Closed-Loop Reservoir Management Using Nonlinear Model Predictive Control: A Field Case Study
Notice bibliographique
Résumé
Summary Closed-loop reservoir management (CLRM) consists of near-continuous data assimilation and real-time optimization to improve oil recovery and reservoir economics. In deep oil sands deposits using steam-assisted gravity drainage (SAGD) recovery process, CLRM involves real-time subcool (difference between actual and saturation temperature) control to develop the uniform steam chamber along the horizontal injector-producer well pair. Recently, model predictive control (MPC) has been implemented to maintain the optimal subcool; however, oversimplified models used in MPC are inadequate as reservoir dynamics in SAGD is highly complex, spatially distributed, and nonlinear. This provides an opportunity for the improved CLRM workflow which can incorporate the nonlinear physical/empirical models in MPC to represent the flow dynamics accurately over the reservoir lifecycle. In this research, two novel workflows, comprising linearization and nonlinear optimization are proposed to implement nonlinear model predictive control (NMPC) in CLRM of SAGD reservoirs. Linearization basically reduces an NMPC problem to linear MPC by estimating an equivalent linear model of a nonlinear black box model for a given input signal in a mean-square-error sense. Due to linear approximation, cost function in the MPC can be minimized using quadratic programming (QP) over the specified time horizon. Another approach is to use nonlinear dynamic models directly for accurate prediction of the plant states and/or outputs. Resulting nonconvex, nonlinear cost optimization problem is solved using interior-point algorithm at each control interval. Proposed workflows are tested using the history-matched, field-scale model of a SAGD reservoir located in northern Alberta, Canada. The horizontal well pair with dual-tubing string completion is segmented and subcool in each section is considered as an output variable while steam injection rates in both tubings and liquid production rate are the input variables of the NMPC controller. Bi-directional communication link was established between the controller and thermal reservoir simulator, acting as a virtual process plant. Qualitative and quantitative analysis of the results reveals that nonlinear black-box models can successfully capture the nonlinearity of the SAGD process in CLRM. Also, both workflows can control the subcool above desired set-point while ensuring the stable well operations. Furthermore, net-present-value (NPV) is increased by 24% when proposed NMPC workflows are used in CLRM as compared to the base case with no closed-loop control. Overall, NMPC can be successfully employed in CLRM of SAGD reservoirs for improved real-time subcool control, energy efficiency, and greenhouse gas emissions while satisfying the constraints offered by the surface facilities.
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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 | 0,000 |
| 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 ».