Notice bibliographique
Résumé
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 195951, “Case Study: Optimizing Eagle Ford Field Development Through a Fully Integrated Work Flow,” by Adrian Morales, SPE, Robert Holman, and Drew Nugent, Chesapeake Energy, et al., prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September-2 October. The paper has not been peer reviewed. An integrated project can take many forms, depending on available data, from a simple horizontally isotropic model with estimated hydraulic fracture geometries used for simple approximations to a large-scale seismic-to-simulation work flow. The complete paper presents a large-scale work flow designed to take a vast amount of data into consideration. The work flow can be scaled for projects of any size, depending on the data available. Introduction In 2017, Chesapeake Energy launched an investigation to evaluate ways of improving overall recoveries within the lower Eagle Ford. Two theoretical approaches were generated to optimize the company’s development plan: modification to current completion designs to achieve greater near-well fracture complexity and modification of targeting strategies to more-effectively drain the Eagle Ford interval. Methodology To evaluate these approaches, the company acquired multiple data sets to provide an integrated study. An already developing and productive area was selected in southwest Texas to examine completion design and targeting strategies while attaining a data set to allow for complex completion monitoring and reservoir simulations to aid in subsequent development optimization while maintaining at least type-curve production. Microseismic was acquired on three wells with multiple downhole arrays used to visualize how fracture geometries were affected by completion design changes. For quality control, data from ultrasonic image tools, cement-bond logs, and gyros were acquired to increase confidence in microseismic results. Time-lapse 2D lines were acquired pre- and post-hydraulic fracture to measure seismic changes induced by completions. Water- and oil-soluble tracers were run to determine hydraulic fracture extent and drainage footprint. Parent wells were instrumented with surface pressure gauges to characterize hydraulic fracture hits. With permanently installed fiber, a post-hydraulic fracture downhole camera was run to examine cluster efficiency per completion design. Core and quad-combo logs were taken in the area to analyze compositional similarities in oil signatures compared with produced oil and to calibrate petrophysical and geomechanical values. Oil samples were collected and analyzed to derive an equation of state for fluid characterization and reservoir simulation. Natural fracture characterization was performed to determine the pre-existing geological fabric of the rock using lateral electrical borehole images, a field outcrop study, and quad-combo and fracture-identification logs derived from drilling data. Multiple facture calibration tests were collected in the study area at different target intervals to calibrate vertical stress profiles and examine reservoir pressures. Lastly, following 1 year of production, a temporary rod-conveyed fiber-optic production log was run to determine cluster contribution based on completion design. The independent data sets were integrated on a common commercial software platform for geomodel creation, discrete natural fracture characterizations, hydraulic fracture simulations, and reservoir simulations. An integration strategy was developed to bring together the vast amount of data acquired. The work flow is a simplified representation of the data interdependencies and was used throughout the study. Only five data acquisitions are shown to overlap; however, any change in interpretation can lead to revision and iteration of several interdependent segments.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 |
| 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 ».