Pipeline Routing and Burial Depth Analysis Using GIS Software
Notice bibliographique
Résumé
Abstract Ice gouging of the seabed is a significant consideration for offshore pipelines in the arctic. A methodology is presented for using GIS (Geographical Information System) software for analyzing pipeline burial depth requirements for protection against ice gouging. This approach allows users to perform development concept evaluations, estimate pipeline burial depth requirements, test and optimize various routes and configurations, and generate estimates for pipe and trenching costs once the required input parameters are generated for a specific region, site or development. The framework and algorithms are described and ice gouge data from the public domain are used for demonstration purposes. The use of the LCP (Least Cost Path) algorithm (included in most GIS software) for pipeline route optimization is demonstrated. This subject is of relevance for any offshore development that requires trenching of pipelines in an ice-gouged seabed. Introduction A substantial portion of the world's petroleum reserves are believed to be in arctic offshore regions and other offshore ice- frequented environments. As the world's energy demand continues to increase, oil and gas developments in these environments will also increase accordingly. Consequently, more subsea pipelines will be constructed in these environments. Some of these pipelines (i.e. flowlines, subsea tiebacks and export pipelines) will be constructed in regions where the seabed is subjected to gouging by ice, requiring the evaluation of the magnitude of the risk, risk mitigation requirements and associated costs. The assessment of pipeline burial depth requirements for protection against ice gouging of the seabed is generally performed by specialists for a limited number of pipeline route options. However, the evaluation of offshore development options can involve the assessment of a number of field configurations and pipeline routing alternatives. The ability to determine pipeline burial requirements and costs, without reverting to specialists during iterations of the field development planning process, potentially represents a significant advantage in terms of time and cost savings. GIS (Geographical Information System) software is useful in the offshore design process, since various relevant data sets (i.e. bathymetry, sediment types, hazards, etc.) can be incorporated into the same platform. GIS software is being used fairly extensively in assessing pipeline routing and hazard assessment for applications on land, but similar offshore applications are not common. The procedure outlined here can be adapted to various regions, provided the required parameters are available. The analysis requires rasters defining ice gouge crossing rates and gouge depth parameters (as well as other parameters such as pipe response, failure criteria, etc.) to calculate the pipeline cover depth required to meet the target reliability, as well as cost values or functions to produce the associated cost rasters used as the basis for the LCP (Least Cost Path) analysis, which can assist in the selection of the optimum pipeline layout. While generating these rasters and incorporating the required algorithms would require the services of a specialist, the GIS tool can be readily employed by others who need to consider pipeline protection in the context of the overall development scenario(s). The final design would still be evaluated or checked by a specialist.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».