Refracturing Candidate Selection in Tight Oil Reservoirs Using Hybrid Analysis of Data and Physics Based Models
Notice bibliographique
Résumé
Abstract Refracturing candidate selection problems can be solved via production statistics, virtual intelligence and type-curve matching, and these methods are mostly developed using data-based models. They unleash great power of data but have not considered the influence of geological distributions in physics-based models. This paper combines the strengths of data and physics based models and proposes a hybrid analysis method to improve and strengthen the current methods. Three criteria, production performance, a completion index and a geological distribution around an offset well, and their sub-criteria are selected to build an evaluation system for refracturing candidate wells. Field data is collected and processed to calculate a completion index and production performance. To quantify a geological distribution around a well, a history-matched reservoir simulation model is required. Besides, a graph theory algorithm, Dijkstra’s shortest path, is used to quantify the influence of geological distributions in 3D reservoir models on wells. An analytic hierarchy process and grey correlation analysis are then used to establish a multi-level evaluation system and determine and rank each individual strategic factor. Finally, datapoints are shown in a 3D coordinate system, and custom defined weights are used to calculate the final ranking of potential refracturing wells. In addition, the hybrid analysis is presented on our self-developed visualization platform. A history-matched reservoir simulation model from the Y284 tight oil reservoir is used as a study case. Eight refractured wells’ data is collected and analyzed. As a grey correlation analysis result, a sub-criteron of productivity performance, relative productivity, ranks the first, followed by cumulative liquid production. Completion and resistance rank third and fourth with a small gap. Based on the analysis results, an evaluation system is built up. 14 refracturing candidate wells are analyzed and ranked using the evaluation system. These wells are displayed in a 3D coordinate system, where x, y and z directions represent three criteria separately. Wells distributed in the first quadrant are regarded as optimum candidates to apply refracturing treatments. Correlations of evaluation factors and increased oil production after refracturing treatment are plotted to validate the method. This study explores how to conduct hybrid analysis in a selection workflow of refracturing candidate wells. Combing visualization, interpretability, robust foundation and understanding of reservoir models with accuracy and efficiency, data-driven artificial intelligence algorithms, the experiences distilled, and insights gained from this project show great potential to apply hybrid analysis as well as modelling in oil and gas industry.
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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 | 0,000 | 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 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 ».