Oilfield Technology - May/June 2020

DRIVING THE FUTURE

SEISMIC

EXPLORATION

ith the exponential increases in computing power

and the recent development of data-driven

processing technologies raising the bar for

quantitative imaging of complex subsurface geologies, acquiring

seismic datawith ocean bottomnodes (OBNs) offers the right

solution tomatch current industry expectations.

Addressing imagingchallenges

Building highly precise images of the subsurface requires

illumination of the geological detail. Howaccurate these images

are depends on the survey design and layout of both the seismic

sources generating the acoustic signals and the receivers

capturing the encoded information carried back fromthe

propagation of the seismicwavefield throughout the subsurface.

Each ‘pixel’ of the image is built by collecting all the

reflected energy produced at that location by the seismic

acquisition. To be able to evaluate this reflected energy, it

is first necessary to estimate the parameters characterising

the subsurface. This important imaging step is known as velocity

model building. Today, as a result of considerable progress in

computing power, it is possible to accurately invert the recorded

wavefields for a subsurfacemodel that is sufficiently detailed to

simulate data, which closelymatch the recorded seismic data.

This data-driven approach, known as full-waveform

inversion (FWI), goes beyond techniques that use only

the seismic data arrival times (traveltime kinematics), by

incorporatingmore information provided by the amplitude and

phase of the seismicwaveform. FWI is considered to be themost

promising data-driven tool for building velocitymodelswithout

the intrinsic bias of subjective structural interpretations. Many

successful applications of FWI have been reported to update the

model of shallowsediments, gas pockets andmud volcanoes

(e.g. offshore Brazil, the Gulf of Mexico and theNorth Sea). All

emphasise the need for datawith a superior signal-to-noise

ratio for the low frequencies, as the latter are critical for the FWI

modelling process.

Risto Siliqi, CGG, France,

explains how the largest multi-client ocean bottom

node survey in the UK Central North Sea will deliver unprecedented seismic

data quality to identify remaining reservoir potential.