Case Study in Production Optimization Applying Information Technology
Notice bibliographique
Résumé
Abstract Well data for oil and gas wells drilled in the Western Canadian Sedimentary Basin (WCSB) exists in many places and multiple formats with ranging detail. Multiple data sources, both internal and external, are needed to truly optimize the well stimulation and the production optimization process. Production and well data in the WCSB is considered the highest in quantity and quality for reservoir description in the world. This data repository exists due to royalty and tax issues and is strictly enforced by government legislation. Unfortunately, detailed completion data is not part of the public record. However, companies such as BJ Services whose core business includes drilling and completion field services usually do keep a comprehensive and detailed collection of data. These drilling and completion companies often have data that is unavailable in the public databases. This paper outlines the first steps in developing a synergistic approach to integrating publicly available production and well data with detailed completion data from private sources. The case study presented shows the effectiveness of combining internal detailed completion data with external third party data. The case study evaluates completions in a Medicine Hat formation shallow gas play in southeast Alberta, Canada and the production impact realized. Introduction There are various sources of information regarding oil and gas activity in the WCSB. Operators, government bodies, data vendors and service companies all collect information. The formats vary from paper to electronic and from organized databases to scattered documents. Information on a large scale is most valuable when combined with related information from different sources. A common point of reference such as the Public Petroleum Data Model (PPDM) implemented in a powerful relational database such as Oracle creates this value when different data sources are mapped into it. Data will typically exist on several different platforms. The highly structured data stored in relational databases such as Oracle, Microsoft SQL Server, or even Microsoft Access is the easiest to work with. Semi-structured data is found in Lotus Notes and Microsoft Exchange. While having the advantage of being centrally administered like a database, it is often more difficult to work with the data due to the "flexible" nature of the underlying technologies. Finally, the huge collection of documents, electronic and paper, created over many decades are on various hardware platforms from mainframe to handheld. The apparently endless variety of layouts and file formats in word processors, spreadsheets, and specialized engineering applications are the most difficult to work with, but often are also the most rewarding. After all, these reports were usually created by engineers thinking deeply before committing their ideas to paper. Several vendors offer a variety of local data installations and/or Internet data hubs. Their data may be either hand entered from original source documents or received on tape from government boards. Information varies widely since there are so many datasets and sources to choose from. Some types of datasets available:
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 | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».