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Enregistrement W4244740968 · doi:10.2523/77598-ms

Analysis of Generated and Reflected Pressure Waves during Fracturing Reveals Fracture Behavior

2002· article· en· W4244740968 sur OpenAlexaffabout
Jim B. Surjaatmadja, Stephenson Stan, Bhaumik Champak, Thompson Stewart, Cheng Alick

Notice bibliographique

RevueProceedings of SPE Annual Technical Conference and Exhibition · 2002
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueSeismic Imaging and Inversion Techniques
Établissements canadiensHaliburton Forest & Wild Life ReserveNexen (Canada)
Organismes subventionnairesnon disponible
Mots-clésCitationExhibitionLibrary scienceComputer scienceEngineeringArt historyArchaeologyWorld Wide WebHistoryOperations research

Résumé

récupéré en direct d'OpenAlex

Analysis of Generated and Reflected Pressure Waves during Fracturing Reveals Fracture Behavior Jim B. Surjaatmadja; Jim B. Surjaatmadja Halliburton Search for other works by this author on: This Site Google Scholar Stan Stephenson; Stan Stephenson Halliburton Search for other works by this author on: This Site Google Scholar Champak Bhaumik; Champak Bhaumik Nexen Canada Ltd. Search for other works by this author on: This Site Google Scholar Stewart Thompson; Stewart Thompson Halliburton Canada Search for other works by this author on: This Site Google Scholar Alick Cheng Alick Cheng Halliburton Canada Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. Paper Number: SPE-77598-MS https://doi.org/10.2118/77598-MS Published: September 29 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Surjaatmadja, Jim B., Stephenson, Stan, Bhaumik, Champak, Thompson, Stewart, and Alick Cheng. "Analysis of Generated and Reflected Pressure Waves during Fracturing Reveals Fracture Behavior." Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. doi: https://doi.org/10.2118/77598-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractFracture behavior is an important aspect in fracturing technology. Many techniques are available for prestimulation simulations and post-stimulation analysis of fracture behavior. However, very few techniques address fracture behavior during the stimulation process itself. Various fracture behaviors, such as fracture extension, ballooning, and tip screenout are often not known to the operator until after it is too late or even after the job is completed. Therefore, it is important to create and evaluate different real-time analysis techniques that can be used to capture the critical information available from data gathered during jobs.During the stimulation process, many types of information are available to the engineer. Pressure, flow, and temperature are basic information evaluated by many in the past. However, surface-pressure measurements are heavily influenced by friction, densities, proppant concentrations, and flow fluctuations, which makes conventional analyses difficult, if not impossible. In spite of this, analysis of dynamic pressure fluctuations has not been actively pursued. Certain changes in the downhole configuration, such as fracture extension, may send different pressure frequency spectra and wave intensities to the surface. The signature of these pressure waves is believed to carry such information to the surface. It is also believed that signal degradation due to friction may not influence the outcome of this type of analysis.Capturing and evaluating generated and reflected pressure waves during fracturing may be a new approach to monitor what happens downhole during fracturing. This paper discusses different real-time analysis approaches, such as frequency analysis and wavelet technologies, and evaluates their results and compares them to real job data. These analysis results and the successful stimulation results are presented in this paper.IntroductionStimulating wells that behave nicely (e.g., wells that are easily stimulated) allows service companies and operators to follow standard procedures commonly performed on such wells. No special attention needs to be placed upon specifics, such as how the fracture behaves; all decisions and actions are based upon the experience the industry has acquired in the last five decades.1However, as the hydrocarbon supply decreases and demand for it increases, the hunt for hydrocarbons becomes more challenging. New technologies, such as fluid chemistry and rheology, or even new stimulation techniques enter the marketplace. These techniques claim to provide better fracture creation, better conductivities, permeability modifications, and more. As these technologies are used, new methods for evaluating the effectiveness of the treatments are needed.In the field of fracture-size development and measurement, tiltmeter technology may be one practical way to detect fracture shape and size. However, special equipment is required to capture this data and the sensitivity requirements make this process quite costly. In this paper, possible practical and economical means of observing fracture development with less elaborate schemes are investigated. Some of these methods eventually could be used for mapping the fracture in the future. Keywords: bhaumik, thompson, fracture development, surjaatmadja, hydraulic fracturing, cheng spe 77598, stephenson, upstream oil & gas, pressure wave spe 77598, annulus pressure Subjects: Hydraulic Fracturing This content is only available via PDF. 2002. Society of Petroleum Engineers You can access this article if you purchase or spend a download.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,296
Score d'incertitude au seuil0,538

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
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)0,0000,000

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 Opus0,020
Tête enseignante GPT0,239
Écart entre enseignants0,219 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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

Citations0
Publié2002
Routes d'admission2
Résumé présentoui

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Même revueProceedings of SPE Annual Technical Conference and ExhibitionMême sujetSeismic Imaging and Inversion TechniquesTravaux en français237 207