Seismic impedance inversion and interpretation of a gas carbonate reservoir in the Alberta Foothills, western Canada

Abstract

Acoustic and simultaneous elastic impedance inversions of a 2D land seismic data set are performed to characterize a carbonate reservoir of Mississippian age in the Turner Valley Formation, in the Rocky Mountain foothills of western Canada. The inversions produce P‐wave and S‐wave impedance sections (Ip and Is, respectively), from which Lamé parameter × density (λρ and μρ) sections are derived. The Ip data provide a separation between the clastics and carbonates. The μρ data provide an estimate of porosity distribution within the dolomitized limestone target. Deviations from baseline curves for water‐saturated carbonates, of λρ versus porosity, λ/μ versus porosity, and Is versus Ip, are interpreted as indicators of gas potential. These indicators all provide similar spatial patterns of areas of high gas potential and are consistent with the gas occurence observed in a well.

Data-Driven Seismic Impedance Inversion Based on Multi-Scale Strategy

“…Because the Earth medium is complex and variable [3], it is often difficult to accurately establish the relationship between seismic data and subsurface model parameters with a limited number of geophysical parameters and associated mathematical physical models. Although the theory and technology of these seismic inversion methods have been continuously improved, they are not as effective as desired in practical reservoir prediction and lithology characterization [4][5][6][7][8][9][10][11].…”

Accurate estimation of acoustic impedance based on spectral inversion

“…AI inversion becomes an essential method of quantitatively interpreting seismic data and estimating reservoir properties. Conventional AI inversion methods include the direct inversion, e.g., trace integration (Oldenburg, Scheuer and Levy ) and recursive inversion (Lavergne ; Lindseth ), iterative inversion, e.g., model‐based inversion (Cooke and Schneider ), sparse spike inversion (Madiba and McMechan ), and nonlinear inversion (Zhang, Shang and Yang ; Baddari et al . ).…”

“…). The low‐frequency model can be extrapolated by using geological layer and logging data or by stacking velocity analysis (Oldenburg, Levy and Stinson ; Madiba and McMechan ). Seismic tomography (Vesnaver et al .…”

“…Adding the low-frequency model or prior impedance constraints can effectively alleviate the nonuniqueness of impedance inversion (Oldenburg et al 1983). The low-frequency model can be extrapolated by using geological layer and logging data or by stacking velocity analysis (Oldenburg, Levy and Stinson 1984;Madiba and McMechan 2003). Seismic tomography (Vesnaver et al 1999;Zhang et al 2014), full waveform inversion (Baeten et al 2013), and specialised velocity modelling software (Nascimento, Menezes and Braga 2014) can be used to generate a more accurate low-frequency model.…”