Automated Gas Lift System Optimization Through Combined Data Analytics and Wellbore Modeling Approach: A Case Study in an Offshore Middle East Oilfield
Notice bibliographique
Résumé
Abstract Systematic and comprehensive analysis of gas lift operations must include historical performance evaluation and heuristic diagnostics for identification of suboptimal performers. Reliable nodal analysis models are necessary to optimize the gas lift systems. Though some degree of automation is currently possible, this process still takes substantial effort and time. The work presented here is a streamlined, repeatable, and automated workflow to diagnose and optimize gas lift operation through analytics, reliable wellbore modeling and calibration that reduces the time from days to hours. The workflow is also very easily repeatable, which makes it very efficient to update when new data is available. The workflow combines the emerging data analytics efforts in the oil and gas industry along with traditional gas lift optimization guidelines to identify artificial lift-related key recovery obstacles and corresponding development plans. This novel combined approach proves the effectiveness and necessity of augmented artificial intelligence, and the case study of field analysis and execution further endorse the impact of such optimization workflow for gas lift assessment and operations. It builds from data-driven and engineering-based workflows that compute smart metrics and dashboards based on the historical well production and the gas lift systems performance. Several KPIs and metrics are utilized, some of which are industry standard while others are intuitively built exclusively for this workflow. In this paper, we present the results and challenges in applying this workflow to an offshore Middle East oilfield, with over 50 gas lift wells. As always with field data, several aspects of the data had to be addressed. Accuracy and relevancy of data was questionable. We developed several workflows to address the missing/incorrect data to ensure the robustness and credibility of the results from the workflow. Due to the practical implication of the analysis, stringent quality control and error margins were maintained throughout the analysis. No nodal analysis models were available, consequently models were built and calibrated as a part of this effort. Overall field and facility constraints were also incorporated to ensure results were achievable in the field. The optimized operational plan would yield an additional 15 to 20% gains over current production rate. Further benefits by upgrading the gas lift design are also quantified using the proposed workflow. The time required to construct, calibrate, and optimize around 50 wells was less than two weeks.
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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 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».