Technology Focus: Bit Technology and Bottomhole Assemblies (December 2013)
Notice bibliographique
Résumé
Technology Focus In previous editions of this feature, I highlighted the industry’s need for capability improvement in applied drilling optimization and we have discussed the gaps in people (knowledge), process (workflows), and equipment or technology that remain to be closed before true drilling optimization can be said to be occurring. Again this year, I have selected papers that either do or potentially can take us closer to that goal. We can see from these excellent publications that progress continues to be made. However, there are plenty of factors and trends in our industry that can make drilling optimization difficult to achieve. Do we even have time to optimize, or do we have time only for execution of repetitive plans? Is our operation so remote that other priorities and constraints dictate what we can do? Has our workforce become so affected by decreasing levels of experience that we do not even know what to look for? Is our company so small that other, bigger organizations take up the industry’s optimization efforts? These are legitimate difficulties, of course, meaning that any optimization effort is going to have to survive the challenge of prioritization. And who decides priorities? Our leaders, of course. So this article, this time, is focused on the leaders who decide where our priorities lie and, hence, how our operations are being managed. We are going to put some questions out there that may help those leaders to see the optimization opportunity. Is rate of penetration in your operations below limits imposed by the capacity of the rig? Is your operation plagued by trip time for worn-out bits or drillstring or downhole-tool failures? In your operations, is the time spent drilling and tripping for the reasons in Question 2 a meaningful portion of your total well time? If the answer is “yes” to at least two of these questions, think about this next step: Is your team supported by an impressive combination of gurus, working processes, and technology that is determinedly focused on changing those “yeses” to “nos?” The thinking is this: If we rigorously assess our optimization capability, how much capability can we really claim to have? And is that enough, given the potential prize from doing better? Of course, judging what “good” looks like is often subjective, but, for us readers, SPE’s excellent archive of technical papers can help us decide. So, while we celebrate the steps forward described by the papers in this feature, and while we enjoy the continued contribution that SPE makes to development and dissemination of technology and best practices, when we turn back to our own activities and operations, let us ask, “Have we really got the capability we need?” And perhaps, just perhaps, the revelations that may come from such self-assessment will provide added impetus toward truly optimized drilling performance. Recommended additional reading at OnePetro: www.onepetro.org. SPE 156136 Structured Mentoring: A Critical Component of a Global Talent-Management Strategy by Meta Rousseau, Baker Hughes SPE 164365 Filling the Experience Gap in the Drilling-Optimization Continuous-Improvement Cycle Through a Self-Learning Expert System by Cliff Kirby, Baker Hughes, et al. SPE 159948 Using Equipment Simulators for Effective Training, Increasing Competence in Well-Services Operations by Anthony Celano, Baker Hughes, et al. SPE 164993 Virtual Reality as Effective Tool for Training and Decision Making: Preliminary Results of Experiments Performed With a Plant Simulator by S. Colombo, Politecnico di Milano, 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 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) | 0,001 | 0,000 |
| Bibliométrie | 0,005 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».