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Prediction of coalbed methane content based on seismic identification of key geological parameters: a case in a study area, Southern Qinshui Basin

Liu Jing, Chang Suoliang, Zhang Sheng, Li Yanrong, Hao Yaju, He Guoxian, He You, Liu Bo

Acta Geophysica

2023

Vol. 71, no. 6

2645--2662

The coalbed methane content (CMC) is an important parameter to evaluate the degree of coalbed methane enrichment, and also an important reservoir parameter to calculate coalbed methane resources, productivity prediction and reservoir simulation. Accurately identifying the distribution of CMC is crucial to the exploration of CBM. In this study, we developed a prediction method for the CMC distribution via seismic techniques identification of key geological parameters such as structure, coal thickness and sedimentation. Firstly, the geological factors that control the generation and preservation of CBM in the study area are quantitatively characterized by using five parameters: surface (X1), residual (X2), dip (X3), coal thickness (X4) and the ratio of sand to mud (X5). Secondly, the geological parameters are extracted by seismic structure interpretation and inversion prediction technology. Thirdly, the key geological parameters of CMC are screened out by grey correlation analysis. Finally, the functional relationship of CMC and the key geological parameters is established to predict the CMC distribution. The method is applied to the CMC distribution prediction of two coal seams of a study area in the southern Qinshui Basin, China. Results show that different coal seams differ in key geological parameters of CMC, resulting in various CMC distribution laws. The CMC prediction method based on the key geological factors can effectively delineate the CBM enrichment area in the study area, providing important reference for the CBM exploration and development.

Nonstationary seismic inversion: joint estimation for acoustic impedance, attenuation factor and source wavelet

Hao Yaju, Wen Xiaotao, Zhang Hua, Zhu Yunfeng, Deng Chengxiang

Acta Geophysica

2021

Vol. 69, no. 2

459--481

Seismic signal can be expressed by nonstationary convolution model (NCM) which integrates acoustic impedance (AI), attenuation factor (AF) and source wavelet (SW) into a single formula. Although it provides attractive potential to invert AI, AF and SW, simultaneously, efective joint inversion algorithm has not been developed because of the extreme instability of this nonlinear inverse problem. In this paper, we propose an alternating optimization scheme to achieve this nonlinear joint inversion. Our algorithm repeatedly alternates among three subproblems corresponding to AI, AF and SW recovery until changes in inverted models become smaller than the user-defned tolerances. Also, when we optimize one parameter, other two parameters are fxed. NCM is an explicit linear formula for AI; therefore, AI recovery is accomplished by linear inver sion which is regularized by low-frequency model and isotropy total variation domain sparse constraints. However, NCM is a complicated nonlinear formula for AF. To facilitate the AF inversion, we propose a centroid frequency-based attenuation tomography method whose forward operator and observations are acquired from the time-varying wavelet amplitude spectra which is estimated according to Gabor domain factorization of NCM. SW is decoupled from NCM based on Toeplitz structure constraint, and we obtain an orthogonal wavelet transform domain sparse regularized SW inverse subproblem. Split Bregman technique is adopted to optimize AI and SW inverse subproblems. Numerical test and feld data application confrm that the proposed nonstationary seismic inversion algorithm can stably generate accurate estimates of AI, AF and SW, simultaneously.