The choice of an optimized gauge length value is an essential part of the DAS data acquisition and processing. This parameter has a significant effect on the signal-to-noise ratio of the data and on resolution in the frequency domain. Dean et al. (2017) presented an approach which helps to maximize the signal-to-noise ratio while keeping interfering influences on the frequency content below a desired threshold value. By selecting an optimum gauge length GLopt (m), a favorable compromise between these two factors can be achieved, using 1GLopt=Rvfp, with R the gauge length / spatial wavelength ratio (–), v (apparent) acoustic velocity (m/s), and fp peak frequency (Hz).

The graphs presented in Fig. 2 show the dependence of signal-to-noise ratio and resulting wavelength on R for the conditions of the current survey. According to this, optimum conditions within the desired limits are found for R values between 0.46 and 0.56. For an intermediate R value of 0.5, an optimum gauge length of 39 m is calculated using Eq. (1), for a velocity of 4800 m/s, which has been extracted from the interval velocities derived for the Rotliegend reservoir interval (see Sect. 4.3), and a middle frequency of 61 Hz for the 10–112 Hz sweep. Therefore, the acquired DAS-VSP data were reprocessed accordingly using the derived optimum gauge length value. It would also be possible to apply a depth-dependent gauge length optimization, as suggested by Dean et al. (2017), by taking local variations of velocity and frequency content into account. This was nevertheless not performed in the current study, because the focus here is predominantly on the deeper Rotliegend reservoir section only.

An overview of the further seismic data processing steps is shown in Table 2. Seismic pre-processing included stacking and correlation with the pilot sweep. The hDVS output strain data were then transformed to strain rate by differentiation in time, resulting in a 90∘ phase shift. The strain-rate data are proportional to acceleration (Daley et al., 2016), and acceleration is in phase with the pilot sweep (Sallas, 1984).

Common-source gathers for zero-offset, intermediate, and far-offset source positions are displayed in Figs. 3 and 4. The common-source gathers are dominated by downgoing P-wave arrivals and arrivals of upgoing waves originating from several reflectors at different depths. For several shots, a strong tube wave arriving at later times is clearly visible as well.

Over several intervals along the wells, a coherent noise with a particular zigzag pattern can be recognized in the DAS data. Similar noise in DAS data recorded using cables suspended in boreholes has also been described in some earlier studies, e.g., by Miller et al. (2012), Yu et al. (2016), Cai et al. (2016), and Willis et al. (2019). It has also been found to occur for tubing-deployed cables, e.g., in the studies of Barberan et al. (2012) and Didraga (2015).

Several methods for elimination of this “ringing” noise, like spectral balancing, deconvolution, and time–frequency domain filtering (Elboth et al., 2008), were tested. For zero-offset data processing, we selected the Burg adaptive deconvolution (Griffiths et al., 1977). This method is a good compromise between computational effort, robustness, and application simplicity. A more thorough description of this ringing noise and further methods of noise suppression can be found in Martuganova et al. (2021). The filtered data sets are displayed in Figs. 3 and 4, together with the unfiltered data sets for comparison.

Usage of self-updating linear prediction operators is the foundation of the Burg adaptive deconvolution method. The designed filter operator is different at each trace sample. A set of filter coefficients is convolved with the data in order to predict the future data values at some prediction distance. Coefficient values are recomputed for each data sample in the seismic record with the criterion of minimizing the root-mean-square error. The computations are performed in forward and reverse directions in the time domain.

The application of Burg adaptive deconvolution resulted in a significant reduction of the coherent ringing noise (Figs. 3 and 4). After filtering, reflections are better visible and sharpened. Nevertheless, not all parts of the noise can be suppressed, especially in a short time window after the first-break arrivals. This residual noise is difficult to distinguish from upgoing reflected waves, as the velocity of the noise traveling along the cable is similar to the compressional velocity of the formation (Martuganova et al., 2021).

A comparison between the DAS strain-rate data and the vertical component of the borehole geophone acceleration data recorded at specific depths in the GrSk4 well is displayed in Fig. 5. Note that during recording of the check-shot data a sweep with 10–88 Hz was used, which is different from the recording of most of the other data during the survey.

The traces recorded at 1200 and 3600 m depth both contain direct P-wave arrivals, at 520 and 1090 ms, respectively. The DAS trace for 1200 m depth is strongly influenced by the ringing noise described above, which is confined to a narrow frequency band between 40 and 50 Hz (Fig. 5b) at this location. Similar noise characteristics have been observed, e.g., in the study of Chen et al. (2019). This noise is however not evident in the geophone data recorded at the same depth, which suggests that the ringing noise in the DAS data is related to the deployment method of the DAS sensor cable. While the DAS sensor cable is freely suspended inside the borehole, the geophone tool is clamped to the borehole wall. Closer analysis of the ringing noise shows that the sensor cable acts like a vibrating string within the affected intervals, with resonances occurring at a fundamental frequency and higher overtones (Didraga et al., 2015; Martuganova et al., 2021).

The traces recorded at 3600 m depth (Fig. 5e) also contain strong reflected waves, which arrive at around 1190 ms and originate from the base of Zechstein reflectors, at around 3850 m depth (see Fig. 4). Overall, the DAS strain-rate data exhibit a high similarity to the geophone measurements, except for the upgoing reflections. Here, the DAS strain-rate data display the opposite polarity as the geophone data. This polarity reversal for reflected upgoing waves has also been observed in previous studies, e.g., by Hartog et al. (2014), Mateeva et al. (2014), or Willis et al. (2016). Frignet and Hartog (2014) note that such a polarity flip compared to geophone data is similar to the characteristics of hydrophone sensors.

As a test, we have also converted the DAS data to geophone-equivalent acceleration data using the method described by Egorov et al. (2018). For this, we performed a transformation of the original DAS strain data into acceleration via filter application in the vertical wavenumber domain (kz) and further double differentiation in the time domain. The results for the check-shot traces recorded at 2400 and 3600 m depth are shown in Fig. 6. After conversion to acceleration, the DAS data display the same polarity as the geophone data, also for the upgoing reflections. This is in line with previous results obtained by Correa et al. (2017).

Common-source gathers recorded with different amounts of cable slack in well GrSk3 are displayed in Fig. 7. There is a zone with decreased amplitude of the first-break signal at the bottom of the well, which increases in length with increasing amount of cable slack. While the random noise is similar, leading to an overall signal-to-noise ratio (SNR) drop within the affected zone, the coherent noise is changing. For the recordings with 1, 5, and 11 m cable slack, a zone with ringing noise is visible at a depth of approximately 2890 m MD. This zone almost disappears in the 20 m cable slack data set, where the zone of decreased first-break amplitudes is approximately approaching the same depth. So ringing noise seems to be reduced within the affected zone, likely because of improved mechanical coupling of the cable to the borehole wall. But at the same time, the signal amplitude is significantly reduced within the affected zone as well.

Due to the higher first-break amplitudes, the best signal quality overall was assigned to the data set recorded with 1 m cable slack, i.e., under almost full cable tension, and further recording was performed like this. Notably, the best seismic record had been found to be recorded under the opposite conditions with released cable tension during the field trial reported by Frignet and Hartog (2014). Nevertheless, in their study, the optical wireline cable had been deployed in a relatively shallow well of 625 m depth, and the borehole conditions might not be representative of deep wells as in the current study for the Groß Schönebeck case. Constantinou et al. (2016) observed a behavior similar to the current study during a field trial in a well of 2580 m depth at the Rittershoffen site in France. Here, the zone of reduced signal amplitudes was found to coincide with a region where the cable was interpreted to form a spiral, gradually building up from the bottom of the well when additional cable slack had been introduced. Schilke et al. (2016) investigated the effect of cable slack on the mechanical coupling of a sensor cable deployed in a vertical well using numerical simulations.

For data quality evaluation, the SNR for each trace of the data set was calculated. The energies of signal and noise were computed as the root mean square (rms) amplitude (in arbitrary units) within time windows of -10 to +30 ms around the first arrival and 150 ms at the beginning of the trace before the first arrival, respectively. The signal-to-noise ratio was then calculated in dB using the following formula: 2SNR=20log10rmssignalrmsnoise.

The calculated SNRs are displayed in Figs. 8 and 9. The data are sorted for the different acquisition days and with increasing source offset. Each vertical column represents a source location, and the calculated SNR for each trace is color coded. Altogether, the data have a good SNR, with average values of approximately 40 to 50 dB at a depth region around 1000 m for the smaller offset source locations, decreasing to approximately 4 to 10 dB at around 4200 m close to the final depth. There is an overall decrease of the SNR with increasing channel depth and source offset, which corresponds to the decay of signal amplitudes to be expected due to spherical divergence of the acoustic waves. The data for the first acquisition day have similar characteristics for both wells, with slightly larger SNRs for well GrSk4.

From the start of the second acquisition day, a sharp drop of the SNR is evident in the data recorded in GrSk3 at a depth of approximately 3400 m. In addition, there are further intervals with decreased SNR at depths of approximately 3100 and 2600–2800 m. Curiously, the SNR for the channels below 3400 m gradually recovers again with increasing depth, until even improved SNRs in comparison to the first acquisition day are reached in the bottom interval.

The observed signal drop at 3400 m after day 1 seems to be similar to the effect of reduced signal amplitudes observed during the slack test. Nevertheless, the configuration of the wireline cable remained unchanged between day 1 and day 2. Accidental introduction of additional cable slack during this time, e.g., by slipping of the wireline winch, or movement of the crane arm holding the cable sheave, can be excluded, as the position of the cables was carefully monitored by placing marks on them after running into the hole. Furthermore, no significant change of the wireline cable tension at surface has been registered between day 1 and day 2. Other causes must therefore be responsible for the observed effect.

Combined with the remaining coherent noise after filtering, there is a significant heterogeneity in the data, which requires to carefully select the data to be considered during evaluation and interpretation.

For every source point, the travel times of the direct downgoing waves were determined by picking of the first-break times (Table 2). A velocity model has been set up based on the geometry of the existing geological model from Moeck et al. (2009) and by calibrating the model velocities with the picked travel times. Vertical travel times have then been determined by ray tracing through the calibrated model.

For the VP 10 zero-offset position, VSP interval velocities along the wells have been calculated from the travel times using the method of smooth inversion after Lizarralde and Swift (1999). Here, a damped least-squares inversion of VSP travel times is applied, which reduces the influence of arrival-time picking errors for closely spaced sampling points and seeks to result in a smooth velocity/depth profile. In our study, a 1.1 ms residual of the travel times has been allowed for. The calculated VSP interval velocities vary between about 2.8 and 5 km s-1 (Fig. 10). Variations within the VSP interval velocity profile show a good correlation to stratigraphy and the dominant lithologies. Taking into account the desired smoothing of the profiles resulting from the applied computation method, the VSP interval velocities agree well with the compressional velocities from the sonic log only recorded in the lower part of the GrSk3 well.

Further processing steps applied to the data from the VP 10 zero-offset position included separation of up- and downgoing wavefields, deconvolution, and transformation to two-way travel time (Table 2). After this, reflections are aligned horizontally, and vertical reflection profiles were generated by stacking of the separated upgoing wavefield data over a defined time window after the first arrival (corridor stack). Because of the recording characteristics of the DAS data (see Sect. 4.1), the polarity of the upgoing wavefield data has been reversed (see Table 2) in order to match the polarity convention of conventional geophone data. The polarity convention of the data is European or European Association of Geoscientists and Engineers (EAGE) normal; i.e., a negative amplitude value (trough) corresponds to an increase in acoustic impedance downwards (Simm and White, 2002).

Corridor stacks for GrSk3 and GrSk4 are displayed in Fig. 10. The recorded reflections are accurately correlated to depth and can therefore directly be assigned to lithology and other borehole data. The most prominent reflection events within the corridor stacks occur at the base (reflectors Z1, Z2, Z3) and top (reflectors X1, X2, X3) of the Upper Permian (Zechstein) and within the Middle Triassic (Buntsandstein; reflectors S1, S2).

Larger differences between the corridor stacks are mostly related to intervals where the reflection data are disturbed by residual ringing noise. The slope of this residual noise in the common-source gathers is similar to the slope of reflected upgoing waves, leading to positive superpositions and enhancements in the corridor stack which cannot be distinguished from real reflection events.

In the eastern part of the North German Basin, the deepest seismic reflections that can be readily recognized and correlated are at or close to the base of the Zechstein. The reflecting interface Z1 is at the boundary between the Stassfurt salt and the underlying Stassfurt anhydrite (“Basalanhydrit”). This “base Zechstein” reflector is used as a marker horizon over the entire Southern Permian Basin area (Doornenbal et al., 2010).

At Groß Schönebeck, the base of the Zechstein is comprised of an 80–90 m thick sequence of anhydrite, salt, and carbonate layers, which is underlain by the sediments of the Rotliegend. This interlayered sequence of strata with high impedance contrasts gives rise to several strong and closely spaced reflection bands, which mark the base of the Zechstein in the corridor stacks (see Figs. 10 and 11).

Reflections within the underlying Rotliegend interval are evident as well, which can now be assigned to individual sections of the reservoir. The corridor stacks for the Rotliegend reservoir interval are shown in Fig. 11, together with well logs and lithology data for both wells. Some of the well logs are unfortunately not available for the lower parts of the wells, especially for GrSk4. Acoustic impedance has been calculated as the product of bulk density and sonic velocity.

The Lower Rotliegend is formed by andesitic volcanic rocks of the Altmark Formation. At the depth of the possible top of the Carboniferous (reflector R8), which was postulated at 4216 m TVDSS for the GrSk3 well, no distinct reflection event is evident in both wells. This is consistent with other regions in the North German Basin, where the base of the Rotliegend series is essentially non-reflective (Guterch et al., 2010). The transition to the overlying Upper Rotliegend sediments occurs at a depth of 4146 m TVDSS. The corridor stacks show a positive reflection around this depth (reflector H6), which nevertheless has a somewhat different character and a slight offset in depth of several meters for both wells.

The succession of the Upper Rotliegend sediments starts with the Mirow Formation, in which conglomerates are the dominant lithology. The clasts of the rock matrix are lithic fragments of the underlying volcanic rocks. The sediments of the overlying Elbe subgroup are of fluvial and eolian facies (Gast et al., 1998). Within the lower part, sandstones with good reservoir properties, i.e., high porosities and permeabilities, are occurring within the Dethlingen Formation. Within the wells, the Dethlingen sandstones, which are also known as the Elbe base sandstone, occur as a continuous interval with a thickness of about 100 m, approximately between a depth of about 4000 and 4100 m TVDSS. This interval is characterized by low gamma-ray values, bulk densities, and sonic velocities, which correspond to a low shale content and increased porosity. It is furthermore marked by a crossover of the bulk density and neutron porosity curves. Bauer et al. (2019) presented an approach to map the distribution and properties of this sandstone layer based on analysis of seismic attributes of the 3-D surface seismic volume.

Above the Dethlingen sandstones, a succession of siltstones and mudstones follows. The transition is marked by a change in the log response, with higher values for the gamma-ray, density, and sonic velocity readings, and a separation of the bulk density and neutron porosity curves. This change in density and velocity corresponds to an overall increase of acoustic impedance. The top of the sandstone interval correlates with a positive amplitude event at a depth of 4010 m TVDSS in both profiles (reflector R3). In GrSk4, another reflection (peak) occurs about 40 m below at a depth of 4051 m TVDSS, which correlates with a step-like decrease of both gamma-ray intensity and sonic velocity. This local change of the log response is not as evident in GrSk3, where only a very weak reflection event occurs at this depth.

The upper interval of the Rotliegend sediments is comprised of an interlayered sequence of siltstones and silty mudstones (Hannover Formation), with local occurrences of thin-bedded sandy layers. The succession of lithological units and interbeds differs between both wells, which is also reflected by the different character of the corridor stacks within this interval.

The different characteristics of the corridor stacks in the Upper Rotliegend are explained by lithological changes between the wells. Within the bottom part, the well trajectories have a horizontal distance of up to 475 m, and such lateral changes in lithology are typical for fluvial sediments. The observed character of the reflectors, with low reflectivity and lateral variability, is in line with other regions in the North German Basin, where deeper reflectors within the Rotliegend or Carboniferous commonly cannot be correlated over long distances, because they are of poor quality and often interrupted (Reinhardt, 1993).