Reservoir Ranking Map Sketching for Selection of Infill and Replacement Drilling Locations Using Machine Learning Technique
Notice bibliographique
Résumé
Abstract Infill and replacement drilling are effective ways to improve oil recovery as increasingly more wells are drilled in close proximity for fracturing. Presently, the approaches being employed are logging surveys, the moving window method, the rapid inversion method, and the customized type curve method. However, these methods are not suitable for reservoirs with high levels of heterogeneity in terms of geology, and require more expert knowledge and field survey, which can be time consuming and costly. Therefore, the present method developed is an economic and fast approach to determine infill and replacement drilling location from reservoir ranking maps generated in combination with machine learning methods. During this project, production data and reservoir parameters were gathered from an old oil field with more than 2,500 wells where most of the field was under water injection. Bubble maps were created for each reservoir parameter for a better visual representation of reservoir conditions. Then, after data cleansing and normalization procedures, the standout attributes were identified from all given reservoir parameters and production history and a reservoir ranking rule was set. Next, five types of classification approaches were used for prediction. This paper additionally presents a regression method, artificial neural network (ANN), to compare with the prediction results from classification. For each machine learning technique, a reserve ranking map was generated for this test field to predict future infill drilling and replacement drilling opportunities. Thus, with only geographic coordinates, the reserve ranking level was obtained. From cross-fold validation results, a quadratic support vector machine provides the highest prediction accuracy. From a practical standpoint, a decision tree offers a more realistic result. In addition to the ANN method outputs, the ranking result provides a smooth method between certain levels. This new approach of using artificial intelligence was used to provide the ranking level and ranking number to identify the best options for drilling the wells, which is different from the present traditional methods. This advanced reservoir ranking map allows operators to identify the best location for infill or replacement drilling. It can additionally help operators benefit from their previously gathered knowledge in a cost-effective way.
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Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».