Direct Simulation of P10, P50 and P90 Reservoir Models
Notice bibliographique
Résumé
Abstract Reservoirs are heterogeneous and uncertain. Multiple realizations are an important aspect of uncertainty; however, parameter uncertainty, that is, uncertainty in the histogram is even more important for uncertainty in resources and reserves. Accounting for parameter uncertainty in geostatistical simulation is a longstanding problem. Targeting specific quantiles such as P10, P50 and P90 realizations is a related challenge. This paper presents a solution to these problems. A methodology is presented to simulate realizations of continuous variables with specified position in the range of global uncertainty. The key to the methodology is the use of the generalized linear distribution in place of the uniform distribution for simulation. The theoretical validity of this method is established, implementation details are discussed and examples are presented. This has a wide range of applicability in modern geostatistical reservoir modeling where global uncertainty is an important goal. Introduction Creating P10, P50 and P90 geostatistical reservoir models is an important task for flow simulation, risk analysis, reservoir forecasting and management. A base case model is always required. The 80% probability interval is common in the earth sciences. Higher probability intervals are often so large that they are difficult to use in risk qualified decision making. There are some statistical methods to establish P10 and P90 reserve figures. The conventional approaches to estimate the reserves are divided into deterministic and probabilistic methods. The deterministic approach consists of volumetric, material balance and decline curve analysis and they use a single value for each parameter for estimating the reserves, there are no P10, P50 and P90 values in this method. The probabilistic approach uses a full range of values for each parameter in the reserve calculation. For example, the volumetric method could use a distribution of values for porosity, initial water saturation, formation volume factor and so on to get a range of values for the reserve. For the purpose of reserve estimation, National Instrument 51–101 (NI 51–101) defines P10, P50 and P90 (ROBINSON et al, 2004). P90 refers to proved reserves, P50 refers to proved and probable reserves and finally P10 refers to proved, probable and possible reserves. Based on NI 51–101 definition, P90 is less than P50, and P50 is less than P10. In this paper, P10 refers to a p-value of 0.9 and P90 refers to a p-value of 0.1 (the p-values in this paper are defined base on the statistical definition of cumulative distribution function). The problem with conventional statistical methods is that there are no specific realizations. It is not possible to run a flow simulator and assess the dynamic performance of the models under different conditions. It is highly desirable to have specific realizations that approximately represent the 80% probability interval. The traditional geostatistical approach to finding P10 and P90 models is based on ranking procedures. Multiple realizations (often 100) are generated, and then some quick-to-calculate static reservoir attribute such as connected pore volume is chosen to rank the realizations. Realizations with specific position in the distribution of the static response are selected.
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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 | 0,000 |
| 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 ».