Study of seismoacoustic effects in an producing oil horizontal well based on a fiber-optic cable sensor DAS

Ипатов, А И; Andrianovsky, A. V.; Gubarev, A. Yu.; Voronkevich, A. V.; Galimzyanov, R. M.; Solovyova, V. V.

doi:10.51890/2587-7399-2021-6-2-50-57

Cited by 2 publications

(2 citation statements)

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“…Having established the frequency spectrum of the useful signal, the processing flow in Figure 3 was adopted to achieve the overall goal of improving the SNR. The workflow was based on classical spectral data processing as well as a literature review [57][58][59]. The spectral picture of the full data section can be separated by origin, but after cutting off the entire data area that is not of interest (data outside the red rectangle from the Figure 1), only the spectrum of the target data interval is left (Figure 2b).…”

Section: Data Processingmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

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Microseismic Monitoring and Analysis Using Cutting-Edge Technology: A Key Enabler for Reservoir Characterization

et al. 2022

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Microseismic monitoring is a useful enabler for reservoir characterization without which the information on the effects of reservoir operations such as hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage would be obscured. This research provides a new breakthrough in the tracking of the reservoir fracture network and characterization by detecting the microseismic events and locating their sources in real-time during reservoir operations. The monitoring was conducted using fiber optic distributed acoustic sensors (DAS) and the data were analyzed by deep learning. The use of DAS for microseismic monitoring is a game changer due to its excellent temporal and spatial resolution as well as cost-effectiveness. The deep learning approach is well-suited to dealing in real-time with the large amounts of data recorded by DAS equipment due to its computational speed. Two convolutional neural network based models were evaluated and the best one was used to detect and locate microseismic events from the DAS recorded field microseismic data from the FORGE project in Milford, United States. The results indicate the capability of deep neural networks to simultaneously detect and locate microseismic events from the raw DAS measurements. The results showed a small percentage error. In addition to the high spatial and temporal resolution, fiber optic cables are durable and can be installed permanently in the field and be used for decades. They are also resistant to high pressure, can withstand considerably high temperature, and therefore can be used even during field operations such as a flooding or hydraulic fracture stimulation. Deep neural networks are very robust; need minimum data pre-processing, can handle large volumes of data, and are able to perform multiple computations in a time- and cost-effective way. Once trained, the network can be easily adopted to new conditions through transfer learning.

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Section: Data Processingmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

Microseismic Monitoring and Analysis Using Cutting-Edge Technology: A Key Enabler for Reservoir Characterization

et al. 2022

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Horizontal wellbore production profile evaluation by distributed fiber-optic temperature surveillance

Ипатов

Kremenetsky

Kaeshkov

et al. 2021

PROneftʹ, Prof. nefti

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The main goal of the paper is demonstration of permanent downhole long-term monitoring capabilities for oil and gas production profile along horizontal wellbore in case of natural flow. The informational basis of the results obtained is the data of long-term temperature and acoustic monitoring in the borehole using a distributed fiber-optic sensor (DTS + DAS). Materials and methods. At the same time, flowing bottom-hole pressure and surface rates were monitored at the well for rate transient analysis, as well as acoustic cross-well interference testing [1], based on the results of which “well-reservoir” system properties were evaluated, the cross-well reservoir properties of the were estimated, and the possibility of cross-well testing using downhole DTS-DAS equipment was justified. The research results made it possible to assess reliability of DTS-DAS long-term monitoring analysis results in case of multiphase inflow and multiphase wellbore content. In particular, DTS-DAS results was strongly affected by the phase segregation in the near-wellbore zone of the formation. Conclusions. In the process of study, the tasks of inflow profile for each fluid phase evaluation, as well as its changes during the well production, were solved. The reservoir intervals with dominantly gas production have been reliably revealed, and the distribution of production along the wellbore has been quantified for time periods at the start of production and after production stabilization.

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Study of seismoacoustic effects in an producing oil horizontal well based on a fiber-optic cable sensor DAS

Cited by 2 publications

References 1 publication

Microseismic Monitoring and Analysis Using Cutting-Edge Technology: A Key Enabler for Reservoir Characterization

Microseismic Monitoring and Analysis Using Cutting-Edge Technology: A Key Enabler for Reservoir Characterization

Horizontal wellbore production profile evaluation by distributed fiber-optic temperature surveillance

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