A GIS-Supported Remote Sensing Technology For Petroleum Exploration and Exploitation
Notice bibliographique
Résumé
Abstract This paper proposes a GIS-supported remote sensing (RS) technology for searching undiscovered oil and gas formations, and identifying potential petroleum exploration and exploitation targets. Remotely sensed surface lineament analysis, correlated with a variety of geoscience data, is applied for oil and gas exploration. A GIS technique is incorporated within the RS framework for manipulating the obtained data and improving effectiveness. This hybrid approach not only facilitates petroleum resources exploration, but also enhances the related spatial analyses, modelling studies, and systems planning. The method was applied to a case study in the Liaohe Oilfields in Liaoning Province, China, and reasonable and interesting outcomes have been generated. Introduction Many countries in the world continue to rely heavily on petroleum resources, due to rapid population growth and economic pressures. As a non-renewable energy resource with the nature of scarcity and depletion, petroleum exploration and exploitation has caused increased attention and concern for several decades since:distribution of petroleum deposits could be verified through exploration, andpetroleum supply may be increased to some extent by reasonable exploitation. Moreover, it seems quite evident that the period of massive discoveries of easily discovered oil and gas deposits had come to an end by the late 1960s(1). Since then, the exploration and development of petroleum resources have involved an enormous capital investment. Low efficiency exists in these processes due to limitations in technical and managerial effectiveness, especially in developing countries. This paper focusses on the provision of potentially more effective tools for petroleum exploration and exploitation. The process of petroleum exploration and exploitation requires the consideration, integration and updating of different types of information for creating a clear understanding of underground gas- and oil-bearing formations. Previously, the related information was collected, collated, and analysed through slow and painstaking manual methods(1). With the advent of aerial photography and satellite remote sensing, RS data has become a major source of the information required by decision-makers for petroleum exploration and exploitation. Meanwhile, various geological, geophysical, and geochemical data are now available from largescale geographical survey and laboratory analysis, with the provision of more detailed information about potential petroleum exploration targets. On the other hand, the remotely sensed data, together with other geoscience datasets, need to be properly managed, analysed, retrieved, displayed, and maintained. This could be accomplished through the introduction of geographic information systems (GIS) into the RS framework. In recent years, although remotely sensed data, together with geological and geophysical information, have been applied to many projects of petroleum exploration and exploitation(2, 3), few studies have been reported on integrated RS-GIS application. Extended from previous works, this study develops a RS-GIS framework to analyse correlations between remotely sensed lineaments data and subsurface geological and geophysical conditions for petroleum exploration and exploitation purposes. Its application to the Liaohe Oilfields in northeastern China is provided for demonstrating the applicability and effectiveness of this method.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| 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) | 0,002 | 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 ».