Multi-well wavelet-synchronized inversion based on particle swarm optimization

Wavelet estimation is an important part of high-resolution seismic data processing. However, it is difficult to preserve the lateral continuity of geological structures and effectively recover weak geological bodies using conventional deterministic wavelet inversion methods, which are based on the joint inversion of wells with seismic data. In this study, starting from a single well, on the basis of the theory of single-well and multi-trace convolution, we propose a steady-state seismic wavelet extraction method for synchronized inversion using spatial multi-well and multi-well-side seismic data. The proposed method uses a spatially variable weighting function and wavelet invariant constraint conditions with particle swarm optimization to extract the optimal spatial seismic wavelet from multi-well and multi-well-side seismic data to improve the spatial adaptability of the extracted wavelet and inversion stability. The simulated data demonstrate that the wavelet extracted using the proposed method is very stable and accurate. Even at a low signal-to-noise ratio, the proposed method can extract satisfactory seismic wavelets that reflect lateral changes in structures and weak effective geological bodies. The processing results for the field data show that the deconvolution results improve the vertical resolution and distinguish between weak oil and water thin layers and that the horizontal distribution characteristics are consistent with the log response characteristics.