Automated Surveillance of Subsurface Wellbore Integrity in a Heavy Oil Field using Passive Seismic Systems
- Sri Venkata Tapovan Lolla (ExxonMobil Upstream Research Co) | Jeffrey Bailey (ExxonMobil Development Co) | Simona Costin (Imperial Oil Resources Ltd) | Michael Hons (Imperial Oil Resources Ltd) | Xinlong Liu (Imperial Oil Resources Ltd) | Helen Yam (Imperial Oil Resources Ltd) | Arslan Akhmetov (ExxonMobil Canada Properties) | Timothy Hayward (Imperial Oil Resources Ltd) | Colin Brisco (Imperial Oil Resources Ltd)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 30 September - 2 October, Calgary, Alberta, Canada
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Heavy Oil, Passive Seismic, Microseismic, Subsurface Surveillance, Monitoring
- 3 in the last 30 days
- 232 since 2007
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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.
|File Size||2 MB||Number of Pages||22|
Bailey, J. R., Smith, R. J., Keith, C. M., Searles, K. H., & Wang, L. (2008, January 1). Passive Seismic Data Management and Processing to Monitor Heavy Oil Steaming Operations. Society of Petroleum Engineers. doi: 10.2118/117484-MS
Warpinski, N. R., & Du, J. (2010, January 1). Source-Mechanism Studies on Microseismicity Induced by Hydraulic Fracturing. Society of Petroleum Engineers. doi: 10.2118/135254-MS