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Record W2043018369 · doi:10.2118/0305-0026-jpt

Techbits: Coalbed-Methane Recovery and CO2 Sequestration Raise Economic, Injectivity Concerns

2005· article· en· W2043018369 on OpenAlexaboutno aff
JPT staff

Bibliographic record

VenueJournal of Petroleum Technology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsCoalbed methaneEngineeringOfficerManagementCoalWaste managementPolitical scienceCoal miningEconomics

Abstract

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Representing Australia, the U.S., Canada, and Europe, 40 engineers and geologists attended an SPE Applied Technology Workshop (ATW) focused on the viability of enhanced coalbed-methane (ECBM) recovery and CO2 sequestration in coalbeds. The workshop “Enhanced Coalbed-Methane Recovery and CO2 Sequestration,” held in Denver from 27–29 October 2004, was led by Scott Reeves, Executive Vice President of Advanced Resources Intl., and the program committee included Bill Gunter, Research Officer of the Alberta Research Council; Mike McGovern, Senior Staff Engineer of Burlington Resources; Henk Pagnier, Program Manager-SUS Energy, TNO-Netherlands Inst. of Applied Geosciences; and Pablo Tejera Cuesta, Reservoir Engineer for Shell Intl. E&P. The ATW featured six sessions focusing on different aspects of ECBM recovery and CO2 sequestration, providing an excellent snap-shot of research and pilot programs in this topic area. Session 1, moderated by Reeves, introduced the workshop and reviewed the state of ECBM-recovery technology. During his introduction, Reeves noted that ongoing work in ECBM recovery is very research oriented and is driven by CO2 sequestration. Cuesta continued the session with an introduction to ECBM-recovery technology, stating that coalbed methane is a significant part of the global hydrocarbon resource base. He reviewed the reservoir characteristics of coal (classified as a continuous-type reservoir) in which gas storage is dominated by adsorption. Major points of discussion highlighted economic considerations, including the cost of injectant required for ECBM-recovery operations as well as the separation, capture, and storage of CO2. Lessons Learned Gunter moderated Session 2, focusing on pilot projects and emphasizing results and lessons learned and noting that experience with ECBM recovery and CO2 sequestration in coal includes one large-scale project in the Allison Unit of the San Juan basin and a series of single-well micropilots in Canada, Poland, and China. These pilot programs have focused on coal of bituminous-through-anthracite rank, and major issues being addressed include geomechanics, permeability, matrix swelling, and fracture porosity. McGovern presented the results of CO2 flooding in the Allison Unit, motivated by the desire to recycle CO2 produced from Fruitland coal while enhancing CBM production. Within the Allison Unit, CO2 was injected continuously for more than 5 years. During the project, approximately 4.7 Bcf of CO2 was injected, and approximately 4.2 Bcf of CO2 was sequestered. Incremental methane recovery during the project was approximately 1.5 Bcf, and the ratio of CO2 injected to methane produced was approximately 3.1:1.0. David Law of the Alberta Research Council presented results of micropilot programs in Mannville coal of the Western Canada sedimentary basin and the No. 3 coal of China’s Qinshui basin. In the pilot areas, Mannville coal is of highly volatile B bituminous rank, and the No. 3 coal is of anthracite rank. Well testing in these micropilots included multiple production and injection-falloff tests. The Mannville micropilot is notable because a variety of gases were injected, including pure CO2, flue gas, pure N2, and CO2-enriched flue gas. The Qinshui basin contains the first pattern of commercial CBM wells drilled in China and contains the highest-rank coal developed for CBM to date. Well testing in the Qinshui basin confirms that sequestration potential exists in anthracite and that permeability can exceed 12 md.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.256
Teacher spread0.248 · 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
GenreEmpirical

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

Citations1
Published2005
Admission routes1
Has abstractyes

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