Application of LogFAC* to Coalbed Methane Exploration in Western Canada: A Case History from Ardley Coals near Red Deer, Alberta
Notice bibliographique
Résumé
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.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».