Automated Surveillance of Subsurface Wellbore Integrity in a Heavy Oil Field using Passive Seismic Systems
Notice bibliographique
Résumé
Abstract Continuous subsurface surveillance is important for heavy oil in-situ recovery processes where induced stresses in the overburden can compromise the integrity of the wellbores. Wellbore failure may lead to the undesirable loss of fluids into the overburden. In recent years, there has been a rapid growth in the use of Passive Seismic monitoring systems to aid in subsurface surveillance activities, with the ultimate goal of detecting potential integrity issues as early as possible. However, the massive volume of data recorded by these instruments is time-consuming and error-prone to process manually. This paper introduces EMMAA (ExxonMobil Microseismic Automated Analyzer), an automated workflow to reliably process continuous microseismic data, detect subsurface integrity issues, and ultimately reduce the latency in responding to wellbore integrity issues. A novel cloud-based technology for managing microseismic data is briefly described. The seismic waveforms, recorded by a distributed array of geophone receivers, are automatically analyzed to determine the type and source of subsurface disturbances (‘events’). First, novel frequency-domain and deep learning analyses are used to distinguish noisy signals from the seismic waveforms such as compressional and shear waves produced by the events. Next, the location of the event is calculated and its seismic attributes are computed. Finally, the type and severity of the seismic event are determined by an event classifier. The performance of the automated workflow is examined in the context of accurate detection of casing failures in a heavy oil Cyclic Steam Stimulation (CSS) application. The event features that distinguish casing breaks from other seismic events are described. It is shown that the methodology is able to achieve a high detection rate when back-tested against a historical data-set of known casing failures. False positives are adequately contained by preventing waveforms of electrical or mechanical noise from being processed. In a production environment, the event processing workflow is run on distributed servers and analyzes triggered seismic data in real-time. Depending on the severity of the microseismic events detected, operators are immediately alerted via email and text messages, so that remedial actions may be swiftly initiated. The utility of this integrated system is further exemplified by the massive reduction in the time taken to detect casing breaks—from up to 36 hours historically, down to less than one hour in most instances. Extensions of EMMAA that enable the detection of a wide variety of microseismic events are also discussed. These events include surface casing slips that occur at the casing shoe, cement de-bonding events near the wellbores, and events indicative of potential fluid migration in the overburden.
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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 | 0,000 | 0,001 |
| 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 | 0,000 | 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) | 0,001 | 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 ».