Efficient Detection of Productive Intervals in Oil and Gas Reservoirs
Notice bibliographique
Résumé
Abstract The utility and evaluation of cutoff values for net pay or net-to-gross determination have been hotly debated topics since the 1950s. There are numerous subtleties to cutoffs, but exactly how the values are calculated has largely been overlooked. Most cutoff users have been content to use a regression line to calculate the cutoff value. We show that cutoffs obtained using a regression line are likely to be inferior to estimates produced by other methods. When four methods were applied to two field datasets and compared, regression-based porosity cutoffs were between 1 and 2 pu different than the values that give the smallest number of errors. Monte Carlo simulations broadly support the results obtained from the datasets. One method, the "trial-and-error?? method, performed well through most of the tests, reducing errors by 40% from those obtained using the regression line-based cutoff. All cutoff estimation methods have errors, caused by the imperfect relationships we have between variables, such as porosity and permeability. This study shows we have a choice of methods. Because the better method can be easily applied in spreadsheet software, this should be a valuable addition to the petrophysicist's toolbox. Introduction Net pay (NP) may be defined as any interval containing economically producible hydrocarbon using a specific production method. This represents the portion of the reservoir that contains sufficient porosity, permeability and hydrocarbons for economic exploitation. NP can be interpreted as an effective thickness that is pertinent to identification of flow units and target intervals for well completions and stimulation programs [Worthington and Cosentino(1)]. The associated net-to-gross ratio (NGR) corresponds to the proportion of the total or gross thickness, which is composed of net pay. Numerous papers have reviewed and proposed methods for NP and NGR determination. Snyder(2) covers many of the methods in use up to the early 1970s, which used the self-potential (SP) or gamma ray (GR) logs and core analysis. More recent proposals include using capillary pressure [Vavra et al.(3)], probe permeameter measurements [Flølo et al.(4)] and percolation modelling [Li et al.(5)]. Of the large variety of possible methods, one approach is much more commonly discussed than any other. This method involves defining threshold values (or cutoffs) for the characteristics of interest and their surrogates. These limiting values are designed to define those rock intervals that show potential to contribute significantly to economic hydrocarbon production.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».