We N Depth Domain Inversion Case Study in Complex Subsalt Area

Transcription

1 We N Depth Domain Inversion Case Study in Complex Subsalt Area L.P. Letki* (Schlumberger), J. Tang (Schlumberger) & X. Du (Schlumberger) SUMMARY Geophysical reservoir characterisation in a complex geologic environment remains a challenge. Conventional amplitude inversion assumes true seismic amplitudes. In a complex subsalt environment, inadequate illumination of the subsurface due to complex geology or the acquisition geometry has detrimental effects on the amplitudes and phase of the migrated image. Such effects are not compensated for in conventional seismic inversion techniques. Consequently an imprint of various non-geological effects, including illumination, will manifest themselves in the results of seismic inversion, leading to a less reliable estimation of the resultant elastic and rock properties. The depth domain inversion workflow uses point spread functions to capture the dip dependent effects due to acquisition geometry and complex geology. The amplitude inversion is performed in the depth domain and the output is a reflectivity image corrected for illumination effects. This provides better event continuity, a sharper image, more reliable amplitude information resulting in an improved structural and quantitative interpretation. This paper presents the results of a field data example where depth domain inversion is applied in a complex subsalt environment. 77 th EAGE Conference & Exhibition 2015

2 Introduction Geophysical reservoir characterisation in a complex geologic environment remains a challenge. In particular, conventional amplitude inversion assumes that the seismic amplitudes are correctly located and can be inverted to derive a true representation of the rock properties (often referred as true amplitude images). This case study is located in the Gulf of Mexico in an area with a complex salt structure. The target is subsalt and poorly illuminated due to the complexity of the salt overburden (Figure 1a). In order to obtain an image of the subsurface, large offset full azimuth data has been acquired. This enables the interpretation of key subsalt horizons on a reverse time migration (RTM) image (Figure 1b). However, the inadequate subsurface illumination due to complex geology or the acquisition geometry has detrimental effects on the amplitudes and phase of the migrated image. Analysis of the amplitudes draped over the key horizons shows that there is a clear correlation with variable illumination (Figure 2). Areas that are very well illuminated by a large range of offsets and azimuths seem to correspond to the higher amplitudes, whereas other areas illuminated by a restricted range of offsets and azimuths seem to correspond to low amplitude areas along the key horizon. Conventional amplitude inversion techniques do not compensate for these amplitude and phase variations. Figure 1 a) (left) Illustration of the subsalt target area, b) (right) RTM image with source illumination compensation through target area, with target horizon interpretation overlay. Figure 2 (left) RMS amplitude extracted around the target horizon (right) illumination map at target horizon (from very low illumination in black to high illumination in white). Highlighted in red, the low illumination corridor corresponds to a low amplitude corridor in the RTM image. Highlighted in black, high amplitudes in the RTM image correspond to highly illuminated areas. This is a significant limitation for quantitative interpretation as an imprint of various non-geological effects, including illumination, will manifest themselves in the results of seismic inversion.

3 Consequently, any attributes derived from the image amplitudes will not accurately represent the properties of the corresponding lithology. In order to address this simplification, we propose a technique to perform amplitude inversion directly in the depth domain, correcting for the dip dependent illumination effects caused by the acquisition geometry and complex geology, and thus creating consistent and more reliable imaging products and seismic inversion attributes from depth migrated data. Capturing dip dependent illumination effects using point spread functions The key input to the workflow is a grid of point spread functions (PSFs). These are the impulse response of the modelling and imaging procedure. In mathematical terms, the migrated image m is related to the true reflectivity r by m = M*Mr = Hr where M is a modelling operator, M* is the migration operator and H=M*M is a Hessian operator, a measure of the illumination effects due to velocity variations and acquisition geometry, which blurs the true reflectivity to give the migrated image. The grid of PSFs is an approximation of the Hessian operator. In other terms, it is a representation of the spatially and depth variant 3D wavelet embedded in the migrated image and it captures the dip dependent illumination effects due to acquisition geometry and complex geology as illustrated in Figure 3. Careful analysis of the information captured by the PSFs can be correlated with the amplitude and phase variations observed along the target horizon. For example, Figure 3 presents inline and crossline displays through the RTM volume. The PSF corresponding to an area with lower amplitudes has been extracted and analysed. The spectra of the PSF clearly illustrates a strong dip dependence. The geological dip estimated from the RTM image is overlaid on the spectra of the corresponding PSF and shows that it lies at the edge of the illuminated dip range. This observation confirms the effects of variable illumination on the RTM image. Figure 3 (left) Inline and crossline through the RTM image, (middle) inline and crossline through the corresponding PSF extracted at the highlighted location, and (right) associated Kx Kz and Ky Kz spectra showing the dip dependent illumination effects captured by the PSF. The estimated local geological dip is represented by the black arrow. Depth domain inversion results The depth domain inversion workflow finds the best reflectivity model r by minimizing the least squares objective function m - Hr 2 (Fletcher et al., 2012). It can be seen as a least squares migration in the image domain and it is entirely performed in the depth domain. The output from depth domain

4 inversion is either a reflectivity image corrected for the dip dependent illumination effects, or if appropriate well data is available for calibration, a reflectivity image and the associated absolute acoustic impedance volume. Comparisons of the initial RTM image (with source illumination compensation) and the reflectivity image (Figure 4) show a clear improvement in major event continuity, an overall increase in bandwidth (Figure 5) and sharpening of the image and detailed minor events previously unseen. Figure 4 (left) RTM image with source illumination compensation, (right) reflectivity image output from the depth domain inversion. Highlighted in back, high- and low-amplitudes areas in the RTM image leading to more balanced amplitudes in the reflectivity image. Circled in back, an area where structural interpretation will be significantly improved after depth domain inversion. Figure 5 Amplitude spectra extracted along target horizon on the RTM image and on the reflectivity image, normalised to maximum amplitude.

5 The amplitudes extracted along the key horizon from the reflectivity image also appear more balanced and an amplitude analysis (Figure 6) confirms that the amplitudes are now more consistent with the structure and are less impacted by the variable illumination. Figure 6 (left) RMS amplitude extracted around the target horizon on the RTM image showing a strong imprint of the illumination effects, (right) RMS amplitude extracted around the target horizon on the reflectivity image showing a better consistency of the amplitudes with the structure of the horizon and significantly reduced imprint of the variable illumination. Conclusions The depth domain inversion workflow illustrated in this paper uses point spread functions to capture the dip dependent effects due to acquisition geometry and complex geology. The amplitude inversion is performed in the depth domain and the output is a reflectivity image corrected for illumination effects. This provides better event continuity, a sharper image, more reliable amplitude information resulting in an improved structural and quantitative interpretation. The depth domain inversion workflow can also be parameterised to output an absolute acoustic impedance volume, as well as the associated reflectivity, provided that appropriate well data is available for calibration. There is also the option to include more sophisticated physics in the generation of the PSFs, to incorporate ghost effects (Caprioli et al., 2014) or attenuation effects (Cavalca et al., 2015). Including such aspects within the inversion provides a mechanism to produce an even higher fidelity reflectivity image. Acknowledgements The authors would like to thank Schlumberger for the permission to publish this work and Schlumberger Multiclient for the permission to use the data. The authors would also like to thank Robin Fletcher, Stewart Archer, Alfonso Gonzales, and Jennifer Graham for their support and technical advice. References Cavalca, M., Fletcher, R.P. and Du X. [2015] Q-compensation through depth domain inversion. 77th EAGE Conference & Exhibition, Expanded Abstracts, [submitted]. Caprioli, P.B.A., Du, X., Fletcher, R.P. and Vasconcelos, I. [2014] 3D source deghosting after imaging. 84th Annual International Meeting, SEG, Expanded Abstracts, 4,092-4,096. Fletcher, R.P., Archer, S., Nichols, D. and Mao, W. [2012] Inversion after depth imaging. 82nd Annual International Meeting, SEG, Expanded Abstracts, 1-5.