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Enregistrement W2254160762 · doi:10.2118/179133-ms

Improving Understanding of Complex Fracture Geometry of the Canadian Horn River Shale Gas Using Unconventional Fracture Propagation Model in Multi-Staged Horizontal Wells

2016· article· en· W2254160762 sur OpenAlex

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Notice bibliographique

RevueSPE Hydraulic Fracturing Technology Conference · 2016
Typearticle
Langueen
DomaineEngineering
ThématiqueHydraulic Fracturing and Reservoir Analysis
Établissements canadiensSchlumberger (Canada)Nexen (Canada)
Organismes subventionnairesnon disponible
Mots-clésHydraulic fracturingFracture (geology)GeologyPetroleum engineeringUnconventional oilTight gasOil shaleMining engineeringGeotechnical engineering

Résumé

récupéré en direct d'OpenAlex

Abstract Hydraulic fracturing has become an important component of well completion in unconventional reservoir development and contributes to over 37% of the overall well construction spend. It has also, been seen as the most significant contributor to return on investment in unconventional reservoir exploitation. Until recently, field operation has been based on "trial and error" approach while modeling has been based hitherto on software used basically for the conventional reservoir fracture simulation. Hydraulic fracturing in shale gas reservoirs has often resulted in complex fracture network, as evidenced by microseismic monitoring. The nature and degree of fracture complexity must be clearly understood to optimize stimulation design and field development planning; completion strategy and operations planning. Unfortunately, the existing planar fracture models used in the industry today are not able to simulate complex fracture networks. A recently developed unconventional complex fracture propagation model (UFM) is able to simulate complex fracture network propagation in a formation with pre-existing natural fractures. Multiple fracture branches can propagate simultaneously and intersect, dilate or cross each other. This paper presents an integrated approach to optimize hydraulic fracture design by fully integrating all the data captured in the Canadian Horn River Shale. Based upon insight from the study, which was initiated by the operator and supported by the service provider, the operator could now make more informed design decisions and understand the interaction between the shale, the hydraulic and pre-existing natural fracture network, and reduce costs. The data incorporated into the study from both vertical and horizontal wells included geophysical, geological, petrophysical and geomechanical data integrated into a 3D earth model. Engineering data such as DFIT (measurement made from small volume of water pumped into target formation) derived fracture closure pressure, production and pressure data from the horizontal well in the pad were used for calibration and constraining of the model. A generation of 2D natural fracture network is also included in the paper by defining natural fracture parameters such as length, orientation, spacing, friction coefficient, cohesion, and toughness which are almost entirely validated using lab data and geomechanical interpretation. The complex hydraulic fracture simulation results calibrated with microseismic and fracturing treatment data were incorporated into numerical simulator and further calibrated with current production history of the candidate wells. The results of the hydraulic fracture, natural fracture and reservoir models were utilized to understand the fracture propagation mechanism in the Canadian Horn River shale gas formation. The prediction of the model (rates, cumulative and pressure) matched very rapidly and more closely with the observed production from the candidate well, improving confidence on the methodology utilized and results obtained. As a result of the project, the team is now able to run different hydraulic fracture design scenarios including stress shadow between stages validated using microseismic, stress shadow between offset wells, tuning factors not only on the geomechanics side but also in the treatment schedule and assess the impact that each key design parameter has over the candidate well's long term production using a numerical simulator with a unique gridding process. The result of the study also opened up new way of estimating the drainage area over a period of time and could be used when considering well spacing, placement and density during the field development planning. Based on these findings, the operator now have an insightful tool that could be used as the building block for future optimization of the fracture design.

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Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,359
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,038
Tête enseignante GPT0,239
Écart entre enseignants0,201 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle