

Peak Detection ◽

Detection Methods ◽

Statistical Framework ◽

Analysis Methods ◽

Decomposition Models ◽

Data Analysis Methods

Over the past several decades, high-resolution sediment–charcoal records have been increasingly used to reconstruct local fire history. Data analysis methods usually involve a decomposition that detrends a charcoal series and then applies a threshold value to isolate individual peaks, which are interpreted as fire episodes. Despite the proliferation of these studies, methods have evolved largely in the absence of a thorough statistical framework. We describe eight alternative decomposition models (four detrending methods used with two threshold-determination methods) and evaluate their sensitivity to a set of known parameters integrated into simulated charcoal records. Results indicate that the combination of a globally defined threshold with specific detrending methods can produce strongly biased results, depending on whether or not variance in a charcoal record is stationary through time. These biases are largely eliminated by using a locally defined threshold, which adapts to changes in variability throughout a charcoal record. Applying the alternative decomposition methods on three previously published charcoal records largely supports our conclusions from simulated records. We also present a minimum-count test for empirical records, which reduces the likelihood of false positives when charcoal counts are low. We conclude by discussing how to evaluate when peak detection methods are warranted with a given sediment–charcoal record.

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