A Semi-Analytical Forecasting Method for Unconventional Gas and Light Oil Wells: A Hybrid Approach for Addressing the Limitations of Existing Empirical and Analytical Methods
Notice bibliographique
Résumé
Abstract The rapid pace of exploitation of unconventional gas and light oil (UG/ULO) plays in North America has necessitated the development of new production forecasting methodologies to aid in reserves assessment, capital planning and field optimization. The generation of defendable forecasts is challenged not only by reservoir complexities but also by the use of multi-fractured horizontal wells (MFHWs) for development. In this work, we have developed a semi-analytical method that provides a solid theoretical basis for forecasting. The technique is analytical in that it uses the methods of Agarwal (2010) to calculate contacted oil- and gas-in-place (COIP/CGIP) from production rates, flowing pressures and fluid properties. The rate-normalized pressure (for liquids) or pseudopressure (for gas) derivative (RNP') is a key component of the calculation. The technique is also empirical in that an empirical function is fit to the resulting COIP/CGIP curve versus time. Although the method is flexible enough that any equation can be used to represent the COIP/CGIP curve, and hence the sequence of flow-regimes exhibited by MFHWs, the equation must be capable of being integrated to allow extraction of rate-normalized pressure or pseudopressure (RNP). The stabilized COIP/OGIP during boundary-dominated flow must be specified for forecasting – thereafter, the method uses a material balance simulator to model boundary-dominated flow. Hence, if the well is still in transient flow, a range in forecasts may be generated, depending on the assumed stabilized COIP/OGIP. Our new semi-analytical method addresses some of the current limitations of empirical and fully analytical (modeling) approaches. Empirical methods, which have been adapted to account for long transient and transitional flow periods associated with ultra-low permeability reservoirs, lack a theoretical basis, and therefore input parameters may be difficult to constrain. However, empirical methods are simple to apply and require a minimum amount of data for forecasting. Analytical models, while better representing the physics, nonetheless require additional reservoir and hydraulic fracture data which may not be available on every well in the field. The semi-analytical method proposed herein is intended to bridge the gap between empirical and modeling-based approaches – it is more rigorous than purely empirical methods, while requiring less data than fully analytical techniques. The new method is tested against simulated and field cases (tight oil and shale gas). Although we have used a simple power-law function to represent COIP/OGIP curve, which appears adequate for the cases studied, we note that wells exhibiting long transitional (e.g. elliptical/radial) will likely require a different functional form.
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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 | 0,001 | 0,001 |
| 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 ».