Obtaining Reservoir Insights From Database On Infill/Step-Out Wells
Notice bibliographique
Résumé
Abstract The wealth of data on infill/step-out wells in the Western Canadian Sedimentary Basin (WCSB) implicitly includes clues to various reservoir characteristics, but techniques for evaluation were not obvious to us. We tried different ways of deriving meaningful insights and present three examples here. We compared the values of the average and the median performance parameters for specific populations of wells. Generally, the average rate was larger than the median. We also prepared frequency plots and their representation on the normal (standard deviation) scale for the wells in each group. From these plots, we were able to characterize the lateral heterogeneity of the reservoirs. These could also be used in analyses of risks in future projects. Risk analysis can also be aided by other features of frequency distributions. A rate-cumulative production plot was also helpful in determining acceleration and incremental reserves benefits resulting from infill drilling. Introduction Production performance of three Canadian pools with large numbers of infill/step-out wells were reviewed. One of these was a light oil carbonate reservoir in the Williston Basin. The other two pools were clastic reservoirs from the WCSB; one containing heavy oil (Medicine Hat Glauconitic C) and the other gas (Kirby U&L Mannville MU #1). A major objective was to show how a relatively simple statistical analysis of the performance curves could provide useful insights about the reservoirs, and also, to present a method to qualitatively/ quantitatively characterize the lateral heterogeneity as well as risks in developing similar pools by infill drilling. In addition, we also illustrate here how the oil rate-cumulative production plot could be used to identify contributions due to acceleration and incremental recovery. We illustrate the application of some of the ideas discussed in our previous papers(1–5). Methodology Our main data sources were the Alberta provincial oil and gas reserves publications. Production data were obtained from a commercial database. An in-house spreadsheet-based statistical program was used for analyzing/sorting the data and developing various statistical parameters, tables and graphs. The production data were collected for each well in these pools, drilled between 1980 and 2005–12. The initial rate of the median well was obtained by referencing the wells to their start-up (zero time). The initial rate was the average of rates for the first 2,000 hours. The choice of the first 2,000 hours, or 3 months, is purely arbitrary to ensure that the well had cleaned itself and achieved stabilized productivity. A rate-time plot for the first five years was prepared for the median wells. For example, see Figure 1a for vertical and horizontal infill wells in the Gainsborough Frobisher-Alida Pool. Similar plots were also generated for the average, the 25 and the 75 percentile wells. Cumulative production was also plotted as a function of standard deviation. Standard deviations around the mean were plotted on the x-axis (normal scale) and cumulative production on the y-axis (log scale). Most of these plots showed a fairly linear trend, reflecting a log-normal type distribution. The slope of the plot reflects the lateral heterogeneity of the reservoir(1).
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,012 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».