INNOVATIVE METHODOLOGY AND ALGORITHMS FOR SOLVING GEOPHYSICAL INVERSE PROBLEMS

The presence of shallow low velocity zone in the subsurface, which can severely affects
the seismic data quality has long been recognized as a significant problem in the
seismic data processing. Due to the complex wave propagation and anelastic losses, it
becomes more complicated and highly challenging and because of the complex faulting,
converted wave (birefringence anisotropy) play a significant role in characterization of
reservoir. The reflected events in such region appear with weak amplitude and lower
frequency content, which is often refers to as Q-attenuation or intrinsic loss. In offshore
peninsular Malaysia major hydrocarbon bearing fields which are affected by shallow
low velocity and therefore data quality in Malaysian basins often suffers from serious
wipeouts due to shallow gas or gas leaking from a deep reservoir.
The existing conventional inversion approach does not provide significant improvement
in low velocity imaging, causes serious wipeouts from a deep reservoir. This research
thesis objective is to develop innovative methodology and algorithm for inverse problem
in exploration geophysics. The proposed methodologies and algorithms are based
on full-waveform inversion and contribute to this largely unsolved problem. Analysis
of converted waves provide insight of anisotropy characteristics in and around the
reservoir.
In principle, full waveform inversion (FWI) is a data-driven strategy wave equation
based and highly non-linear approach. It is an iterative forward and inverse modeling
procedure that takes advantage of full information contained in recorded seismic data.
Using minimization of the differences between the observed and calculated simulated
data, FWI provide high resolution and accurate subsurface properties.