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Record W2037192548 · doi:10.2118/90256-ms

Alberta Multiwell Micro-Pilot Testing for CBM Properties, Enhanced Methane Recovery And CO2 Storage Potential

2004· article· en· W2037192548 on OpenAlexaboutno aff
Matthew J. Mavor, William D. Gunter, John R. Robinson

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2004
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsCoalbed methaneCoalPetroleum engineeringPermeability (electromagnetism)Environmental scienceShut downCoal miningGreenhouse gasMethaneFossil fuelNatural gasMining engineeringVolume (thermodynamics)Waste managementEngineeringGeologyProcess engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract The Alberta Research Council (ARC) is performing a project entitled "Sustainable Development of Coalbed Methane; A Life-Cycle Approach to Production of Fossil Energy" that is funded by an international consortium of companies. The main objectives of the project are to reduce greenhouse gas emissions by subsurface injection of CO2 into deep coalbeds and to enhance coalbed methane recovery and production rates. We have performed extensive field tests that includes efforts on two wells located near the towns of Fenn and Big Valley in Alberta that penetrated Medicine River (Mannville) coal seams. This paper presents test procedures, quantitative data measured during this effort, and the interpretation thereof. The evaluation was completed over three years ago and was based upon procedures described previously.1 These data have allowed complete characterization of the coal seam properties with new insights gained into the behavior of coal seams, the volume of natural gas that can be produced, and the volumes of CO2 that can be sequestered in this area. The fieldwork began by reentering the Gulf Canada (now ConocoPhillips) well FBV 4A-23-36-20 (FBV 4A) well. We conducted a series of single well micro-pilot tests. We began by production and shut-in testing to obtain estimates of the reservoir pressure and permeability before CO2 injection. A CO2 injection test was followed by a shut-in test to insure that CO2 injection was possible. We then injected over 91,500 m3 of CO2 vapor in 12 separate injection cycles. Although CO2 reduced the absolute permeability, injectivity actually increased. The CO2 was allowed to soak into the coal and we returned the well to production. Post-injection testing allowed us to determine the CO2 sweep efficiency as well as the ECBM and CO2 storage potential. Fourteen months later, we injected 83,500 m3 of flue gas using underbalanced drilling equipment that was followed by a post injection-production test. A second well (FBV 5) was drilled 487 m north of the first well. FBV 5 was cored and logged for reservoir property data. The well was then cased and completed in one Medicine River coal seam. A combination of production tests and water-injection falloff tests were conducted to determine original reservoir pressure, permeability, and gas composition. N2 injectivity tests were performed before injecting 75,483 m3 of a 53%-47% mix of N2 and CO2. The gas mixture was allowed to soak into the coal and the well was returned to production. All of the post-injection production tests of both wells included detailed measurement of pressure and temperature conditions as well as gas composition variations vs. time. These data allowed us to determine the changes in permeability caused by the injected gas, to estimate possible hydrocarbon sweep efficiency, and possible CO2 storage volumes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.226
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations102
Published2004
Admission routes1
Has abstractyes

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