Quantifying Fluid Prediction Using Angle-Dependent Inversion Measured Against Log Fluid Substitutions
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Notice bibliographique
Résumé
Abstract A Bright spot prospect was identified using 2D and 3D seismic. Due to the isolation of this prospect from existing infrastructure it is critical to be able to predict the type of hydrocarbon likely to be present. The approach taken to quantify hydrocarbon type included the following steps:reprocess a 2D seismic line which connects the well control to the prospect,use the well to model the response of different pore fluids in the reservoir quality sands,perform incident angle dependant inversions of the 2D seismic,statistically quantify and compare the results from the prospect with the model results,use the fluid probability at the prospect in the project risk assessment. We know that normal incidence seismic data is the response to the acoustic impedance, AI, of the geologic layers. However, at non-zero incident angles, the seismic data is the response to the elastic impedance1, EI, of the geologic layers. Because of the inherent pitfalls of using amplitude variation with offset2, AVO, a new approach was taken to quantify the fluid at the prospect. First, a pre-stack time migration of a 2D seismic line was created followed by near and far angle stacks. AI and EI volumes are then generated for the respective angle stacks. This process is called angle dependent inversion, ADI. AI and EI values are extracted for each CDP within the prospect. Log-based fluid substitution3,4,5 models are created to establish a range of AI and EI values for reservoir quality sands. Each model is displayed as a probability distribution function, PDF and compared to the extracted ADI values. The results indicate that it is unlikely that the prospect sand is brine filled. The most probable hydrocarbon in the prospect reservoir is gas. Introduction A deepwater Gulf of Mexico prospect was generated in which high amplitude seismic events were used to define the areal extent of a potential reservoir. The location of the prospect is over 30 miles from the nearest host platform. It was assumed that a single subsea completion would be used to develop a discovery. However, it was quickly realized that given the estimated volume of the reservoir and the tie back distance a gas discovery would be very commercial while an oil discovery would be equivalent to a dry hole. Trend analysis of the surrounding fields was not conclusive in predicting the most likely hydrocarbon type at the prospect. Therefore, a study was conducted in which modeled acoustic and elastic impedance for varying reservoir fluids were compared to extracted values from near and far angle seismic inversions in an attempt to predict the fluid type in the prospect. Although the prospect was mapped using a 3D data set it was concluded that a single 2D seismic line through the prospect would be sufficient to make the fluid prediction. A recent-vintage 2D line was selected that went through the center of the prospect and within 500 feet of our primary well control (Well 1).
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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 | 0,001 |
| 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 découle