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Record W1981864559 · doi:10.2118/75676-ms

Application of LogFAC* to Coalbed Methane Exploration in Western Canada: A Case History from Ardley Coals near Red Deer, Alberta

2002· article· en· W1981864559 on OpenAlexaboutno aff
A. Thomas Rozak, R.M. Bustin, Gary W. Strashok, A.P. Beaton, Richard J. Richardson, Trent Hunter

Bibliographic record

VenueAll Days · 2002
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsCoalbed methaneGeologyDrillingCoalPetroleum engineeringPermeability (electromagnetism)Structural basinSedimentary rockMethaneHydrogeologyMining engineeringGeochemistryCoal miningGeotechnical engineeringGeomorphologyEngineering

Abstract

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Abstract LogFAC fracture detection software was used as the primary exploration tool to delineate a potential coalbed methane reservoir in Tertiary age Ardley coals of the Scollard Formation near Red Deer, Alberta. Drill results show a methane saturated coal with an average of 5.8 millidarcies of permeability over 6.0 meters of pay. Introduction Permeability is considered a major control on coalbed methane producibility and is one of the reservoir characteristics sought by explorationists. An application of a new geophysical log interpretation technology for detection of fractured and permeable coals is outlined in this paper. LogFAC is a software program that re-interprets existing conventional well log data and calculates LogFAC permeability factors (LPF's) for these data [1]. The technique is applicable to developed basins where significant well control exists. In developed basins such as the Western Canada Sedimentary Basin a large database of subsurface information exists in the form of geophysical well logs. In the Western Canada Sedimentary Basin many prospective coalbed methane targets have been traversed and logged in the search for deeper conventional hydrocarbon reserves. LogFAC software uses these conventional well log data to calculate a volume of movable fluid present in the coal's natural fracture, or cleat, system, whereby a larger moveable fluid volume infers greater permeability. The use of existing well log data to locate permeable zones in coal is a cost effective method of delineating exploration targets. LogFAC Theory During drilling operations, drilling mud is used for well control, cooling the bit and cleaning the hole of rock cuttings. Drilling mud is composed of a solid clay faction suspended within a liquid faction. On penetration of a porous and permeable zone, the mud solids separate from the liquids at the well bore and form a mud cake while the liquid faction, or mud filtrate, invades into the permeable formation. LogFAC is based on a well established well log interpretation principle which states that depth of invasion of mud filtrate is inversely proportional to porosity. This statement appears counter-intuitive, as deeper invasion might be expected in zones of greater permeability. However, the opposite occurs as only a certain amount of mud filtrate can be removed from the drilling mud before mud cake formation creates a very low permeability barrier to additional invasion. This amount of mud filtrate must be accommodated within the porous and permeable portions of the formation. In a high porosity formation, invading fluids must move a shorter distance into the formation to be fully accommodated. This creates the counterintuitive inverse relationship between depth of invasion and porosity [2]. Although it is well known that some general, though undefined, relationship exists between depth of invasion and porosity, it is problematic to well log analysts as this relationship does not hold true under all circumstances [3]. The nature and limits of this relationship has remained undefined probably due to a lack of need. Several conventional well logging tools and techniques exist for determining porosity and permeability in clastics and carbonates [3], so there was no pressing reason to investigate this phenomenon further.

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)0.000
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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.037
GPT teacher head0.201
Teacher spread0.164 · 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 designCase report
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

Citations6
Published2002
Admission routes1
Has abstractyes

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