Creating Open Source Models, Test Cases, and Data for Oilfield Drilling Challenges
Notice bibliographique
Résumé
Abstract The drilling industry has substantially improved performance based on knowledge from physics-based, statistical, and empirical models of components and systems. However, most models and source code have been recreated multiple times, which requires significant effort and energy with little additional benefit or step-wise improvements. The authors propose that it is time to form a coalition of industry and academic leaders to support an open source effort for drilling, to encourage the reuse of continuously improving models and coding efforts. The vision for this guiding coalition is to 1) set up a repository for source code, data, benchmarks, and documentation, 2) encourage good coding practices, 3) review and comment on the models and data submitted, 4) test, use and improve the code, 5) propose and collect anonymized real data, 6) attract talent and support to the effort, and 7) mentor those getting started. Those interested to add their time and talent to the cause may publish their results through peer-reviewed literature. Several online meetings are planned to create this coalition, establish a charter, and layout the guiding principles. Multiple support avenues are proposed to sustain the effort such as: annual user group meetings, create a SPE Technical Section, and initiating a Joint Industry Program (JIP). The Open Porous Media Initiative is just one example of how this could be organized and maintained. As a starting point, this paper reviews existing published drilling models and highlights the similarities and differences for commonly used drillstring hydraulics, dynamics, directional, and bit-rock interaction models. The key requirements for re-usability of the models and code are: 1) The model itself must be available as open source, well documented with the objective and expected outcomes, include commented code, and shared in a publicly available repository which can be updated, 2) A user's guide must include how to run the core software, how to extend software capabilities, i.e., plug in new features or elements, 3) Include a "theory" manual to explain the fundamental principles, the base equations, any assumptions, and the known limitations, 4) Data examples and formatting requirements to cover a diversity of drilling operations, and 5) Test cases to benchmark the performance and output of different proposed models. In May 2018 at "The 4th International Colloquium on Non-linear dynamics and control of deep drilling systems," the keynote question was, "Is it time to start using open source models?" The answer is "yes". Modeling the drilling process is done to help drill a round, ledge free hole, without patterns, with minimum vibration, minimum unplanned dog legs, that reaches all geological targets, in one run per section, and in the least time possible. An open source repository for drilling will speed up the rate of learning and automation efforts to achieve this goal throughout the entire well execution workflow, including planning, BHA design, real-time operations, and post well analysis.
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 | 0,000 | 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 ».