New Method Combines Simulation and Novel Spreadsheet Tools to Enable Direct Optimization of Expansion Decisions in a Giant Heavy-Oil Field
Notice bibliographique
Résumé
Summary Giant, geologically complex heavy-oil fields can take decades to develop, so development decisions made early in the life of the field can have long-range implications. Decision and risk analysis (D&RA) is often needed to make decisions that will maximize the risk-adjusted economic benefit. Unfortunately, in large fields, D&RA can be very challenging because of the large number of variables and the endless number of development and expansion scenarios to analyze. The time needed to complete a D&RA can become prohibitive when full-field reservoir simulation is the main tool for forecasting primary production and well count, with one simulation taking many hours or days to complete. This paper describes two new methods developed to overcome these challenges for a specific depletion-drive heavy-oil reservoir: a method for optimally populating a model with hundreds of horizontal wells, and a method to optimize expansion decisions quickly and directly. The utility of these tools has not been determined for other reservoirs and/or recovery mechanisms. A semiautomated spreadsheet-and-simulation method was developed to quickly place and select hundreds to thousands of hypothetical/future horizontal wells in a multimillion-gridblock model. Because the method automatically accounted for all model static properties and their effects on dynamic production response, the hypothetical wells had productivity characteristics very similar to the actual drilled wells placed in the model. A multivariate nonlinear interpolation method was developed that enabled full-field forecasts—for any combination of acreage allocation, well count, drilling order, and field rate constraint—to be calculated in less than 5 seconds, compared to approximately 20 hours for traditional simulation. Extensive validation work showed that well count and production curves from the spreadsheet virtually overlaid those obtained using traditional simulation of the particular expansion scenario. Such close agreement was possible because the basis of the spreadsheet forecast was utilization of traditional simulation forecasts from a handful of relevant cases. A key breakthrough beyond just fast forecasting was the coupling of the following three components inside the same spreadsheet: the fast forecasting method, calculation of an economic indicator/objective function (NPV), and commercial optimization tools. This linkage made possible, perhaps for the first time (at least at this scale), realization of direct optimization of any development scenario in a matter of minutes to a few hours, depending on the number of variables being optimized.
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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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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 ».