Efficiency and Effectiveness - A Fine Balance: An Integrated System to Improve Decisions in Real-Time Hydraulic Fracturing Operations
Notice bibliographique
Résumé
Abstract Hydraulic fracturing is a key driver of well productivity and field development planning, in addition to being the most significant portion of capex in shales. Recent breakthroughs in connectivity and digital technologies have enabled the monitoring and analyses of frac operations in real-time. However, most of the digitalization effort to date has been focused on increasing operational efficiency to reduce cost. Without an equal consideration for creating effective fracture geometries, this may lead to poor resource recovery and leave significant value behind. In this paper, we - 1) demonstrate the need to balance between optimizing fracture efficiency and effectiveness; 2) present an integrated system for frac optimization using real-time, historical data along with organizational knowledge; and 3) discuss the challenges of setting up such a system and key considerations, along with examples of large, untapped potential that can be unlocked with data science to deliver real value. Currently, several service providers exist to stream frac data with interactive analytics dashboards. While they offer some customizability, most do not provide a true frac optimization platform that goes beyond frac monitoring and analytics geared towards efficiency and cost indicators. We are still dependent on an individual operator's experience and rules of thumb to make job decisions during a frac stage. In this paper, a real-time optimization workflow is presented that uses advanced data science and statistical techniques to interpret and predict time-series treatment data, integrate historical and contextual information, and honor basin-specific knowledge that has been gathered and tested over the years. Examples are presented from diagnostic pads that highlight the need for balancing stimulation effectiveness with efficiency. We demonstrate a platform to host and execute an ensemble of models and visualizations that communicate actionable insights to an operator within minutes of identifying an event, gather feedback, and learn. Results from field testing show that our system accelerates the learning curve, enables consistent decision making by operators, and can generate significant cost savings. Finally, we share learnings from our digitalization journey. Completion and stimulation expenses account for approximately half of an unconventional well cost. Automated decision making for real-time fracture treatment is the holy grail of digital completions in shales. However, a blind pursuit of efficiency may lead to sub-par fracture treatments and significant value erosion for shale assets. We present an integrated framework that connects real-time data and organizational knowledge to guide an operator to pump the best frac stage while reacting to formation response within a set of constraints. To the best of our knowledge, this is the first paper to describe the general architecture and demonstrate the viability of such a system that relies only on standard wellhead measurements during fracturing.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».