Investigating Acoustic Anomalies in the Water Column
Alistair Robertshaw, Geohazards Specialist, BP
Oceanology 2016
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
Water Column Data - First Impressions
• Reflections above seabed
Distribution of water column anomalies and their relationship to a
subsurface channel
• Visually a powerful tool.
• Shows correlation with subsurface
information
• Extremely sensitive
• Un-calibrated response
• 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 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.
• 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)
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!
Analysis
24 August
• What information could be derived:
− Fluid type?
− Flow rate?
− Volume?
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.
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 symmetrical in
sail direction indicating a
‘static’ feature
Reflections shallower in sail
direction indicating a rising
feature
No side lobe reflections from moving
features
Side lobe reflections occur off ‘stationary’
features
Seabed
Stack of beams 80-140
Rope and Buoy suspended in the water
Seabed
Stack of beams 100-140
Low rate bubble seepage
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)
Static Feature in Water Column
Starting Depth
(m)
Depth (m)
Rising Bubble in Water Column
Starting Depth
(m)
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.
Stationary Recording
Time
Individual releases / bubble
clusters
Depth
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
Combined MBES & ROV Operation
Simultaneous MBES and ROV survey used to successfully identify and locate a minor seep
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)
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
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.
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.