Seismic Applications of Downhole DAS

Lellouch, Ariel; Biondi, Biondo

doi:10.3390/s21092897

Cited by 40 publications

(21 citation statements)

References 140 publications

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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%

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%

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

et al. 2022

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“…Spacing between geophones (channel spacing) is usually more than 10 m (for some applications could be less), the length of the array is limited by a few hundred meters, and the tool is typically moved 6-8 times to cover the entire length of the well, which can be logistically difficult. To overcome these limitations, VSP acquisition based on distributed acoustic sensing (DAS) [2][3][4] is emerging as an alternative technology to acquire borehole seismic data. Moreover, DAS VSP can be acquired in cases when the conventional acquisition is very challenging or even not possible, e.g., in subsea umbilical cables [5] or during well stimulation [6,7].…”

Section: Introductionmentioning

confidence: 99%

Quantification of DAS VSP Quality: SNR vs. Log-Based Metrics

Titov

Kazei

Aldawood³

et al. 2022

Sensors

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The initial quantification of data quality is an important step in seismic data acquisition design, including the choice of sensing strategy. The signal-to-noise ratio (SNR) often drives the choice of distributed acoustic sensing (DAS) parameters in vertical seismic profiling (VSP). We compare this established approach for data quality assessment with metrics comparing DAS data products to available well logs. First, we create kinematic and dynamic data products derived from original seismic data, such as the interval velocity and amplitude of P-wave arrivals. Next, we quantify the quality of derived data products using well log data by calculating various statistical metrics. Using a large dataset of 220 different VSP experiments with a fixed source location and various DAS acquisition parameters, such as gauge length (GL), conveyance type, and lead-in length, we analyzed the statistical distribution of various metrics. The results indicate the decoupling between seismic-based and log-based metrics as well as between the quality of dynamic and kinematic data-products for the same record. Therefore, we propose using fit-for-purpose metrics to optimize the acquisition cost. In particular, for ray-based tomographic processing, it is sufficient to use traveltime-based metrics. On the other hand, for advanced dynamic analysis, amplitude-based metrics define the quality of final processing products. Hence, it is crucial to use fit-for-purpose metrics to optimize DAS VSP acquisition.

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“…Although the lower strain limit of φ-OTDR based DAS (φ-DAS) systems are extended to sub nε levels, the ceiling of the strain range of these systems is limited to several tens of µε due to the trade-off between the maximum sampling rate and the length of the sensing fiber [15,16] (detailed explanation of this trade-off is provided in Section 2). This limitation restricts the use of φ-DAS in applications that require measurement of high strain-rates over long distances such as analysis of large-magnitude earthquakes or evaluation of railway track behavior [3,17]. Although, a number of φ-DAS systems capable of measuring relatively large strain levels have recently been demonstrated, the sensing range in none of those studies were exceeding [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Long Range Raman-Amplified Distributed Acoustic Sensor Based on Spontaneous Brillouin Scattering for Large Strain Sensing

Gorajoobi

Masoudi

Brambilla

2022

Sensors

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A Brillouin distributed acoustic sensor (DAS) based on optical time-domain refractometry exhibiting a maximum detectible strain of 8.7 mε and a low signal fading is developed. Strain waves with frequencies of up to 120 Hz are measured with an accuracy of 12 με at a sampling rate of 1.2 kHz and a spatial resolution of 4 m over a sensing range of 8.5 km. The sensing range is further extended by using a modified inline Raman amplifier configuration. Using 80 ns Raman pump pulses, the signal-to-noise ratio is improved by 3.5 dB, while the accuracy of the measurement is enhanced by a factor of 2.5 to 62 με at the far-end of a 20 km fiber.

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Seismic Applications of Downhole DAS

Cited by 40 publications

References 140 publications

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

Quantification of DAS VSP Quality: SNR vs. Log-Based Metrics

Long Range Raman-Amplified Distributed Acoustic Sensor Based on Spontaneous Brillouin Scattering for Large Strain Sensing

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