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Enregistrement W2099602450 · doi:10.2118/2005-031

Perforation Inflow Test Analysis (PITA)

2005· article· en· W2099602450 sur OpenAlexaff
N. M. Anisur Rahman, M. Pooladi‐Darvish, Louis Mattar

Notice bibliographique

RevueCanadian International Petroleum Conference · 2005
Typearticle
Langueen
DomaineEngineering
ThématiqueHydraulic Fracturing and Reservoir Analysis
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésInflowPermeability (electromagnetism)PerforationImpulse responseInversion (geology)UniquenessHydrostatic testGeologyPetroleum engineeringComputer scienceGeotechnical engineeringMathematicsEngineering

Résumé

récupéré en direct d'OpenAlex

Abstract Due to economics or time constraints, well-testing is sometimes reduced to perforating a well under-balanced, andanalyzing the inflow characteristics. The objective of a Perforation Inflow Test Analysis (PITA) is to estimate the initial reservoir pressure, permeability and skin, immediately after perforating the well. This information can be used for evaluating future development strategy. However, special analytical procedures are required for analyzing the data, because these perforation inflow tests are shorter than conventional well tests and the influx rates are not measured. In this study, the working equations for analyzing these short tests are presented, and the procedure required for calculating meaningful estimates of the reservoir parameters is presented. Analyses of early-time and late-time data are the two major components of this approach. The early-time analysis isused for estimating the skin, and the late-time analysis is used for estimating the initial pressure and permeability. A distinctive feature of the PITA is that it does not require calculation of the influx rates, which are generally not available during a perforation test. A special derivative, called the impulse derivative, can be used to determine if the data collected is sufficient to yield meaningful results from a PITA. It is particularly important that the reservoir-dominated flow regime be reached, if the estimates of initial reservoir pressure, permeability and skin are to be acceptable. Good estimates of these parameters from a PITA will minimize the uncertainty associated with non-uniqueness in inverse problems, when modeling the test data. Introduction Conventional well tests have served the petroleum industry faithfully for decades as the primary and most reliable means of:quantifying deliverability,characterizing the reservoir,collecting reservoir fluid samples, andevaluating the condition of the well. However, for the last few years, oil and gas producers have been searching for alternatives that could yield the desired information in less time, in a more environmentally-friendly manner, and at a cheaper cost than from conventional well tests. The trend has inevitably been towards tests of shorter duration. 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. InAlberta 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. One such green test consists of simply allowing the well to flow into the closed wellbore after perforating (closed chamber test). As the fluid from the reservoir enters the wellbore (with a fixed volume), the wellbore pressure builds up.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,344
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,208
Écart entre enseignants0,200 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2005
Routes d'admission1
Résumé présentoui

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