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Enregistrement W1999035071 · doi:10.2118/1214-0084-jpt

Technology Focus: Reserves/Asset Management (December 2014)

2014· article· en· W1999035071 sur OpenAlexaboutno aff
Delores Hinkle

Notice bibliographique

RevueJournal of Petroleum Technology · 2014
Typearticle
Langueen
DomaineEngineering
ThématiqueReservoir Engineering and Simulation Methods
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAsset (computer security)Value (mathematics)Reading (process)Executive summaryTheme (computing)Work (physics)Computer scienceOperations researchMarketingBusinessEngineeringPolitical scienceWorld Wide WebComputer securityLawFinanceMechanical engineering

Résumé

récupéré en direct d'OpenAlex

Technology Focus When I joined the petroleum industry almost 40 years ago, integration of the various aspects of our work seemed like something out of a science-fiction movie. As an industry, we have come a long way. More than half of the roughly 120 abstracts of papers presented this year in the category of reserves and asset management addressed integration in some fashion. Mercifully, advances in recording instruments and computers have released the handcuffs of limited data and cumbersome calculations. I fear the handcuffs may have been replaced by data overload, but I will leave that discussion for my colleagues who present the information-management section in this magazine. One popular theme was something we sometimes lose sight of: how to integrate investments and production into portfolios without destroying value. Both technical and planning advances have been addressed at length in the presentations from which the papers summarized and those recommended for additional reading were selected. Optimization was also a very popular topic among the papers I reviewed. With the current emphasis on the development of unconventional properties, both the technical and economic optimization of well spacing and stimulation techniques was addressed in numerous papers. Several authors used different approaches, but most came to the same conclusion: The balance between net present value and overdrilling or fracturing to maintain rate is a tricky one. In addition to the highly technical discussions, there were several papers addressing the maturing regulatory environment. Because the Modernized SEC Rules, Canada’s NI-51, and the SPE Guidelines for Application for Petroleum Resources Management System have been around for several years now, there were numerous papers on the handling of specific situations within those regulations. Resource estimation and reporting continue to be topics of much interest, specifically in unconventional reservoirs. I always enjoy reviewing papers for this section. When I sat back to reflect on what would be the most useful information to present, it occurred to me that the theme of growth and maturity was reflected in almost all of the papers. Our understanding of how to estimate unconventional reserves and resources properly has definitely grown over the last few years. Our realization that we should not look at any individual aspect of our business such as drilling or production without considering its place in the project life cycle, including its economics, reflects a maturity within the industry, as does learning how to reflect our assets effectively in a changing regulatory environment. It is satisfying to see the depth and strength of the advances in our industry, no longer the stuff of science fiction. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 170616 Interpretation of Recent SEC Reserves-Reporting Guidelines by Enrique Morales, SGS Horizon, et al. SPE 169984 Optimized Shale-Resource Development: Balance Between Technology and Economic Considerations by U. Ahmed, Baker Hughes SPE 169564 Estimation of Stimulated Reservoir Volume Using the Concept of Shale Capacity and Its Validation With Microseismic and Well Performance: Application to the Marcellus and Haynesville by A. Ouenes, Sigma Cubed, et al. SPE 170681 Integrating Unconventional- Resource Opportunities Into an Exploration-and-Production Portfolio by Larry Chorn, Halliburton, et al.

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)0,001
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: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,654
Score d'incertitude au seuil0,809

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
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 Opus0,007
Tête enseignante GPT0,248
Écart entre enseignants0,241 · 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'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

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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