Notice bibliographique
Résumé
The past 25 years marked the emergence of shale as the next frontier for oil and gas production, and JPT’s Houston office was right in the middle of it all. Credit for starting this disruptive change goes to George P. Mitchell who led a 17-year-long effort to extract gas from the ultratight rock in the Barnett formation. A JPT story described how this effort was finally looking like a significant, profitable business in 1998 when Mitchell Energy’s engineers turned to lower-cost slickwater fracturing in their vertical wells. The success of this trial-and-error engineering marathon drew little notice at the time. But that seed grew into a huge business based on technology that showed it was possible to profitably produce oil and gas from virtually impermeable rock using large-scale fracturing and ever-longer horizontal wells. In the coming decades this reverberated through drilling, fracturing, and reservoir engineering, disciplines whose physics-based view of the world could not predict how that would happen. As JPT’s staff grew with the addition of reporters covering emerging technology, there was no ignoring what was going on around us. The magazine highlighted futuristic reservoir monitoring ideas in Saudi Arabia and advances in deepwater technology in Brazil, but the booms and busts of this high-profile business that delivered more oil than profits kept changing in interesting ways. The first SPE Hydraulic Fracturing Technology Conference I covered in 2011 was amazing and puzzling to this technology novice. I wondered how they could create fractured areas that looked like shoe boxes. At an early show, a speaker advised those who believed that fracturing knowledge began with shale to look back further in OnePetro. One of those paper authors, then and now, is Dave Cramer, senior engineering fellow for ConocoPhillips, who began learning fracturing when he graduated from college with a business administration degree and got a job with Western Company of North America. The singular change over the past 25 years is how much fracturing has been scaled up. “It like a factory out there, and it was not a factory when we started,” he said. In the previous 25 years, fracturing experts were doing a lot of innovative things. He proudly remembers being involved in a job for Amoco where they pumped 600,000 gallons of gelled fluids and 1.3 million pounds of large-mesh sand into a 35-ft tight sandstone section of a well in the DJ Basin in Colorado. The morning he told that story he was evaluating a well completed in Canada where ConocoPhillips used slickwater to pump 970,000 pounds of sand per frac stage. In some ways the job in 1978 looms larger in his mind. The whole job was aimed at a single target and was sophisticated: They pumped five different fluid mixtures as the job progressed based on their lab testing and fracture modeling. While he thinks the technical skills of those fracturing tight rock back then are underestimated, wells now with laterals that are miles long and hundreds of clusters to stimulate are so much more complex, which has created an insatiable appetite for data by engineers continually feeling pressure for greater production and efficiency.
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 | 0,003 | 0,001 |
| 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 | 0,001 |
| 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 ».