A fluid factor inversion method using the frequency-domain two-step sub-band regularization

Abstract

In oil and gas seismic exploration, fluid factors are key parameters for identifying reservoir fluid properties and evaluating reservoir potential. Although regularization methods are commonly used to enhance inversion stability, traditional time-domain lateral constraint methods struggle to effectively address the issue of abrupt lateral stratigraphic variations. This paper aims to improve the prediction accuracy and stability of fluid factors in such scenarios. Based on the characteristic that frequency-domain seismic data exhibit stronger correlation during stratigraphic abrupt changes, this paper proposes a frequency-domain two-step sub-band regularization inversion method. First, a difference operator is introduced into the frequency domain to construct the objective function, and the Alternating Direction Method of Multipliers algorithm is adopted as the solution. Furthermore, a two-step sub-band regularization strategy is proposed: First, integrating low-frequency and high-frequency residual terms into the same objective function; then performing inversion in two stages. These stages first use low-frequency data inversion to obtain the general profile of the subsurface structure as an initial model, followed by using high-frequency data inversion based on this to obtain detailed information. Theoretical model tests have verified the superiority of this method under complex geological conditions. Field application in practical work areas shows that the frequency-domain two-step method significantly outperforms the traditional time-domain lateral constraint L1 regularization method in terms of vertical resolution and lateral continuity. This method provides a more accurate and stable solution for fluid identification under complex geological conditions.

Keywords

Frequency-domain regularization / Fluid factor / Lateral constraint / Alternating Direction Method of Multipliers / Pre-stack inversion

Funding

This work was supported in part by the Technology Cooperation Project of the CNPC-SWPU Innovation Alliance (2020C×020000) and in part by the Natural Science Foundation of Sichuan Province (24NSFSC0808).

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