Lessons Learned from History-Matching the First Out-Of-Sequence Fracturing Field Test in North America
Notice bibliographique
Résumé
Abstract One of the considerations in Out-Of-Sequence Fracturing treatment is maximizing reservoir contact by creating fracture complexity through reducing or possibly eliminating or neutralizing the in-situ stress anisotropy (differential stress) to enhance hydraulic fracture conductivity and connectivity by activating planes of weakness (natural fractures, fissures, faults, cleats, etc.) within the formation in order to create secondary or branch fractures (induced stress-relief fractures) and connect them to the main bi-wing hydraulic fractures. In Out-Of-Sequence Fracturing, this is achieved by beginning fracturing Stage 1 at the toe of the well and then moving toward the heel and fracturing Stage 3 so that there is a degree of interference between the two fractures followed by placing Stage 2 between the previously fractured Stages 1 and 3. Out-Of-Sequence Fracturing in this mode ensures that fracture in Stage 2 (Centre Frac) takes advantage of the altered stress in the rock and connects to the stress-relief fractures from the previous Stages 1 and 3 (Outside Fracs), thus enhancing the connectivity and conductivity of the fracture network. Out-Of-Sequence Fracturing has already been tested successfully by LUKOIL Group in treating eight wells in Western Siberia in 2014. The first case of Out-Of-Sequence Fracturing in North America was later conducted in Western Canada in 2017. In this work, a three-dimensional hydraulic fracture extension simulator is rigorously calibrated by history-matching the observed treatment pressures and instantaneous shut-in pressures (ISIP) from the Out-Of-Sequence Fracturing field treatment in Western Canada in order to reliably quantify effective fracture geometries. Then, a separate set of fracture modeling is conducted to predict hydraulic fracture geometries in a conventional (Sequential Fracturing) treatment of the same candidate well. Finally, production forecasting is used to assess the production potential from the candidate well based on each set of the generated fracture geometries from each of the scenarios (Out-Of-Sequence Fracturing versus conventional Sequential Fracturing). The results of coupling rigorously calibrated fracture modeling and production forecasting indicate noticeable production uplift potential from the carefully designed Out-Of-Sequence Fracturing, with the realization that its success is sensitive to both treatment variables (stage spacing, well placement, treatment fluid viscosity and rate, and Centre Frac proppant size and tonnage) and formation's petrophysical and geomechanical properties (magnitude of stress anisotropy, Young's modulus, Poisson's ratio, process zone stress/net extension pressure, fracturing gradient, and matrix permeability). A carefully designed Out-Of-Sequence Fracturing should avoid excessive fracture complexity that impedes fracture growth due to pressure-out and screenout. This work is the first attempt in comparative evaluation of the impact of Out-Of-Sequence Fracturing incorporating the actual field data into fracture modeling coupled with production forecasting. The learnings from this multi-faceted study are worth sharing with the industry and could be used to guide future successful designs of the Out-Of-Sequence Fracturing for completion optimization in both unconventional and conventional reservoirs. From a large-scale field-development perspective, when conducted in multiple wells, optimized Out-Of-Sequence Fracturing has the potential of rendering full-length interference effect and optimizing the stress shadowing while reducing the risk of well bashing.
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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 | 0,002 | 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 | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».