The Energy Transition, lessons learned from other heavy industries and the opportunities they present for Oil and Gas Operators demonstrated by associated case studies
Notice bibliographique
Résumé
Abstract The Oil and Gas industry today faces the ȢEnergy Trilemma,Ȣ that is satisfying the growing global demand for energy, in conjunction with increasing societal pressure to decarbonise whilst also reducing costs. The decarbonisation of Oil and Gas assets is often perceived to be a capital-intensive process, which will make operations more difficult and impact profitability. Whilst this may be true for the more aggressive/ambitious mitigation schemes, there are solutions that can significantly improve the bottom line. Many of these solutions can be easily implemented, without significant disruption, and result present material GHG reductions. This paper highlights the opportunities for Oil and Gas operators to identify, fund, and execute energy transition projects that have successfully decarbonised assets. The decarbonisation methodology builds on lessons learned in identifying low carbon transition pathways for other high emitting industries. The process begins with a framework and evaluation model to assess a wide set of potential carbon reduction technologies that Oil and Gas companies can use to achieve carbon reduction. The key evaluation and prioritisation tool is the marginal abatement model which incorporates low carbon transition scenario planning with extended functionality aimed at providing insights to successfully achieve the targeted reduction and the potential impact of these scenarios on future financial performance. Following the evaluation and prioritisation methodology, this paper will review two decarbonisation case studies that have identified positive cashflow outcomes. The first is the application of a hybrid energy system installed at a remote onshore site to reduce reliance on diesel. The second considers reductions in the cold venting operations on a complex offshore facility to reduce fugitive emissions. The first case study demonstrates how an energy transition programme resulted in the phased delivery of a complete hybrid energy system which integrated wind power, diesel generation, and several energy storage systems including hydrogen electrolysis, storage and fuels cells, as well as lithium ion batteries and flywheel technology, all managed by a custom microgrid controller to power this remote production site whilst reducing GHG emissions. This case study shows how experience and investment in another industry can be exploited in the Oil and Gas industry. The lessons from the first phase were applied to make the second phase more economic, resulting in significant operating cost savings and the reduction in GHG emissions is 10,530 tCO2-eq per annum. The second case study offers an approach to decarbonisation which can be applied more generally in the context of operational efficiency. The ease with which the project can be executed was also assessed to ensure minimum operational downtime during the implementation phase. Our paper concludes that energy transition initiatives, if approached by combining deep techno-economical expertise, coupled with the experience from a wide range of industries, can provide attractive commercial opportunities for upstream and midstream operators. These projects whist meeting decarbonisation goals also make suitable candidates for emerging energy transition financing initiatives.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».