Optimal Horizontal Well Placement with Deep-Learning-Based Production Forecast in Unconventional Assets
Notice bibliographique
Résumé
Abstract Horizontal wells placement and production forecast of unconventional assets play critical roles in the success or failure of any given operation. Traditional reservoir simulation workflows are ineffective for unconventional assets and often lead to erroneous results in addition to being both cost- and time-prohibitive. This paper presents a streamlined data-driven workflow of optimal horizontal target placement in unconventionals coupled with deep-learning (DL) techniques to accurately forecast production rates. The presented framework relies on automated geologic and engineering workflows to map remaining oil, advanced algorithms to perform an optimized global search with 3D pay tracking, and statistical and DL-based techniques to assess neighborhood performance and geologic risks. The workflow handles multiple types of constraints, including configuration constraints like length, azimuth, and deviation range, as well as path constraints like zone, baffle, and fault-surface crossing. For production forecasting, we developed an Encoder-Decoder Long Short-Term Memory Networks (LSTM) architecture. The model combines three types of inputs, and the output is multi-step multiphase forecasts. The input data for each well consists of time-variant information (i.e., historical production), static well features (e.g., geology and spacing parameters), and known-in-advance control variables. In addition, we use quantile regression to estimate the confidence interval around the forecasts. The post-prediction process then aggregates the results by combining economic analysis, risk assessment, and operational restrictions. We successfully deployed this technology to a giant unconventional play in North America with more than 4000 wells. We identified an inventory of 700 potential horizontal targets with optimized completion design; 90 of them were in the low-risk category with estimated additional reserves of 55.6 MMSTB. After establishing a database of tens of thousands of historical hydraulic fractures using advanced data mining techniques, we defined key impacting features using advanced feature engineering techniques (combining key fracture features as well as deconvoluting geological effects using unsupervised learning). We then developed a predictive DL model using selected features and quantified the impact of each feature on the production of each well. Moreover, the model generates a probabilistic production forecast that allows operators to model future activities. This technology provides a robust, streamlined, fast, and accurate approach to identifying optimal horizontal well targets as well as examining historical hydraulic fracturing performance, using state-of-the-art machine learning workflows augmented by domain expertise. It provides a domain-infused feature engineering process, absorbed by an explainable DL architecture. It uncovers non-linear dependences on well features and provides fast prediction and uncertainty quantification.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 |
| 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 ».