Technical Challenges for Offshore Heavy Oil Field Developments
Notice bibliographique
Résumé
Summary This paper discusses technical challenges and technology development opportunities associated with developing and producing offshore heavy oil (OHO) reservoirs, with emphasis on projects in cold or deep waters. The paper addresses how the reservoir and fluid characteristics will impact reservoir characterization, development concept selection, well construction, reservoir performance, artificial lift requirements, flow assurance, and operations. The applicability of common onshore heavy oil practices to OHO developments will be discussed. Emerging technologies and technology development opportunities will also be discussed. The material presented in this paper will be of particular interest to technology development personnel and asset team personnel who are in the appraisal or concept selection stages of a project; however, additional information is provided which may also be valuable later in the project life. Introduction Heavy oil reservoirs (those with API gravity < 20 deg, ? > 0.934) have been produced onshore successfully for decades in many basins around the world. There are also numerous examples of offshore heavy oil developments in shallow waters and gentle surface conditions, including those cited here from California (USA), Campeche (Mexico), Italy, and Brunei, respectively.1,2,3,4 Fields in shallow water but with harsh surface conditions have been developed or are still being evaluated in the North Sea.5 Recently, other OHO developments have been contemplated in cold water areas with sea ice (Jeanne d'Arc Basin, Canada) or in deep waters. With exploration efforts focused on deepwater basins such as those in West Africa and Brazil where heavy oil has already been found, significant additional heavy oil discoveries are likely. Heavy oil reservoirs tend to be low energy, low GOR systems with high viscosities and inferior crude properties. These projects tend to have low recovery efficiency and low productivity as compared to lighter oil reservoirs. OHO pro-jects typically have higher costs per unit volume of hydrocarbon (Capex and Opex). Investment requirements and operating challenges are even greater in cold or deep waters. These attributes combine to impact the entire value chain of OHO developments, from the appraisal process, through concept selection, development, operation, and even marketing of the oil. Technical advances are needed that can directly address the attributes described above, such as efficient recovery processes, enhanced productivity, reduced development costs, and improved crude value. In addition to the technical challenges associated with OHO, the larger investments and lower market values add a significant challenge to the economics of these offshore developments. Some OHO projects are moving forward but significant technical advances are needed to realize improved economic returns. Other OHO discoveries have been put on hold due to the combined technical and economic challenges. The technologies required to develop, produce, and market these reserves require the integration and optimization of skill sets from across the petroleum value chain, especially onshore heavy oil expertise and deepwater development skills. Identifying the key technical challenges and value drivers that are common to these projects, effectively developing and deploying technical solutions, and effectively learning from experiences on previous projects will be crucial for the success of future OHO projects.
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 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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,005 |
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 ».