Over recent years, many authors have proposed to compensate the absorption loss effects inside of the imaging process through the use of an attenuation model. This is necessary in the presence of strong attenuation anomalies. Q tomography has been developed for estimating this attenuation model but is generally limited to estimating attenuation in predefined anomaly areas. In this paper, we show how shallow gas pockets are revealed automatically by using a high-resolution volumetric Q tomography on the complex offshore Brunei dataset. A key component of our approach is the estimation of effective attenuation in pre-stack migrated domain through accurate picking of the frequency peak. Estimated Q-model is then used to compensate for absorption in the imaging process.
The Brunei region is considered as a complex area known for its gas escaping features over folded structures, producing shallow strong absorption anomalies. These strong anomalies seriously mask the coherency of the structure beneath.
Typically, the overall effect on the signal is that higher frequencies are dimmed more rapidly as the signal propagates through these very attenuating media. This results in a loss of signal resolution. Conversely, the attenuated signal carries additional information that can be useful in locating such gas pockets.
Measured attenuation can be compensated by applying processes such as the early techniques of inverse-Q filtering (Wang, 2002). More recently, stronger compensation due to gas or mud was included directly in the imaging process (Xie et al., 2009; Fletcher et al., 2012) through an interval Q model computed by tomography (Xin et al., 2008; Cavalca et al., 2011; Xin et al., 2014, Gamar et al., 2015). Generally, effective Q quantities are then inverted to produce a 3D interval Q model. The main purpose of tomography is to de-noise effective Q measurements in a model-consistent manner. Because the tomographic inverse problem is poorly constrained due to a difficult estimation of effective attenuation, a priori information is introduced to guide the inversion.
We present a robust workflow that uses Q tomography for converting dense inhomogeneous prestack effective Q measurements into a 3D model-consistent interval Q. To compute the effective Q volume in the pre-stack domain, we have used the method proposed by Zhang and Ulrych (2002) based on the shift of the frequency peak. Since the frequency peak (frequency at maximum amplitude) is very sensitive to the noise, we increase the signal/noise ratio by using the autocorrelation of the signal rather than the signal itself. This improves the resolution of the frequency peak value and thus the accuracy of effective Q estimation. We apply the workflow on Brunei offshore dataset to localize shallow gas pockets without any a priori information on their positions. This was made possible thanks to an adaptation to Q tomography of non-linear slope tomography (Guillaume et al., 2011) using an accurate effective Q volume picked from pre-stack migrated gathers.