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Most existing amplitude variation with angle (AVA) inversions are based on the exact Zoeppritz equation or its approximations. These modeling methods, which are ray-tracing-based and describe P-wave primary reflections only, lead to exacting requirements for pre-processing of the input data. Current processing is inadequate to satisfy these demands, especially for removing the effects of transmission losses, P-wave multiples and various converted-wave modes. By using input data with processing errors, inversion results of primary-only methods are predictably not accurate enough. The propagator matrix (PM), like the reflectivity method, uses an analytical solution to the wave equation and considers full-wave propagation effects in horizontal or nearly horizontal multilayered earth models. The numerical examples verify that a PM can effectively estimate transmission losses, multi-reflections and the comprehensive responses of thin interbedded layers, and also has higher reflection sensitivities to P-wave and S-wave velocity and density, as compared with ray-tracing-based AVA modelling. A pre-stack AVA three-parameter inversion by using a PM as the forward engine is proposed. Following a Bayesian approach, the inversion is stabilized by including the correlation of P-wave velocity, S-wave velocity and density. For inversion accuracy, the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization method is used to solve the augmented function, and the generalized cross-validation (GCV) criterion (Huang et al. in J Geophys Eng 14(1):100–112, 2017) is introduced to adaptively acquire the regularization parameter. Theoretical model inversion analysis shows that the proposed inversion can make use of transmission losses, P-wave multiples and converted wave modes, which not only cost-effectively simplifies the pre-processing, but also generates reasonable inverted results for multilayered conditions. The proposed inversion is then applied to a set of real data, and a comparison with Zoeppritz equation-based inversion demonstrates that PM inversion is clearly superior to Zoeppritz equation-based inversion in terms of stability and accuracy.

Abstract Conventional pre-stack seismic inversion methods based on the Aki–Richards approximation rely on the assumption of weak elastic contrasts, which often leads to inversion distortions at interfaces with high elastic contrasts. This study proposes an innovative pre-stack seismic inversion method that integrates low-frequency boundary inequality constraints based on prior elastic parameter information with Lp (0 < p < 1) quasi-norm sparsity constraints. First, low-frequency information constraints and sparsity constraints on reflection coefficients are incorporated into the inversion objective function. Then, by introducing non-negative slack variables and residual variables, the inequality constraints based on prior elastic parameter information are transformed into equality constraints and embedded into an augmented Lagrangian function to effectively reduce the solution space. Finally, the problem is solved comprehensively using the Alternating Direction Method of Multipliers along with a soft-thresholding shrinkage algorithm. Model tests and practical applications demonstrate that the proposed method effectively mitigates the strong dependence of the Aki–Richards linear approximation on the weak elasticity assumption and can accurately characterize gas-bearing reservoir boundaries.

Pre-stack seismic inversion is essential to detailed stratigraphic interpretation of seismic data. Recently, various deep learning methods have been introduced into pre-stack inversion, effectively capturing the vertical correlations of seismic data. However, existing deep learning methods face challenges such as insufficient feature extraction, poor lateral continuity, and unclear inversion details. We introduce the atrous spatial pyramid pooling (ASPP) module into the pre-stack inversion process, modifying the connection order and mode of its three components. Additionally, we incorporate a triplet attention module to extract features at different scales and utilize a gate recurrent unit (GRU) module to extract global information. During the network training stage, we employ a multi-gather simultaneous inversion method, combining one- and two-dimensional inversions. The proposed method is named IGIT (I for improved ASPP, G for GRU, I for initial model, and T for triplet attention). To verify the feasibility of this network model, we evaluate it using the Marmousi2 model, SEAM model, and field data, comparing the results with other deep learning methods. Experimental results demonstrate that the IGIT not only improves lateral continuity but also delivers accurate and clear inversion details. Notably, the inversion effect for density parameters shows significant enhancement.

With the high yield of many wells represented by Well JT1 in the Maokou Formation, has catalyzed a surge in exploration activities along the platform margin facies of the Maokou Formation in central Sichuan and further showed the significant exploration potential of the Maokou Formation in the northern slope. However, the fracture cave body of the Maokou Formation exhibits a high degree of development, strong longitudinal and horizontal heterogeneity, large formation pressure differences, and drilling events such as gas kicks and lost circulation occur frequently, which seriously affects the efficient implementation of drilling. Understanding the spatial distribution of the three-pressure in the formation can help better deal with and solve the above problems. Therefore, in order to help the safe, high-quality and rapid drilling of the Maokou Formation in the study area, and enhance the efficiency of oil and gas development, this paper explores the research on the prediction method of the three-pressure of jointing well-seismic data based on the geomechanical experimental data and the actual drilling data. In the process of prediction of pore pressure, this study found that the pore pressure and formation velocity in the study area have an exponential relationship. In order to enhance the applicability of the Filippone’s method in the study area and improve the prediction accuracy of pore pressure, the linear relationship between pore pressure and formation velocity in the Filippone’s method is modified to an exponential relationship, and a pore pressure prediction model suitable for the work area was established. Based on the Mohr–Coulomb criterion and Huang's model, the prediction models of collapse pressure and fracture pressure applicable to the study area were established, respectively. Then, the elastic parameters were obtained through pre-stack inversion, and the three-pressure bodies were calculated based on the elastic parameter bodies. The results indicate that: (1) The three-pressure prediction method of the jointing well-seismic data in this paper can predict the formation's longitudinal and transverse pressure anomaly zones in advance. (2) The Maokou Formation in the study area is characterized by abnormally high pressure, to balance the pressure of the high-ground formation, high-density drilling fluid is necessary. (3) The prediction results of three-pressure in this paper are highly consistent with the actual drilling engineering events, which verifies the reliability of the three-pressure prediction results presented in this study. The results of the study can provide a basis for decision-making in drilling geological design, such as the determination of drilling fluid density, the evaluation of borehole stability and other engineering problems that require support from three-pressure data.

Geofluid identification from seismic data are crucial for understanding reservoir characteristics. However, fluid indicators based on elastic parameter combinations show strong ambiguity in terms of geofluid identification. Although the effective pore-fluid bulk modulus proves to be the superior fluid indicator in geofluid discrimination, it is limited to empirical models such as the critical porosity model. The consolidation parameter model can evaluate the consolidation and compaction of sediments and is widely used due to its better applicability. Therefore, a novel fluid indicator considering consolidation parameter is proposed and a boundary-constrained inversion strategy for geofluid identification is developed. First, the novel fluid indicator is defined based on the poroelasticity theory. Then, the linearized AVA (amplitude variation with angle) approximation equation related to the new fluid indicator is derived to directly link seismic data and properties of pore-filling materials. The model analysis verifies the accuracy of the derived approximation equation at moderate incident angles, which can be used for parameter prediction by pre-stack seismic inversion. Furthermore, the contribution of each attribute of the novel equation to the reflectivity is analyzed to validate the feasibility of new fluid indicator inversion. Finally, a boundary-constrained AVA pre-stack inversion method is presented to enhance the robustness of the inversion results of the model parameter. The reliability of the improved method is proved by accurate inversion results of synthetic seismic records. After determining the consolidation parameter using well-log data combined with BGT (Biot–Gassmann Theory), the field data further demonstrated that the proposed approach is accurate and effective.

This study investigates the natural gas hydrates within the Qiongdongnan Basin by integrating well-log and seismic data. Through pre-stack inversion and rock physics analysis, key parameters such as P-wave and S-wave impedances were utilized to distinguish hydrate-bearing formations from other geological bodies. A low-frequency model was constructed using the Inverse Distance Weighting (IDW) algorithm to improve the precision of parameter inversion. This study employs a multi-constraint inversion strategy, incorporating hard constraints from multiple wells and soft constraints from geological frameworks, ensuring reliable inversion results. Findings indicate that hydrate reservoirs are characterized by increased wave velocity and density due to hydrate accumulation, providing insights into the spatial distribution and characteristics of hydrates. This research enhances the understanding of hydrate reservoirs and offers valuable data for exploration in the Qiongdongnan Basin.

Seismic inversion is the primary way to obtain subsurface models, lithologic and stratigraphic information. However, seismic elastic parameters inversion and ‘discrete lithofacies’ identification for complex volcanic reservoirs are usually independent during the whole inversion process. Also, the influence of reservoir lithology on elastic parameters is not always considered directly before lithofacies prediction. This paper proposes a probabilistic pre-stack seismic inversion method for lithofacies and elastic parameters of volcanic reservoirs. Under the framework of Bayesian inversion, considering that the prior probability distribution of elastic parameters of volcanic reservoirs is affected by volcanic lithofacies, a posteriori probability distribution characterized by a mixed probability model is first derived. Then, a single-point-direct sequential simulation stochastic algorithm with simultaneous optimization of multiple solutions is used to simulate the posterior probability distribution of elastic parameters and lithofacies of volcanic reservoirs, which improves the resolution of lithofacies prediction results of volcanic reservoirs. The feasibility and stability of our method are ensured through synthetic and field applications. The prediction results highly agree with logging curves and lithology logging interpretation data. We have improved the resolution of volcanic rock reservoir lithofacies prediction results. In one-dimensional tests, we achieved the prediction of lithofacies and elastic parameters for three types of volcanic lithofacies. The error compared to prior information is no higher than 15%, thereby verifying the method’s good noise resistance.

Although pre-stack inversion has been carried out on reservoir prediction, few studies have focused on the application of pre-stack for seismic inversion in fractured-cavity carbonate reservoirs. In carbonate rock, complicated combinations and fluid predictions in karst caves are remain unclear. Post-stack methods are commonly used to predict the position, size, and fillings of caves, but pre-stack inversion is seldom applied in carbonate karst reservoirs. This paper proposes a pre-stack inversion method for forward modeling data and oil survey seismic data, using both points to indicate the application of pre-stack inversion in karst caves. Considering influence of cave size, depth, and filler on prediction, three sets of models (different caves volume; different fillings velocity of caves; complicated combination of caves) are employed and inverted by pre-stack inversion. We analyze the pre-stack results to depict Ordovician oil bearing and characterize caves. Geological model parameters came from actual data of the Tahe oilfield, and seismic data were synthesized from geological models based on full-wave equation forward simulation. Moreover, a case study of pre-stack inversion from the Tahe area was employed. The study shows that, from both the forward modeling and the oil seismic data points of view, pre-stack inversion is applicable to carbonate karst reservoirs.

The reliable prediction of pore pressure is essential for petroleum engineering in its different stages, with the Eaton and Bowers' methods being the most used for this purpose. However, their application in carbonate rocks still needs to be improved because carbonates do not compact uniformly with depth, as shale does. This research calculated the pore pressure using the Eaton, Bowers, and Weakley methods and well logs of a carbonate formation and found that the Weakley's approach predicts pressure more accurately. The method presented uses an acoustic impedance equation derived from the Bowers' method, whose parameters were calibrated with the Weakley's pore pressure profile. The pore pressure estimated near the borehole, via the acoustic impedance provided by the pre-stack inversion, is very close to that observed during drilling, which indicates a reliable prediction. The method was applied to a seismic line and well logs in the Middle Magdalena Valley Basin—Colombia, where the overpressured well Lizama 158 caused a significant environmental disaster in 2018. The obtained subsurface pore pressure distribution is reliable, matches overpressure in calcareous rocks near the well, and estimates anomalous pressure in zones distant from the well.

Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation. Conventional methods often rely on indirect computation or approximations of the Zoeppritz equations to estimate Young's modulus, which can introduce cumulative errors and reduce the accuracy of inversion results. To address these issues, this paper introduces the analytical solution of the Zoeppritz equation into the inversion process. The equation is re-derived and expressed in terms of Young's modulus, Poisson's ratio, and density. Within the Bayesian framework, we construct an objective function for the joint inversion of PP and PS waves. Traditional gradient-based algorithms often suffer from low precision and the computational complexity. In this study, we address limitations of conventional approaches related to low precision and complicated code by using Circle chaotic mapping, Lévy flights, and Gaussian mutation to optimize the quantum particle swarm optimization (QPSO), named improved quantum particle swarm optimization (IQPSO). The IQPSO demonstrates superior global optimization capabilities. We test the proposed inversion method with both synthetic and field data. The test results demonstrate the proposed method's feasibility and effectiveness, indicating an improvement in inversion accuracy over traditional methods.