Far-Field Proppant Detection Using Electromagnetic Methods - Latest Field Results
Notice bibliographique
Résumé
Abstract Over 100 billion lbm of proppant are placed annually in wells across the globe, with the majority in unconventional reservoirs. The location of the proppant in these horizontal wells and formations is critical to understanding reservoir drainage, well spacing and stage spacing. However, for many years proppant detection has primarily been limited to near-wellbore measurements. A novel method to detect proppant in the far-field has been developed and is the subject of this paper. The method to detect proppant that has been developed utilizes electro-magnetic methods. This technology entails using a transmitter source and an array of electric- and magnetic-field sensors located at the surface. A current signal with a unique wave form and frequency is transmitted to the bottom of the wellbore via a standard electric line unit. In addition, an electrically conductive proppant is pumped into the stage(s) of interest. The electric- and magnetic-fields are measured both before and after the detectable proppant stages, and a novel analysis method is then employed to process and invert this differenced data to create an image of the propped reservoir volume. This technology is the product of years of development of computer models capable of forward modeling this technique. Once this modeling was completed, an initial field test was performed in west Texas, with a preliminary analysis of this work presented in a previous paper. Since that paper, however, additional processing of the data has yielded a much more detailed image of the proppant location in this Bone Springs well. In addition, a subsequent field application has been performed in a major basin in the Northeast US. Multiple stages received detectable proppant of varying stage volumes and the analysis has shown a detailed image of the proppant location in that wellbore also. In addition, the initial west Texas field test employed only electric-field sensors, while this latest test employed both electric- and magnetic-field receivers. Numerical simulations and field results indicate the percentage difference between pre- and post-frac results are two times higher using magnetic versus electric field sensors. This paper will review the technology development and methods, it will present the latest imaging from the initial west Texas test, and it will describe the latest learnings from the most recent field test. This paper should be beneficial to all completions and development personnel who are interested in knowing where proppant is located in their fractures. This technology has the potential to assist in understanding well drainage and spacing, stage and perf cluster spacing, vertical fracture coverage as well as the impact of fracture design changes.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».