Use of PITA for Estimating Key Reservoir Parameters
Notice bibliographique
Résumé
Abstract Well testing is sometimes reduced to perforating the well, capturing the pressure data and analyzing the data. The primary objective of a Perforation Inflow Test Analysis (PITA) is to estimate the initial reservoir pressure, permeability and skin, for evaluating future development strategy. However, special analytical procedures are required for analyzing the data, because these perforation inflow tests are considerably shorter than conventional well tests, and there is no recorded production. In this study, a systematic analytical procedure for estimating meaningful reservoir parameters from perforation inflow tests will be presented. Two major aspects of data interpretation will be discussed. Diagnostic Analysis and Modeling. A straight-line approach is taken to analyze the early-time and late-time data. A special diagnostic technique is required for detecting and estimating positive or negative skins. A distinctive feature of PITA, is that it does not require calculation of the inflow rates. Note that the same approach can be applied in over-balanced situations, when there is fluid efflux, rather than fluid influx. When employing straight-line analysis techniques, acceptable estimates of initial reservoir pressure, permeability and skin are only obtained if the test duration is sufficient to achieve radial flow. This is usually not a problem in high permeability reservoirs. However, in low permeability reservoirs, the test duration required to reach radial flow canbe prohibitively long. In these cases, the test is often terminated during the transition from wellbore storage to radial flow. Consequently, acceptable estimates of initial reservoir pressure, permeability and skin can only be obtained by extending the analysis into modeling. Field examples will be presented to highlight the methodology. A rigorous technique for estimating the radius of investigation during the test will be discussed. It will be shown that in the presence of measurement errors, radius of investigation will grow to a maximum value. Running the tests for anytime longer will be dominated by the noise. Introduction Well tests have been the primary and most reliable means of characterizing reservoirs for decades. However, there has been a growing trend over the last several years to search for alternatives that could yield the desired information in less time, in a more environmentally-friendly manner, and at a cheaper cost than conventional well tests. The desired change has inevitably been towards tests of shorter duration.1–4 Although it is accepted that results from short tests with small radii of investigation may not be as reliable as those from conventional well tests, it is reasonable to accept that they could be of value in assisting with strategic decisions about field development, when an increased margin of error can be tolerated. In offshore wells, in addition to the potentially exorbitant cost of testing (several millions of dollars), the drive towards green (shorter) tests is fuelled by environmental considerations, such as requirements for restricted flaring of hydrocarbons. In Alberta and elsewhere in North America, the driving force towards inexpensive tests is the marginal economics of low deliverability wells. Either way, there is an increasing trend towards these green tests to replace conventional well tests.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,000 |
| 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 ».