Implementation First of a Kind Edge Computing Solution to Increase Production and Reduce Emissions at Bakken and Permian Unconventional Basins
Notice bibliographique
Résumé
Abstract There is a need to reduce carbon emissions sources at production facilities, which can be addressed by employing a digital solution that optimizes the facility's operational set-points in real-time. A novel software solution was developed which can operate on a cost-effective controller at a facility. The software uses many process variables already monitored at a facility to accurately model variables that are not measured, such as the Reid Vapor Pressure (RVP). The software also computes optimized set points, such as the minimum oil temperature required to meet the RVP of the final product. The novel digital system was installed at one facility in the Bakken and two facilities in the Permian. At the first facility (Bakken), the temperature was optimized using an air cooler. At the second and third facilities in the Permian, the temperature was optimized using heaters. The temperature was optimized to minimize emissions and maximize oil shrinkage whilst ensuring that the RVP would not exceed a specified value required for custody transfer. It was found that the initial model needed calibration at all three sites. The model was calibrated using oil samples that were processed with an analyzer. The calibration method applied was API MPMS 4.2 Appendix C, whereafter the RVP predictions consistently were within a ± 0.5 psi delta compared to values measured with a sample analyzer. Once the system successfully optimized the oil temperature in the Bakken, the tank vapor combustion and oil shrinkage reduced by 40% and 0.7%, respectively. The oil temperature was successfully optimized using the heater at the second facility (Permian West), resulting in a 72% reduction of heater emissions, 28% reduction of tank vapor combustion and 0.5% reduction of oil shrinkage. At the third facility (Permian East), heater emissions were reduced by 77%, tank vapor combustion reduced with 40% and oil shrinkage reduced by 1.7%. The field trials prove that the system can be implemented successfully without cyber-security issues. The relatively low implementation cost combined with reduced oil shrinkage results in an attractive return on investment and a negative cost of carbon abatement. In other words, implementing the system is profitable whilst carbon emissions are reduced at the same time, yielding an attractive business case.
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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 | 0,000 |
| 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 ».