On the use of microtremors for hydrocarbon detection A. Vesnaver 1,2, F. Da Col 3, M. Jervis 4, S. I. Kaka 1, D. Nieto 2 1 King Fahd University of Petroleum and Minerals, Earth Sciences Department, Dhahran (Saudi Arabia) 2 OGS – Italian National Institute for Oceanography and Applied Geophysics, Geophysics Section, Trieste (Italy) 3 Delft University, Civil Engineering and Geosciences Department, Delft (The Netherlands) 4 Saudi Aramco, Expec ARC, Dhahran (Saudi Arabia)

ABSTRACT The link of spectral anomalies of microtremors with underlying hydrocarbon reservoirs is very controversial, as field experiments support both positive and negative opinions, and there is not a solid theory supporting this work hypothesis. We conducted field tests in different sites, with and without oil and gas presence, to add new experimental data to the ongoing studies. Microtremor information may become repeatable (and so physically meaningful) only when the observation duration exceeds a few days, but even in this case factors as topography and active faults may severely bias the signal. Ocean waves impinging the coasts provide a natural background noise, which stands out clearly when the observation time exceeds a dozen of days, or so, in such a way that human noise is stacked out statistically over time. Microtremors recorded in (relatively) deep wells may provide useful information about ongoing production in a reservoir, as well as to link well data and seismic surveys, as their interferometric analysis can provide VSP-like information. INTRODUCTION The possible use of spectral properties of the ambient noise as a diagnostic tool for hydrocarbon exploration, proposed by Lambert et al. (2007, 2009) and Steiner et al. (2008) opened a lively debate in the geophysical literature, involving Green and Greenhalgh (2009, 2010), Lambert et al. (2010a), among others. Spectral anomalies in the frequency band between 1 and 6 Hz were presented as empirical tools for hydrocarbon detection in a few case histories (van Mastrigt and Al-Dulaijan 2008, Saenger et al. 2009). On the other hand, experiments that lead to opposite conclusions were presented by Hanssen and Bussat (2008), Berteussen et al. (2008) and Ali et al. (2010). Nieto et al. (2011) highlighted the low repeatability of these measurements, and in this paper we expand their study using new data. Beside the empirical observations, attempts were made to justify the claim linking spectral anomalies of surface data to hydrocarbon presence in underlying reservoirs. Frehner et al. (2009) tried to explain these anomalies by resonance effects in porous media, and Holzner et al. (2009) suggested the ocean waves impinging coastal areas as the energy source for such a resonance effect. However, Broadhead (2010) proved that these effects, if experimentally observable, might show up at much higher frequencies, i.e., over 300 Hz, using realistic rock and fluid properties in the proposed formulae. So far, there is not a solid theory, validated by experimental data, able to model low-frequency spectral anomalies from hydrocarbon reservoirs. The lack of a consistent theory to explain observations is not an obstacle, but a challenge for experimental studies. The time instability of microtremors’ signal does not imply that its information is invalid. There are at least two ways for improving this information extraction. The first one is trying

to separate the near-surface waves (including wind- and man-made noise) from those travelling across deeper Earth layers, as pursued by Lambert et al. (2010b), Riahi et al. (2011), Witten and Artman (2011) and Birkelo et al. (2012), among others. The second method relies on exploiting the potentially unlimited recording duration by stacking signals obtained using seismic interferometry. This idea has been around for a few decades (Claerbout 1968, Rickett and Claerbout 1999), but recently it matured in terms of theory (see, e.g., Schuster 2005, 2009, Bakulin and Calvert 2006, Korneev and Bakulin 2006, Halliday and Curtis 2010, Wapenaar et al. 2010a,b) and experimental validation (Gouédard et al. 2008, Duguid et al. 2011, Nakata et al. 2011, Ruigrok et al.2011). In this paper, we follow an intermediate way, analyzing the stability and reliability of microtremor signal attributes including polarization, spectral ratios and colour. We try to quantify these properties experimentally in different geological settings and noise conditions. We conclude that microtremors are not so useful for seismic exploration with surface receivers, but of possible use for reservoir monitoring with receivers in deep observation wells. POLARIZATION ANALYSIS Lambert et al. (2007, 2009) and van Mastrigt and Al-Dulaijan (2008) proposed linking spectral anomalies in surface seismic records to possible underlying reservoirs, with a straight vertical projection. This simple extrapolation implies assuming that most seismic energy is composed of body waves generated below the reservoir and travelling nearly vertically in a flat layered medium (Fig. 1, left). Only in that case, indeed, we do not need an accurate depth velocity model, as provided by expensive 3D seismic surveys calibrated by wells. Otherwise, in general cases with complex overburden and topography (Fig.1, right), the raypaths are bent and the signal scattered by the reservoir (if any) must be back-projected along the image rays (Hubral and Krey 1980) or by a full-waveform approach (Draganov et al. 2007, 2009, Witten and Artman 2011). Therefore, the simplest method mentioned above, based on spectral anomalies only, is inaccurate at least because it does not allow for Snell’s law.

The natural energy sources proposed for microtremors include remote earthquakes, ocean waves impinging the coasts and winds hitting vegetation (Zhang et al. 2009, among others). A classical paper by Peterson (1993) shows that a frequency peak is observed consistently in a range from 1 to 6 Hz at several seismological stations all over the world, in different climate and geological conditions. Ubiquitous phenomena may be not effective indicators of local petrophysical properties, as gravity and magnetic anomalies are, unless they are repeatable in time and linked to these properties at least by empirical laws. Thus, we studied some new data sets to understand microtremors better from an experimental point of view.

A first data set was acquired at Torrate (north-eastern Italy), at a test site of OGS (Italian National Institute of Oceanography and Applied Geophysics) for seismic and hydrological studies. It is located in a flat alluvial plane, where the water table depth is a few meters (Giustiniani et al. 2008, 2009). Nieto et al. (2011). Microtremors were recorded with three-component 1-Hz, 4.5-Hz and 10-Hz geophones for three weeks, computing the average polarization of seismic waves for different time intervals, from a few minutes to a full day. Variations in the low frequency response were relevant for intervals up to a few days, but became much smaller for time spans exceeding a week. An intriguing result was that the polarization direction of the low frequency signal, when averaged over the full observation period was towards the sea shore, about 50 km distant, along the median axis of the Adriatic Sea. This result was quite consistent with a similar experiment carried out by Berteussen et al. (2008) in the United Arab Emirates. Thus, we acquired and processed two further data sets: one from the Trieste station of the OGS Seismological Network of North-Eastern Italy (Working Group OASIS 2011), and another one by using a small array of receivers at the Torrate test site, where the

previous data was collected. The Trieste seismological station is placed in a relatively quiet environment, i.e., in the Giant Cave close to the OGS headquarters. Figure 2 shows the polarization directions averaged for 3 weeks in Trieste and in the past experiment in Torrate. In both cases the direction is orthogonal to the sea shore. Figure 3 displays the correlation between the azimuth and dip angles of the average polarization of microtremors in Torrate, with the wave size at four buoys in the Gulf of Trieste. We notice a fair correlation between the major peaks in the wave size and the microtremor energy.

Looking at these observations only, it seems that ocean waves play a key factor for the microtremor generation. However, such a conclusion was not consistent with our new experiment in Torrate. We deployed a linear array of 6 three-component receivers aligned in the SW-NE direction (Fig. 4, Table 1) and recorded the seismic noise for 8 days. We see in Figure 5 that the polarization azimuth and dip angles are very consistent among all stations except one (station 108), and is varying slowly in time. The distance between the receivers ranges from 200 to 500 m, so we expected a fair spatial coherency. However, the outlier station is consistent in time, and suggests that local factors play a relevant role, so reducing further the reliability of these measurements for characterization of deeper targets. Seasonal variations have been observed and studied in the past by Grevemeyer, Herber and Essen (2000), among others. They observed a good correlation between microtremor energy and ocean storms in Atlantic coastal areas and “reversed” such a link, using seismological records to estimate past meteorological conditions at sea. SPECTRAL COLOUR The most “popular” noise type is the white one. It is valued very much by theoreticians because its properties, flat spectrum, zero average and unitary dispersion, simplify several mathematical problems. It is an explicit assumption for predictive deconvolution, but an implicit one for stacking and migration. As these procedures have been the backbone of seismic data processing, working successfully for decades, there are no major complaints in the geophysical literature about such an assumption. The probable reason is that within the usual frequency band for seismic surveys (i.e., 10 and 100 Hz) it is an acceptable approximation. However, this is not the case when we try to recover and use signals in frequency bands below 10 Hz.

Figure 6 shows the amplitude spectrum of the vertical component of the seismic signal acquired in Torrate by a 1 Hz receiver. The plots include the power spectrum (blue line), but also the power spectrum multiplied by the frequency (red line). In the spectral jargon, the “colour” is assigned according to the power α of the frequency function f -α that best approximates the power spectrum of the signal. The conventional colors are white for α = 0, pink for α = 1 and red (or Brownian) for α = 2. The pink noise is typical of electronic instruments, and is also called flicker noise. Instead, in all our experiments described in this paper, we found that the spectral colour of microtremors is red, as the power spectrum trend approximates f -2 (i.e., the amplitude spectrum approximates f -1). The colour difference between instrument and ambient noise is a simple but effective tool for quality control of passive seismic surveys; if the data spectrum is not red, we may have an equipment problem. For example, in Figure 6 (right) we notice that deviations from this trend occur in terms of peak dispersion, when a car is passing in the vicinity of the receiver. This feature may be exploited for improving data quality by indicating which records are noisy and need to be removed. H/V RATIO AND OTHER ATTRIBUTES

The most popular microtremor attribute is the H/V ratio which is the ratio between spectra of the horizontal and vertical components of the three-component seismic records. A method was proposed by Nakamura (1989) and later by Lermo and Chavez-Garcia (1993) and Herak (2008) to characterize the near-surface layers, which is widely used in civil engineering and geotechnical surveys. It is widely adopted because of the low cost of the recording instruments, the use of free natural sources and the simplicity of the data processing. According to the literature, the record duration ranges from a few dozens of minutes to a few hours (Mucciarelli and Gallipoli 2001, Albarello and Lunedei 2010). According to our experience, however, a full day is required to get stable and repeatable results. Figures 7 and 8 display the H/V spectral ratios of the same stations, averaged for 1 hour and 1 day respectively. When considering 1 hour records (Fig.7), for the most “quiet” station (119), the oscillation does not exceed 20%, while others (stations 111, 116, 118) show major oscillations over a relatively stable “plateau”, and one station (115) is very irregular. As the distance between the stations is not relevant, being very small, and the geological setting is the same, these measurements seem definitely unstable, despite the care spent in the data acquisition. The Torrate test site is relatively quiet, but part of the microtremor energy recorded there comes from human activities. To reduce this noise and quantify the stability of the H/V ratio, we computed it for the data of the OGS Seismological Network of north-eastern Italy (Working Group OASIS, 2011). Among the several stations available, we selected a few located in different geological settings, from alluvial valleys to Alpine foothills and mountain tops (Fig. 9). Table 2 lists the stations’ details including coordinates, elevation and abbreviated name. The blue segments in Figure 9 show the azimuthal polarization angles averaged over time intervals of 1 hour. We notice very different features from place to place. Close to urban areas (stations DST2 and SABO, near Trieste and Gorizia), the azimuth dispersion is maximum, while in the foothills area (BALD, CGRP, VARN and POLC) it is aligned in an direction that is either aligned or orthogonal to the mountain range axis. The minimum dispersion is observed at the stations of Polcenigo and Fusea (POLC and FUSE), where the signal polarization angle is towards Gemona. This is a seismically active area where major earthquakes occur and active faults are present. This figure highlights that ongoing tectonic phenomena is a dominating factor (at least in this area) and that wave guides related to the topography may play a key role. When these factors become weaker, as in Torrate or Trieste, the ocean waves emerge as a background for long observation times. Figure 10 shows the H/V ratios for the seismological stations, averaged over time intervals of one hour for a few days of observations. We notice significant oscillations even at these locations, where the instrument coupling has been optimized to reduce site and seasonal effects. While several stations located in the mountain area (PRED, CLUD) show a stable trend, with oscillations not exceeding 50%, one displays quite regular peaks a 7 a.m. and 7 p.m. (FUSE), probably due to human activities. All others show irregular major peaks, probably related to natural seismicity. In any case, the observed oscillations in the H/V ratios are definitely large and mostly unpredictable. Figure 11 (top) shows the amplitude spectra at a few seismological stations and (bottom) the amplitude multiplied by the frequency, averaged for the full day. The lower plots show a flat trend, proving that the noise colour at all stations is red. This experimental evidence should be taken into account, when simulating the background noise contribution in synthetic seismic records. In general, white noise is added to synthetic data, probably because of its nice statistical properties and ease of modeling it. Instead, red noise should be used, which can be obtained from white noise using an appropriate filter. SURFACE OR BOREHOLE?

All Italian sites share a common property; they are located in areas without any known hydrocarbon reservoir. Thus, these experiments cannot yet exclude the possibility that spectral anomalies or other features of seismic signals might arise when a significant amount of oil and gas is present under the receivers. To improve further the experimental evidence, we studied microtremors in the Arabian Peninsula and processed data recorded over a major producing reservoir (Jervis and Dasgupta 2006, 2009, Menanno et al. 2012). A regular grid of 15x15 receivers was deployed near two water injection wells and recorded the ambient noise during several months, when the brine injection was on and off, to detect possible micro-earthquakes. The distance between two adjacent receivers is 200 m, both in the in-line and cross-line directions. Additional receivers were deployed in two wells: 8 in a shallow one, from 82 to 135 m depth, and 24 in a deeper one, ranging from 350 to 1065 m. Vesnaver et al. (2011) reported the dominant contribution of pumps at the injection wells as ground vibration sources, as the average polarization vectors were pointing towards their direction with a consistent and spatially coherent pattern. Figure 12 shows the dispersion over 24 hours of the polarization azimuth overlaid on a satellite image of the area. This dispersion is spatially organized and correlates fairly well with the mild variations in the elevation and soil properties of that area. This is likely due to the local amplification factor of the shallow layer, as the receivers are buried in shallow holes of about 4 m. Factors as wind and air waves are strongly attenuated by such a deployment, so most signal energy is due to surface waves. Plots like this one may be used for near-surface characterization and modeling, eventually in conjunction with other geophysical measurements via clustering (Vesnaver et al. 2009), to improve static corrections and imaging. The dominant contribution of surface waves becomes even more evident when analyzing the borehole data from that area (Fig. 13). The records from receivers in the shallow borehole (central column) and deep one (right column) were correlated with pilot records at different depths in the deeper well. The record length is 15 s, with a sampling interval of 2 ms. The correlations were stacked for a total recording time of 1 hour. The red lines across the seismograms show the expected time for the maximum correlation peaks according to the velocity model, obtained by well logs and seismic tomography (Menanno et al. 2012). We notice a clear alignment, nicely fitting the red lines, in the deeper part of the plots for the deep well (right column) for all 3 pilot records, ranging from the deepest receiver up to 850 m depth. At that point, the velocity curve changes its slope and the correlation peaks diminish. The records from the shallow well display some peaks aligned along the expected trend, but for the shallowest interval the data are too noisy to interpret, unless a velocity trend is known “a priori” and used as a reference.

We can draw a few conclusions from this experiment. First, the wavefield in the near surface is complex and cannot be approximated by impulsive waves in a kinematic model. The basic theory and the daily experience of seismologists predict that most energy recorded by surface receivers is due to surface waves (Miller and Pursey 1955, Landau and Lishitz 1987, among many others). We observe a vertical correlation exceeding the shallow borehole depth (135 m) but disappearing above a depth of about 850 m. This means that the shallow and deep receivers “see” two very different “worlds”, in terms of average microtremor characteristics. But the deep receivers seem particularly promising, because they provide information about the local velocity that is easily interpretable and consistent with the independent information from well logs and string shots. CONCLUSIONS Microtremors were claimed to be bearing information about possible underlying hydrocarbon reservoirs based on some empirical evidence from a few case histories. In this paper, we showed other case histories leading to the opposite conclusion. A key problem for using surface recorded microtremors is their limited repeatability. Fluctuations in the direction and strength of the dominant

wavefield at the surface requires very long recording duration, exceeding several hours and in some areas even a few days. However, this is rarely done in practice (Albarello and Lunedei 2010). Beside instabilities in time, microtremors are influenced by other factors such as local topography, cultural noise and natural tectonic activity, which can be compensated for only to a limited extent by some data processing and recording technology – (see also Bonnefoy-Claudet et al. 2006). The most appealing feature of microtremors shows up in deep borehole records, where most of the drawbacks mentioned above become negligible. The correlation of noise records with some (relatively) deep pilot provides a velocity function with a similar resolution to standard seismic surveys, so reducing up-scaling/down-scaling challenges from logs, or even possibly filling existing gaps in the data. Even more intriguing is the experimental evidence that a good seismic signal can be obtained by interferometry, if the involved receivers are not overwhelmed by the energy of surface waves. Thus, microtremors may provide useful information to the civil engineers (see, e.g., Okada 2003) with surface receivers, and to petroleum engineers with borehole receivers. In both cases, the observation time must be long enough to guarantee the signal repeatability.

In deeper boreholes, the surface wave contribution to the recorded signal becomes negligible. Thus we can cross-correlate and stack noise records for an adequate recording duration, converting the microtremors into virtual sources. As natural microtremors exist continuously, they may be used for time-lapse observations without the cost of an active seismic source. We remark that these conclusions do not apply to micro-earthquakes, i.e., impulsive events that may be interpreted individually, but to microtremors, consisting of long-duration sequences with spectral (or similar) properties bearing information about the underlying Earth. Recent observations of Das and Zoback (2011) suggest that non-impulsive seismic signals may be related to fault reactivation and friction. Further analysis of microtremors may add information that is missing in impulsive signals from active sources or micro-earthquakes. ACKNOWLEDGEMENTS We thank Giovanni Menanno (formerly at KFUPM) for his contribution to the borehole data processing, Luca Baradello (OGS) for the data acquisition in Torrate, Fabio Brunetti (OGS) for the oceanographic data, Enrico Priolo and the OASIS Working Group (OGS) for the seismological data, Elio Poggiagliolmi (Entec.it) for fruitful discussions, and Syed Rizwanullah Husseini and Qazi Sohail (KFUPM) for some technical work. Part of the seismic data was processed using Paradigm software. This work was partially supported by the Grant RG-1115-1-5 of the Deanship of Scientific Research at the King Fahd University of Petroleum and Minerals (Dhahran, Saudi Arabia). We thank Saudi Aramco for providing the borehole data from the Arabian Peninsula and its publication permission. REFERENCES Albarello D. and Lunedei E. 2010. Alternative interpretations of horizontal to vertical spectral ratios

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Tables

Table 1 UTM and relative coordinates of the temporary stations at the Torrate test site. Station name Short Elevation

(m) UTM

Easting UTM

Northing Receiver type

Acomizza ACOM 1715 667618.65 1494607.25 STS-2 G3 Monte Baldo BALD 1911 574660.42 1196016.66 Trillium 40 s Cima Grappa CGRP 1757 595933.25 1305108.72 STS-2 G3 Cludinico CLUD 635 658078.35 1424468.37 Trillium 120 s Trieste DST2 80 571195.62 1525848.88 STS-1 Fusea FUSE 520 653366.06 1437688.31 Trillium 40 s Polcenigo POLC 150 611549.78 1382115.27 STS-1 Predil PRED 902 656116.61 1500077.78 STS-2 G3 Monte Sabotino SABO 575 606811.18 1507432.15 STS-2 G3 Valmareno VARN 1265 608092.75 1338338.75 Trillium 40 s Zouf ZOU2 1895 669031.68 1434652.77 Trillium 120 s Table 2 Characteristics of Italian seismological stations used in Figure 10.

Station number

UTM Easting

UTM Northing

X (m) Y (m)

108 328553 5085207 881 1104

111 327672 5084103 0 0

115 328862 5085646 1190 1543

116 328047 5084319 375 216

118 328074 5084504 402 401

119 327878 5084304 206 201

Figure captions

Figure 1 Image rays across a reservoir with nearly vertical raypaths through a flat overburden (left) and bent raypaths through a complex structure (right).

Figure 2 Average microtremor polarization directions over a time span of 24 days at Torrate and Trieste (yellow arrows) and location of 4 oceanographic buoys (green dots), in the northern Adriatic Sea.

Figure 3 Average polarization azimuth and signal energy over a time span of 24 days (top) and wave height at the 3 buoys in Fig. 2, during the same period.

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Easting (m) Figure 4 Location map of the recording stations (squares) at the Torrate test site, aligned along a SW-NE direction.

Figure 5 Average polarization azimuth (left) and dip (right) at the stations in Figure 4 during 8 days, averaged for full day duration.

Figure 6 Power spectrum (cyan line), and power spectrum multiplied by the frequency (red line) for a standard record (left) and a record acquired when a car was passing in the receiver vicinity (right).

Figure 7 Averaged spectral ratio H/V between the horizontal and vertical component for the synchronous stations in Torrate during 4 days, for a record duration of 1 hour.

Figure 8 Spectral ratio H/V between the horizontal and vertical component for the synchronous stations in Torrate during 8 days, averaged over time intervals of 24 hours.

Figure 9 A few stations of the Seismological Network of north-eastern Italy. The vectors at the stations (yellow pins) show the azimuth of the average polarization for 1 hour intervals during 1 day only. The orange ellipse indicates an area with high natural seismicity.

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Figure 10 Spectral ratios H/V at the seismological stations in Figure 9, averaged over time intervals of 1 hour for a duration of 4 days. We notice significant variations even in these locations, where the instrument coupling is optimal.

Fig. 11 Amplitude spectra at some seismological stations (a) and the same spectra multiplied by the frequency (b), averaged for a recording period of a full day. These last resulting functions have a flat trend, indicating that the spectral color of the background noise is red.

Figure 12 Dispersion of the polarization azimuth in the Arabian Peninsula (top), superimposed as a transparency (center) to the satellite image of the area (bottom).

Figure 13 Correlation functions in the shallow well (central column) and in the deep well (right column) using a pilot signal from the deep well at depths of 350 m (top), 915 m (center) and 1065 m (bottom). The red lines show the expected correlation peaks in time, based on the velocity model obtained from well logs and seismic tomography.