Investigating Acoustic Anomalies in the Water Column. Alistair Robertshaw, Geohazards Specialist, BP Oceanology 2016

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1 Investigating Acoustic Anomalies in the Water Column Alistair Robertshaw, Geohazards Specialist, BP Oceanology 2016

2 Overview Water Column Data First Impressions Basic Theory Reflection Geometries Examples Published, Real-time and Processed Analysis Anomaly Characterisation Experimentation Calibration and Quantification Opportunities for Research and Development

3 Water Column Data - First Impressions Reflections above seabed Visually a powerful tool. Shows correlation with subsurface information Extremely sensitive Un-calibrated response Distribution of water column anomalies and their relationship to a subsurface channel Processing/filtering of data is labour intensive Selection of features is down to interpreter choice Can it be made consistent and quantitative? Natural gas migration and escape occurring along the edges of an impermeable clay channel

Geometrical Acquisition Effects Inline and cross-line reflection geometries observed in multibeam data: A frown is seen in the in-line (swaths) direction as the vessel sails over an object, moving

4 Geometrical Acquisition Effects Inline and cross-line reflection geometries observed in multibeam data: A frown is seen in the in-line (swaths) direction as the vessel sails over an object, moving progressively closer to it and then farther from it. A smile is seen in the cross-line (beam) direction, caused by side-lobe energy in the multibeam source signature. The shape and intensity of these features can be used to help characterise mid-water reflections. (Images from Marques and Clarke, 2012)

Published Data Examples Published water column data examples from an EK60 echosounder, typically used for fisheries research. Strong acoustic return from gas flare.

5 Published Data Examples Published water column data examples from an EK60 echosounder, typically used for fisheries research. Strong acoustic return from gas flare. Schools of fish and scattering layers also provide midwater returns. Can we relate these images to a seep s activity, volume or fluid type? Or, do we always require visual confirmation and measured flow rates to understand seep activity? (Images from Schneider et al 2011)

Consistent anomaly characteristics Deflected by currents 15 August

6 Real-time Acquisition Examples 09 August Observation 3 anomalies identified in the same location at separate times. Consistent anomaly characteristics Deflected by currents 15 August Interpretation Potentially a persistent fluid seep! 24 August Analysis What information could be derived: Fluid type? Flow rate? Volume?

Can be quantified by: Measuring the time taken for a container of known volume to

7 ROV Bubble Emission and Veracity Scales BP Standard seepage activity scale, based on ROV video observations. Description of fluid type and flow rate. Can be quantified by: Measuring the time taken for a container of known volume to fill. Geochemical analysis. But, this is a very localised measurement, requiring prolonged ROV operation.

8 Uncertainty from Acoustic Visualisation Level 5/100? Level 5/1? Multibeam measurements are more regional than ROV video, but the selection of display can have a bearing on how the information is perceived: The volume rendering, encompassing all anomalies after processing, can help identify locations for further study. The filtered fan display, from a single swath of the unprocessed data, is better for detailed analysis.

Characteristics of Fluid Seep Reflections Sail direction Sail direction

Reflections shallower in sail direction indicating a rising feature

reflections from moving features Seabed Seabed Rope and Buoy suspended

9 Characteristics of Fluid Seep Reflections Sail direction Sail direction Reflections symmetrical in sail direction indicating a static feature Reflections shallower in sail direction indicating a rising feature Side lobe reflections occur off stationary features No side lobe reflections from moving features Seabed Seabed Rope and Buoy suspended in the water Stack of beams Low rate bubble seepage Stack of beams

10 Modelled Reflection Shapes Models of in-line echosounder response to features in the water column Beams are narrow in the inline direction, so only part of the hyperbola is imaged Offset (m) Offset (m) Depth (m) Starting Depth (m) Depth (m) Starting Depth (m) Static Feature in Water Column Rising Bubble in Water Column

11 Calculation of Bubble Rise Rate The real data points fit the modelled hyperbola for: Vessel travelling at 2m/s Bubble rising at 0.33m/s But, this is not a unique solution - need to know the current velocity too.

22 m/s Data recorded while keeping the vessel dynamically positioned above an anomaly Fan display shows individual releases rising

12 Depth Stationary Recording Individual releases / bubble clusters Time 20 releases in 16 minutes = Every 48s Bubble rise rate, 22.5/100 = 0.22 m/s Data recorded while keeping the vessel dynamically positioned above an anomaly Fan display shows individual releases rising through the water column. Section display shows repeated releases, approximately every 50 seconds, and can be used to accurately measure a rise rate of 0.22 m/s, typical of gas bubbles. This data can also be viewed as a movie, watching the features rise in the water

13 Combined MBES & ROV Operation Simultaneous MBES and ROV survey used to successfully identify and locate a minor seep

14 ROV Acoustic and Video Bubble Counting A C B A) Bubble trajectories from HD video tracking B) Bright MBES targets attributed to individual bubbles C) Calculated bubble rise velocities from video and acoustic methods (All Figures from Moustier et al. 2013)

15 Approaches to Quantitative Analysis Reflection frown asymmetry is related to the relative velocities of: Vessel movement, water current, bubble rise path If the first two are measured (known) or eliminated, the bubble rise rate can be calculated easily from the MBES data. Bubble rise rate is related to buoyancy: Relative fluid densities, i.e. oil globule versus gas bubble Changes with depth due to expansion, separation and temperature effects! Reflection intensity (db) proportional to fluid density and velocity of sound. Gas = Strong reflection Fresh water or oil = Weak reflection? Can reliable time-lapse interpretations be made to monitor changes in seep activity? Considering the potential variability in: fluid seep process (temporal changes in volume, rate, direction, etc.) Acquisition conditions and Data processing

16 Conclusions Water column data acquired from a multibeam echosounder system is an efficient screening method for identifying potential fluid seeps or leaks over large areas. Currently, it is only indicative, with observations requiring visual confirmation via ROV. In particular, the high sensitivity of an acoustic system has the potential to raise false alarms. However, the data contains diagnostic information which when analysed appropriately can be used to more confidently characterise observed water column acoustic anomalies. Future potential exists for acoustic systems to be used as a stand alone method of characterising fluid seeps without the additional cost of mobilising an ROV vessel.

17 Acknowledgements I would like to thank: My colleagues across BP for their support in this work. Gardline Geosurvey for their dedication to the acquisition and processing of the water column data and their interest in trialling different MBES and ROV operations. QPS Fledermaus and FMMidwater as the tools for visualising and manipulating the different datasets.

BOTTOM MAPPING WITH EM1002 /EM300 /TOPAS Calibration of the Simrad EM300 and EM1002 Multibeam Echo Sounders in the Langryggene calibration area.

BOTTOM MAPPING WITH EM1002 /EM300 /TOPAS Calibration of the Simrad EM300 and EM1002 Multibeam Echo Sounders in the Langryggene calibration area. by Igor Kazantsev Haflidi Haflidason Asgeir Steinsland Introduction