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Record W7058074281

Methane leakage from abandoned gas wells in the Netherlands, reality or fiction?

2017· dissertation· en· W7058074281 on OpenAlexaboutno aff

Bibliographic record

VenueUtrecht University Repository (Utrecht University) · 2017
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsMethaneGreenhouse gasMethane gasFugitive emissionsWater wellCarbon dioxideMethane emissionsFlux (metallurgy)Aquifer
DOInot available

Abstract

fetched live from OpenAlex

A number of studies performed in the U.S. and Canada has proved that methane leakages from abandoned gas wells exist. The leakages pose environmental and safety risks as they represent a non-negligible amount of methane emitted in the atmosphere. IPCC inventories on gas emission considered as nulle the emitted methane from abandoned gas wells. Methane is a potent greenhouse gas 30 times stronger than carbon dioxide in the atmosphere. Methane in groundwater aquifer favors the risk of contamination of pollutants. Because the Netherlands is an important gas-producing country, this makes the question relevant whether or not methane leakages from abandoned wells occurs in the Netherlands as well. Therefore, this research aims at detecting: (1) the methane flux emissions of a selected number of abandoned gas wells, (2) the evidence of high methane emitter, and (3) the source of the emitted methane, i.e. biogenic or thermogenic.\n29 abandoned gas wells were investigated in the Netherlands during July 2017 in order to identify the presence of any leakages. In this research, a field work monitoring campaign has been performed. As a first step, a screening measurement was performed in a circle of approximately 16 meters radius for 24 wells locations, excluding 5 others wells because of obstacles on sites (e.g. presence of ditches, ponds or invasive vegetation). Secondly, two sets of flux measurements (one at the surface and one at one meter depth) were performed at the controls and at the exact X-Y coordinates of the wells using the static chamber method for the 29 wells selected. Finally, the isotopic analysis of 35 samples was monitored at IMAU laboratories (Utrecht University) using the δ13C and δD ratios as references.\nThe concentration screening method in a 16 meters radius circle used to identify evidences of high fluxes at the surface has proved to be inefficient as high fluxes were detected independently from the results of the screening. Only one decommissioned well located at Monster (MON-02) had a significant high flux (40,026 mg/(hr.m2)) of methane. Further analysis of the isotopic composition for this site revealed the presence of thermogenic gas, confirming the hypothesis of leakage for this well. Three other wells have shown methane fluxes above 100 mg/(hr.m2), for which only one has been performed at ground surface. The application of a linear model to time series of methane concentrations for a spot showed a better fit of the flux estimates for the measurements performed at one meter depth compared to those at the surface. The outcomes of the isotopic analysis showed the existence of two main biogenic groups differing for their δD ratios: primary biogenic methane that became altered in their isotopic composition due to partial oxidation. Considering the abundant presence of peat in the Netherlands, a comparison with the regional occurrence of the shallow Holocene Nieuwkoop Formation (which is by definition composed of peat) did not allow any reliable conclusion on the origin of the two groups of biogenic methane. \nThe presence of one well with a high flux of thermogenic methane (MON-02) out of 29 investigated makes the Netherlands a country subject to leakages. Considering further research, the circled screening method performed is not recommended for leakage identification. However, performing the methane measurements at one meter depth is an efficient way to detect fluxes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.535
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0230.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.245
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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