Reconstructing an accurate and high-resolution subsurface model is attractive in the fields of both geology and seismology. However, due to the band-limited characteristics of seismic data, the inversion greatly depends on the reliability of the initial model. A fairly acceptable initial model could lay a good foundation for seismic inversion. In this article, we first introduce a well-log interpolation method with the local slope as a constraint for building a high-fidelity starting model in prestack amplitude versus offset/angle (AVO/AVA) inversion. First, we briefly review the basic theory of general seismic inversion. Then, instead of using the conventional preconditioned least-squares method, we introduce shaping regularization theory into the geological structure-guided well-log interpolation to accelerate the convergence. We use the plane-wave destruction (PWD) algorithm to extract the slope attribute from seismic data, images, or velocity models. The slope is used as the constraint to solve the inverse problem based on the shaping regularization method. Numerical examples demonstrate that the proposed initial model building method performs better than the conventional ones. It greatly improves the accuracy of inversion results. Furthermore, we apply the proposed model building method to the inverse problems of AVO/AVA inversion and reservoir parameter estimation of several field data sets for the first time, which demonstrate encouraging performance.

Seismic exploration of unconventional hydrocarbon reservoirs (e.g., shale rocks) must take into account the transverse isotropy with a vertical axis of symmetry (VTI) characteristics. Prestack inversion for VTI media is more complex than the isotropic case, since the forward engine is highly nonlinear and more unknowns (five instead of three) are involved, which aggravate the ill-posedness of the inverse problem. Here, we propose a hierarchical inversion scheme to improve the estimation of the five parameters, where the exact reflection coefficient is used as forward engine (instead of the commonly-used approximations which lack accuracy). To handle the highly nonlinear inverse problem, we perform the prestack anisotropic inversion in two steps, namely, a preliminary linear result is used to reduce the search window and to formulate the constrain term and initial models of the subsequent nonlinear step. Specifically, for a reasonable preliminary estimation, we introduce a data-driven model building algorithm to provide reliable initial models, and employ the limited-memory Broyden–Fletcher–Goldfarb–Shanno combined with the momentum technique (LBFGS-MT) optimization to increase the convergence speed. We derive the Fréchet derivatives of the exact forward operator with respect to the parameters, i.e., the key factors of the linear part. Besides, we introduce a hybrid global optimization, the particle swarm optimization aided by very fast simulated annealing (PSO-VFSA) to enhance the accuracy and computational efficiency of the nonlinear stage. Synthetic tests demonstrate the effectiveness and accuracy of the proposed scheme. The field application shows that the method is capable to obtain reliable elastic information of shale reservoirs.

Seismic waveform inversion has been one of the most studied topics in recent years, which can be implemented both in common shot gather and prestack common midpoint gather. However, there are still several intractable issues to be addressed urgently, such as high-computational complexity, nonuniqueness, and robustness. In this paper, we have developed a model-based prestack waveform inversion (PWI) with a generalized propagation matrix scheme as forward operator, where a regularized function is minimized with the limited memory-Broyden-Fletcher-Goldfarb-Shanno technique to determine a model update corresponding to an adaptively determined regularization weight in each iterative step. To avoid falling into local extrema in the process of solving the objective function, we introduce an optimal transport method into the objective function to improve its convexity and use L-curve method to acquire the optimal regularization weight adaptively. The model tests show that the proposed scheme performs better than the conventional method significantly both on convergence and on accuracy. Furthermore, we apply the PWI with the proposed inversion scheme to the well-logging data and real seismic data. The results demonstrate that the proposed inversion scheme is not only capable of obtaining an accurate description of subsurface properties but also has a good convergence and robustness.

Regularization is effective in mitigating the ill-condition existing in inverse problems. With respect to the ill-conditioned prestack seismic inversion, regularization aims to stabilize the multiple inverted results and, essentially, reconstruct structural features of subsurface parameter (model) as realistic as possible. Among variants of regularization method, Markov random field (MRF) is an effective approach in formulating prior constraint. However, standard MRF-based or other methods often require prior knowledge of the structural features of desired models, e.g., smoothness, blockiness, and sparsity, after which the prior constraint is formulated. Therefore, such a model-driven regularization method lacks applicability to the cases with geological complexity or limited prior knowledge. In this article, we propose a data-driven regularization scheme for prestack seismic inversion. The MRF-based constraints formulated by multiple orders are quantitatively integrated into the inversion procedure driven by seismic data. In order to endow the method with high adaptation to geological complexity, we iteratively adjust the regularization parameters of multiple orders via the maximum likelihood estimator. Besides, we incorporate the multivariate Gaussian distribution among elastic parameters into the model update/perturbation in fast simulated annealing, by which the objective function is optimized while the multiple results are correlated and stabilized. Synthetic tests indicate that the proposed method is capable of revealing structural details and achieving multiple results with less uncertainty. Field application provides further validation, wherein the results distinctly reveal structural details within the target formation.

Since most information on S-wave velocity and density exists in the middle to large angle range of seismic data, traditional inversion methods based on the Zoeppritz approximation have difficulty in obtaining satisfactory results. Therefore, as a high-accuracy amplitude varied with angle (AVA) inversion, exact Zoeppritz (EZ) equation inversion has aroused a lot of attention in recent years. As for any other nonlinear inversion, iterative convergence and error are the important problems. In this letter, based on a Bayesian framework, we introduce optimal transport into EZ equation AVA inversion. Then, the limited-memory Broyden-Fletcher-Goldfarb-Shanno method is adopted to solve the regularization-constrained least-square argument function to obtain the inversion results, including P-wave velocity, S-wave velocity, and density. We compare this method with a conventional method, which is based on an L2 norm or weighted L2 norm as a residual method in the model test. The results show that the proposed method not only reduces the error of the results to be smaller than L2 norm, but it also improves the convergence rate.

Rock-physics amplitude-variation-with-offset (AVO) inversion aims at directly predicting reservoir properties from prestack seismic data. However, carbonate reservoir rocks often develop complex pore structures, which limits the application of conventional inversion methods with fixed pore-geometry parameters. We derive a PP-wave reflectivity equation in terms of porosity, water saturation, and pore-aspect ratio and we develop a rock-physics AVO inversion method to jointly estimate the petrophysical and pore-geometry parameters in carbonates. The reflectivity equation is obtained by linearizing the rock-physics model based on the differential effective medium model and Gassmann’s equation using Taylor series approximation, and we combine the linearization with the Aki-Richards equation. Thanks to the linearized model, the analytical solution to the inverse problem is derived using Bayesian linear theory. Our solution combines simulated annealing to estimate the prior information of the pore geometry and the iterative Bayesian inversion to update the posterior model. The method is tested on a benchmark data set and compared with conventional methods with a fixed aspect ratio. The results show that our method improves the petrophysical results. The method is also compared with two-step and nonlinear inversion methods to demonstrate its advantages. The method is applied to a field seismic section from a carbonate reservoir with a complex pore structure to demonstrate its advantages in terms of prediction accuracy.