The assessment of undersea gas leakages from anthropogenic and natural sources is becoming increasingly important. This includes the detection of gas leaks and the quantification of gas flux. This has applications within oceanography (e.g. natural methane seeps) and the oil and gas industry (e.g. leaks from undersea gas pipelines, carbon capture and storage facilities). Gas escaping underwater can result in the formation of gas bubbles, and this leads to specific acoustic pressure fluctuations (sound) which can be analysed using passive acoustic systems. Such a technique offers the advantage of lower electrical power requirements for long term monitoring. It is common practice for researchers to identify single bubble injection events from time histories or time frequency representations of hydrophone data, and infer bubble sizes from the centre frequency of the emission. Such a technique is well suited for gas releases that represent low flow rates, and involving solitary bubble release. However, for larger events, with the overlapping of bubble acoustic emissions, the inability to discriminate each individual bubble injection event makes this approach inappropriate. In this study, an inverse method to quantify such release is used. The model is first outlined and following this its accuracy at different flow rate regimes is tested against experimental data collected from tests which took place in a large water tank. The direct measurements are compared to estimates inferred from acoustics.<br/

Berges, B.J.P.

Leighton, T.G.

Tomczyk, M.

White, P.R.

English

Southampton (e-Prints Soton)

 PASSIVE ACOUSTIC QUANTICATION OF GAS RELEASES Benoît J.P. Bergèsa*,   Timothy G. Leightona, Paul R. Whitea, Michal Tomczykb aInstitute of Sound and Vibration Research, Engineering and the Environment, University of Southampton, Highfield, Southampton, SO17 1BJ, UK  bMARUM, Center for Marine Environmental Sciences and Faculty of Geosciences; University of Bremen, Klagenfurter Straße, D-28359 Bremen, Germany *bjpb1e09@soton.ac.uk Abstract: The assessment of undersea gas leakages from anthropogenic and natural sources is becoming increasingly important. This includes the detection of gas leaks and the quantification of gas flux. This has applications within oceanography (e.g. natural methane seeps) and the oil and gas industry (e.g. leaks from undersea gas pipelines, carbon capture and storage facilities). Gas escaping underwater can result in the formation of gas bubbles, and this leads to specific acoustic pressure fluctuations (sound) which can be analysed using passive acoustic systems. Such a technique offers the advantage of lower electrical power requirements for long term monitoring. It is common practice for researchers to identify single bubble injection events from time histories or time frequency representations of hydrophone data, and infer bubble sizes from the centre frequency of the emission. Such a technique is well suited for gas releases that represent low flow rates, and involving solitary bubble release. However, for larger events, with the overlapping of bubble acoustic emissions, the inability to discriminate each individual bubble injection event makes this approach inappropriate. In this study, an inverse method to quantify such release is used. The model is first outlined and following this its accuracy at different flow rate regimes is tested against experimental data collected from tests which took place in a large water tank. The direct measurements are compared to estimates inferred from acoustics.  Keywords: methane seeps, inverse problem, passive acoustic UA2014 - 2nd International Conference and Exhibition on Underwater Acoustics177 1.  INTRODUCTION Assessing  levels  of  underwater  gas  leaks  from  anthropogenic  sources  (pipelines, Carbon Capture and Storage facilities, etc.) or natural sources (hydrocarbon gas release) is of importance because of their economic and polluting impacts. These releases can take the form of bubbles that present specific acoustic emissions [1,2]. For the purpose of quantification of gas release, passive acoustics are applicable and it presents advantages in term of cost and electrical power consumption. Leighton and Walton were the first to use such emissions to quantify gas bubble formation in the natural world [3], and now it is common  practice  to  determine  gas  volumes  by  relating  the  centre  frequency  of  the acoustic traces to the bubble sizes [4–6]. However, this approach requires distinguishing each bubble emissions and although signal processing techniques can help as the flow rate increases  [7],  in  essence  it  is  limited  to  releases  representing  low  flow  rates  [8].  The inversion scheme proposed by Leighton and White [9] is aimed at quantifying higher volume gas discharges, especially in the case where the acoustic emission of each single bubble overlaps with other bubbles. This paper draws on experimental data to assess the accuracy and applicability of the model. An experiment, which was carried out in a large water tank facility, is presented here. Metered amounts of gas were released from an arrangement of needles and the acoustic emissions were recorded. Gas was injected in two ways: 1) with a stationary flow rate at different regimes, and 2) with varying flow rates. In each case, based on the inverse model [9], gas flow rates are estimated and compared to direct measurements. 2.  EXPERIMENTAL PROCEDURES In a large water tank (8 m x 8 m x 5m deep) of volume   = 320 m , bubbles were released from an arrangement of needles (1.2 mm nozzle diameter) at the bottom of the enclosure. The bubble generation system was placed at large enough distances from the side walls so the reverberation has no effect on the damping and resonance frequency of the bubbles [10]. The gas used was nitrogen and the amount injected was controlled and metered  by  a  mass  flow  meter  (Bronkhorst  high-tech  in-flow  F-111 B I ) .  T h e  g a s  w a s  injected with flow rates kept steady at 15 discrete regimes and also with varying flow rates over a period of 200 seconds. The acoustic emissions of the bubbles were recorded using a SM2M+ hydrophone unit (wildlife  acoustics).  This  acoustic  recorder  consists  of  a  buoyant  body  containing  an embedded data acquisition board connected to a calibrated hydrophone and is powered by internal batteries. A schematic of the experiment is presented in Fig. 1. First, similarly to Bergès et al. [11], flow rates were kept steady at 15 regimes for 30 seconds. From the spectra of the acoustic emissions at different regimes, gas fluxes are estimated. In addition, in this gas injection case, at all regimes, signals were acquired at different distances from the bubble release site in order to investigate the effect reverberation has on the results. Second, a scenario is considered where the amount of gas is varied over a period of 200 seconds. In this case, flow rate rates are estimated every second. Signals are acquired at a sample frequency of 48 kHz. UA2014 - 2nd International Conference and Exhibition on Underwater Acoustics178   Fig. 1: Schematic of the experimental apparatus deployed in the water tank. It consisted on the acquisition of acoustic emissions of bubble release from controlled nitrogen gas injection using a calibrated hydrophone.  Using the method of Leighton and White [9], from the acoustic traces of the release of bubbles, the power spectral density  ( )  is calculated. At frequency   this relates to the distribution of bubble sizes in the form of bubble generation rates  (  ) as:  S(ω) =    (    )|  ( ,  )|       , (1)  with |  ( ,  )|  the predicted magnitude of the Fourier transform for a bubble of radius    at a frequency   [9]:  |  ( ,  )|  =                    × 4 (      )  +4      (      )  +4 (   −  )   (      )  +4 (   +  )   .  (2)  The quantity    is the density of the water,   the range from the bubble to the sensor,    the bubble natural frequency [1] and       the  total bubble damping coefficient [12,13]. One important unknown to consider is the initial amplitude of the bubble wall at the start of the emission     . This quantity can differ for example with different type of nozzles of bubble sizes. This is expressed relative to the equilibrium bubble radius and in this paper,     /   =3 . 7×1 0     is  used  following  the  procedure  of  reference  [9].  This  factor UA2014 - 2nd International Conference and Exhibition on Underwater Acoustics179 constitutes  the  biggest  source  of  uncertainty  and  its  relevance h a s  f o r  e x a m p l e  b e e n  discussed by Deane and Stokes [14] or Leighton and White [9]. In this study, focus is given to the effect due to the relative change in flow rates. From Eq. (1), the problem can be approximated at discrete frequencies and bubble radii and expressed in matrix form [15].  The  function   (  )  can  then  be  determined  in  different  bubble  size  bins. Furthermore,  as  the  problem  tends  to  be  ill-posed  because  of  measurement  errors, regularization in the form a Tikhonov regularisation is introduced. In matrix form, the regularised solution is as:   =(      +    )    ,  (3)  with the regularisation factor   chosen by the mean of a Generalised Cross Validation function including a positivity constraint on the distribution of bubble sizes  (  ) >0  [15]. The inversion gives a bubble count in terms of the radius the bubble would have if it was spherical (  ), since this is the parameter that governs the frequency of the sound emission [1,2]. Thus, it can be easily converted into flow rates assuming individual bubble volumes of 4    /3. This gives direct comparison to the measurements from the mass flow meter. The starting point of the inversion scheme is the acoustic trace of bubbles in free field condition. Because here the acoustic measurements were acquired in a water tank, the reverberation  of  the  chamber  results  in  increased  acoustic  energy.  It  is  important  to investigate  the  effect  this  has  on  the  inversion  scheme.  In  an  enclosure,  there  is  a combination of the direct and reverberant field and the averaged total rms pressure is given by [16,17]:          ( ) =     1   +1     , (4)  with  =    ×   the source output, the product of the range and the direct field pressure   . The quantity    is the radius of reverberation, corresponding to the distance where the reverberant and direct fields have equal contribution. This is given by [16,17]:     =     16 , (5)  with   = 55.3 ×  /     the Sabine coefficient being dependent on the sound speed in water  . The reverberation time in the enclosure is     = 181 ms between 0.8 kHz and 8 kHz (frequency band of interest in this study) and the directivity factor    is taken equal to 2, corresponding to an omnidirectional source lying on a reflective flat surface [16]. The radius of reverberation is    = 1.62 m in the conditions considered here. UA2014 - 2nd International Conference and Exhibition on Underwater Acoustics180 For the case of steady flow rates, measurements at 10 distances were repeated and the Sound Pressure Levels (SPL) were determined. The 10 data points at each regime are plotted against the distance from the source and a fitting based on Eq. (4) is performed. This is shown in Fig. 2 for regimes 10 and 15. The direct field decay can be observed at short ranges while the contribution of the reverberant field is noticeable at largest   (level stabilizing). Because it is needed to apply the inversion to data in free field, it is important to account for the effect of the reverberation field. To that purpose, the measurements at the shortest range (1 m) are used because at this distance, the direct field dominates over the reverberant field.   Fig. 2: Acoustic emissions from bubbles at different regimes recorded at 10 different distances. Black circle (regime 15) and grey cross (regime 10) markers are the measurements. Black (regime 15) and grey (regime 10) lines are the best fits using Eq. (4) 3.  RESULTS First, results for steady flow rates are presented in Fig. 3 (left axis). At the 15 regimes, the  spectra  from  the  bubble  emissions  are  inverted  to  obtain  bubble  emission  size distributions at discrete size bins. From these distributions, assuming spherical bubbles, flow rates can be inferred and compared to measurements from the mass flow meter. At the highest regimes, the results from the inversion scheme correlate with the metered flow rates. Between regimes 15 and 10, the flow rates are underestimated by 1.5 dB to 3.7 dB. Because the model described in Sec. 2 relies on the quantity      /   the agreement in absolute level could be improved by using a value that account for this specific bubble injection  system.    However,  independently  from  the  absolute  level  of  the  flow  rate estimates, it can be noticed that there is an agreement with the relative decrease. Between regimes 15 and 10, flow rates decrease by 35% and this is estimated to 57% from the acoustic emissions.  The inversion method is sensitive to noise and the signal to noise ratio (SNR) is shown in Fig. 3 (right axis, dashed line). Noise floor was measured when no gas was being UA2014 - 2nd International Conference and Exhibition on Underwater Acoustics181 injected and the ratio of measurements at each flow rates gives the SNR. The agreement of the acoustically inferred flow rates is seen to decrease with decreasing SNR. As SNR diminishes, the estimates fail to track the change in flow rate, this corresponds to regimes where the SNR is too low for the inversion to perform accurately.  Fig. 3: metered and acoustically inferred flow rates at different regimes. Left axis: mass flow meter measurements (straight line) and acoustic estimates (diamond markers). Right axis: SNR (dashed line) Second, results from varying flow rates are presented in Fig. 4. This compares metered and acoustically inferred flow rates when the injected gas was being varied. Estimates are computed by inverting the power spectral density of the acoustic traces for each 1 second window. It can be observed that the estimates track the changes in flow rate. Also, local fluctuations  of  the  flow  rates  inferred  from  acoustics  is  noticeable  and  is  due  to  the influence of the background noise that corrupts the bubble count when the signal spectra are inverted. This can be mitigated by applying a filter to smooth the results.   Fig. 4: Results from data collected during laboratory experiment (Fig. 1(a)). Comparison between metered (solid black line) and acoustically inferred (solid grey line) flow rates over 200 seconds of a varying release of nitrogen gas. UA2014 - 2nd International Conference and Exhibition on Underwater Acoustics182 4.  CONCLUSION In  this  study,  the  applicability  of  the  inversion  scheme  described  by  Leighton  and White [9] to quantify “high volume” gas leaks is demonstrated by the mean of results from a gas injection experiment in a large water tank. First, gas was injected and controlled using a mass flow meter at 15 flow rates regimes. Gas releases are estimated from the acoustic emissions and compared to metered results. A good agreement is found at high flow rates. The absolute level of the estimates is dependent on the quantity     /   that needs further research. The influence this factor has on the uncertainty of the estimated flow rates will be presented in separate work. Second, gas was injected and varied over a period of 200 seconds. Applying the inversion method every second allows tracking the changes in flow rates. 5.  ACKNOWLEDGEMENTS  The  studentship  of  Mr  Bergès  is  sponsored  by  Statoil  Ltd.  The  studentship  of  Mr. Tomczyk is sponsored by the SENSEnet project within an EU Framework 7 funded Marie Curie Initial Training Network. The authors thank Dr Trond Erland Bustnes of Statoil for providing guidance on industrial practicalities and requirements.   REFERENCES [1]  Leighton TG. The acoustic bubble. London: Academic Press; 1994. [2]  Minnaert M. On musical air-bubbles and the sounds of running water. Philos Mag Ser 7 1933;16:235–48. [3]  Leighton TG, Walton AJ. An experimental study of the sound emitted from gas bubbles in a liquid. Eur J Phys 1987;8:98–104. [4]  Greene CA, Wilson PS. Laboratory investigation of a passive acoustic method for measurement of underwater gas seep ebullition. J Acoust Soc Am 2012;131:EL61–EL66. [5]  Nikolovska  A,  Waldmann  C.  Passive  acoustic  quantification  of  underwater  gas seepage. Ocean. 2006, Boston, 18-21 Sept. 2006: IEEE-Institute of Electrical and Electronic Engineers; n.d., p. 1–6. [6]  Leifer I, Tang D. The acoustic signature of marine seep bubbles. J Acoust Soc Am 2007;121:EL35–EL40. [7]  Leighton TG, White PR, Schneider MF. The detection and dimension of bubble entrainment and comminution. J Acoust Soc Am 1998;103:1825–35. UA2014 - 2nd International Conference and Exhibition on Underwater Acoustics183 [8]  Leighton TG, Fagan KJ, Field JE. Acoustic and photographic studies of injected bubbles. Eur J Phys 1991;12:77–85. [9]  Leighton TG, White PR. Quantification of undersea gas leaks from carbon capture and storage facilities, from pipelines and from methane seeps, by their acoustic emissions. Proc R Soc a-Mathematical Phys Eng Sci 2012;468:485–510. [10]  Leighton TG, White PR, Morfey CL, Clarke JWL, Heald GJ, Dumbrell HA, et al. The  effect  of  reverberation  on  the  damping  of  bubbles.  J  Acoust S o c  A m  2002;112:1366–76. [11]  Bergès BJP, Leighton TG, White PR, Tomczyk M, Wright IC. Acoustic detection of seabed gas leaks, with application to carbon capture and storage (CCS), and leak prevention for the oil and gas industry: preliminary assessment of use of active and passive acoustic inversion for the quantification of underwater gas. Proc. 11th Eur. Conf. Underw. Acoust., Edinburgh, UK: 2012, p. 146–52. [12]  Ainslie  MA,  Leighton  TG.  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UA2014 - 2nd International Conference and Exhibition on Underwater Acoustics184

Acoustic and photographic studies of injected bubbles.

Acoustic detection of seabed gas leaks, with application to carbon capture and storage (CCS), and leak prevention for the oil and gas industry: preliminary assessment of use of active and passive acoustic inversion for the quantification of underwater gas.

Acoustics: an Introduction to Its Physical Principles and Applications.

An experimental study of the sound emitted from gas bubbles in a liquid.

Discrete Inverse Problems: Insight and Algorithms (Fundamentals of Algorithms). SIAM-Society for Industrial and Applied Mathematics;

Laboratory investigation of a passive acoustic method for measurement of underwater gas seep ebullition.

On musical air-bubbles and the sounds of running water.

Passive acoustic quantification of underwater gas seepage.

Quantification of undersea gas leaks from carbon capture and storage facilities, from pipelines and from methane seeps, by their acoustic emissions.

Review of scattering and extinction cross-sections, damping factors, and resonance frequencies of a spherical gas bubble.

The acoustic bubble.

The acoustic excitation of air bubbles fragmenting in sheared flow.

The acoustic signature of marine seep bubbles.

The detection and dimension of bubble entrainment and comminution.

The effect of reverberation on the damping of bubbles.

Thermal effects and damping mechanisms in the forced radial oscillations of gas bubbles in liquids.

Underwater acoustic power measurements in reverberant fields.

Passive acoustic quantification of gas releases

Passive acoustic quantification of gas releases

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