Geophysical Prospecting最新文献

Pre- and post-stack seismic inversion is the primary approach for converting collected seismic data into geophysical property models particularly velocity for subsurface interpretation and reservoir characterization. The traditional workflows often start from an initial property model and iteratively revise it by minimizing the misfit between the acquired real seismic dataset and the synthetic one derived from the updated property models, here denoted as the soft constraint in seismic space. However, it heavily relies on human supervision in building a good initial model and monitoring the misfit optimization process. On the contrary, most of the recent deep learning-based workflows target at non-linearly mapping seismic patterns to properties measured at available well only, here denoted as the hard constraint in property space. Correspondingly, its accuracy greatly depends on the availability of sufficient training wells; otherwise, overfitting occurs causing the machine prediction not meeting the soft constraint throughout the target seismic survey. To resolve these limitations, this study presents a practical workflow that enables rock property inversion from partial-stack seismic via two physics-guided convolutional neural networks (CNNs), with the first one embedding the approximated AVO gradient to build initial property models that satisfy the hard constraint and the second one embedding the reflection coefficients to refine the models by enforcing the soft constraint. Between both CNNs is the use of well-established relevant physics to generate pseudo property–reflectivity–seismic pairs for training the second CNN. Its added values are validated through applications to the Volve survey in North Sea and the Exmouth survey in Western Australia. The produced property volumes not only are observed of high lateral consistency and vertical resolution but also derive synthetic seismic data that are closely correlated with actual seismic data.

Accurately and effectively handling undulating interfaces, including both free surfaces and internal interfaces, remains a key challenge in seismic exploration. Traditional scalar wave equations typically neglect the influence of internal interfaces on wave propagation. To address this, the wave equation in layered media (WEILM) is established by introducing the Dirac delta function. For undulating interfaces, existing methods are mostly grid-based, and often require additional grid processing to achieve accurate description, which increases computational cost. Therefore, by introducing the meshless method combined with free surfaces boundary conditions, this article proposes the method for dealing with undulating surfaces on the basis of the radial-basis-function-generated finite difference (RBF-FD) method. Theoretical analysis indicates that the stability of the proposed method is affected by the sum of the stencil node weights. Numerical experiments show that, compared with the scalar wave equation, the interfaces term introduced in WEILM effectively adjusts waveform amplitudes. Moreover, relative to the classical Lax–Wendroff correction (LWC) method, our approach can avoid spurious diffraction waves caused by grid discretization when handling undulating surfaces. By applying RBF-FD to solve WEILM in conjunction with the method for dealing with undulating surfaces, complex seismic wavefield, including undulating surfaces, can be simulated with higher accuracy.

Seismic fluid discrimination plays a critical role in sweet spot detection, reservoir characterization, reserve evaluation and well placement. Tight sandstone reservoirs are typically characterized by low porosity, poor pore connectivity, complex pore types, non-uniform gas–water distribution and strong heterogeneity, which often lead to inaccurate fluid discrimination. In this study, we develop a double-porosity equivalent medium model for tight sandstone reservoirs using the Keys–Xu model combined with Gassmann's equation. We systematically investigate the effects of pore structure, porosity and water saturation on elastic responses. On the basis of this model, a rock physics template (RPT) is constructed using the P-wave modulus and the P- to S-wave modulus ratio. Polynomial fitting is then applied to derive mathematical expressions for both water- and gas-saturated trendlines. On the basis of these trendlines, an RPT-based fluid indicator is defined to quantify deviations from the gas-saturated sandstone trendline. We further apply the proposed fluid indicator to a tight gas sandstone reservoir in the central Sichuan Basin, Southwest China. The strong agreement between the extracted fluid indicator and well log-based water saturation interpretation demonstrates that this method significantly improves the accuracy of fluid content quantification compared with traditional semi-quantitative RPT-based approaches. Application to seismic data further shows that our method yields a reasonable estimation of gas distribution in tight sandstone reservoirs, confirming its reliability and practical applicability for fluid characterization. This approach offers promising potential for quantifying fluid content in deep-buried tight reservoirs.

The limited understanding of rock physical properties in marine shale from the Ordovician Wulalike Formation along the western margin of the Ordos Basin has hindered comprehensive evaluations of shale gas reservoirs. This study systematically investigates the variation patterns and controlling factors of seismic elastic properties in Wulalike Formation marine shale samples through petrological and petrophysical tests, while discussing the distribution characteristics of reservoir ‘sweet spots’. Results indicate that the petrological characteristics of Wulalike Formation marine shale are influenced by tectono-sedimentary differentiation. The lithology transitions from calcareous shale in upper slope environment to mixed shale in slope depression, and finally to siliceous shale in open marine shelf environment. Both organic matter abundance and porosity of the shale samples progressively increase with depositional environments and lithological transitions. Simultaneously, the rock stress skeleton evolves from carbonate particle dominance to clay and quartz particle dominance. Variations in rock microstructural characteristics among different lithological types of samples are the primary factor influencing seismic elastic properties. In petrophysical crossplots (impedance vs. porosity, Poisson's ratio vs. P-wave impedance and λρ vs. μρ), the shale samples exhibit partitioned distributions on the basis of their composition and lithology. The ‘sweet spots’ reservoirs are predominantly composed of siliceous shale, characterized by high total organic carbon (TOC), porosity and low Poisson's ratio and λρ characteristics. On the basis of the petrophysical analysis, reservoirs are categorized into three grades. Laterally, reservoir classification transitions from Grade ‘III’ to ‘I’ with changing depositional environments. Shale gas reservoirs in open marine shelf and slope depression environments (e.g., the lower part of Well ZP1) meet or exceed Grade ‘II’ standards, indicating high-quality reservoir potential.

Acoustic emission (AE) is elastic waves generated spontaneously from the creation of micro-cracks. AE waveforms share significant similarities with microseismic signals and serve as an effective tool for improving the understanding of fracture processes during hydraulic fracturing. AE events typically have small magnitude with low amplitude. To detect weak AE events, it is always necessary to set a larger gain control, but this increases the risk of large amplitude waveform being clipped beyond the saturation level of the A/D converter. Amplitude-clipped AE events are usually considered unusable and must be excluded from the estimation of source properties such as focal mechanisms. We introduce an extension of compressed sensing methods to reconstruct the clipped waveform and further use them to perform the moment tensor inversions and decomposition. This method assumes that the AE events are band-limited and the clipped segment of the waveform shares the same frequency content as the unclipped segment. Compared to conventional techniques, the proposed method can effectively reconstruct the clipped waveforms with clipping level less than 0.7, ensuring reliable moment tensor inversions and decomposition. The reconstruction method reduces the risk of confounding reasoning or misinterpretation caused by waveform distortion and provides a more reliable basis for the physical interpretation of AE properties.

The amplitude variation with offset (AVO) inversion method is crucial for predicting lithology and identifying fluids in hydrocarbon reservoirs. It is especially useful for evaluating shale oil or shale gas reservoirs. The accuracy of AVO inversion is critical to the quantitative interpretation of lithology and hydrocarbon-bearing properties in reservoirs with vertical transverse isotropic (VTI) features. This work proposes a rock-physics-constrained anisotropic AVO inversion method to achieve stable density estimations in VTI media. This method establishes a new parameter set with density as an independent variable by transforming the conventional elastic parameter domain into a deviation parameter domain through rock-physics-constrained equations. Combined with the explicit form of the Rüger approximation, this approach not only eliminates correlations among conventional inversion parameters and mitigates the impact of anisotropy but also significantly improves the accuracy of density inversion. The feasibility and effectiveness of the proposed inversion method are demonstrated through the application of synthetic and field seismic data.

Urban underground tunnelling faces challenges from small-scale unfavourable geological bodies such as boulders and karst caves, the diameters of which are less than 1 m mostly. To address this issue, the tunnel-array acoustic wave prospecting technique has been proposed. It utilizes piezoelectric transducers to excite acoustic waves with a central frequency of 4000 Hz, enabling the detection of small-scale unfavourable geological bodies ahead of tunnel. However, due to the excavation by the shield cutterhead, the cracks and fissures in the rock mass near the cutterhead will significantly develop, forming a disturbed zone with high inhomogeneity. The existence of the disturbed zone will cause severe multiple scattering, which induces artefacts in the imaging results and reduces the accuracy of the advanced prospecting results. In terms of above issues, we introduce the idea of multiple signal classification (MUSIC) algorithm into the reflection matrix method and propose a novel MUSIC algorithm–based reflection matrix method. The reflection matrix can achieve the imaging of reflectors through re-projecting the acquired data into the media at excitation and reception using Green's function. But it cannot deal with the artefacts induced by multiple scattering. The idea of MUSIC algorithm is to calculate the correlation between Green's function and the singular vectors of the signal or noise subspace, which are obtained by singular value decomposition (SVD) of covariance matrix of the acquired data, achieving estimation of the reflectors. Referring to this idea, we further improved the reflection matrix using MUSIC algorithm. The reflection matrix method is applied first, and the reflection matrix is obtained. Then by SVD of covariance matrix of the reflection matrix, we obtain the signal vectors related to the imaging results of reflectors and noise vectors related to artefacts. The signal vectors are used to calculate the correlation with an imaging operator K, which is derived from the product of the conjugate of Green's function and itself. When the computing grid within reflectors, the results reach the local maximum; otherwise, it tends to 0. In this way, we mitigate the imaging artefacts introduced by the multiple scattering. Through synthetic experiment, we verified that the proposed method can effectively suppress the imaging noise and improve resolution of the imaging results compared to the reflection matrix method. Finally, the proposed method was applied on field data obtained in Zhanmatun Iron Mine and successfully predicted the interface of the opposite tunnel in the target area.

Deploying large datasets for training machine learning models often reveals more information about the target variable and helps to avoid overfitting. However, these advantages are associated with certain challenges, such as data noise and redundancy. In the present study on well log data consisting of a relatively large dataset (40 wells from the Cambay Basin), we deploy different classes of feature selection methods (filter-based methods, wrapper-based methods and embedded methods) to obtain the optimal feature set aimed at accurate prediction of sonic logs. Additionally, we utilize methods such as the boxplot and histogram analysis to remove outliers present in the dataset. Subsequently, we use XGBoost as our machine learning model, with fivefold cross-validation and a 70:30 split. We then proceed to predict the sonic log data in a blind well. We establish that the maximum relevance minimum redundancy method shows the best results with an R-squared value of 63% when we select three out of six features – depth, neutron porosity and bulk density. Significance of the results was demonstrated using statistical tests of significance, namely one-way analysis of variance and Tukey's honestly significant difference test. The selection of these features is further validated by established geophysical principles in the form of empirical relationships.

This research presents an innovative framework for lithology detection that combines domain adaptation, an Actor–Critic reinforcement learning (RL) architecture and Transformer-based sequence modelling to enhance log interpretation reliability in complex depositional environments. The study first reviews conventional petrophysical characterization methods using wireline measurements, noting their limitations in dealing with varied lithofacies distributions and non-stationary formation properties. Subsequently, it emphasizes the superior capabilities of neural networks, particularly the Transformer architecture, in analysing temporal measurement sequences. The multi-head attention mechanism in Transformers effectively models contextual relationships within depth-dependent logging signals, which is vital for stratigraphic interpretation. The proposed framework incorporates the Actor–Critic reinforcement paradigm, where the policy network (Actor) generates lithofacies predictions, and the value network (Critic) evaluates prediction quality. This dual-network setup promotes iterative policy refinement through feedback, enhancing classification consistency and computational efficiency. Moreover, recognizing the potential for domain shifts in logging campaigns, the framework includes parameter transfer mechanisms to facilitate knowledge distillation from source to target domains. This ability to adapt across projects significantly boosts model robustness and deployment feasibility in diverse reservoirs. Experimental validation on multiple well-log datasets shows that the combined Transformer architecture, RL, and transfer strategies outperform traditional machine learning and standalone deep learning models. Quantitative results reveal improvements in prediction accuracy, cross-well generalizability and domain adaptation efficiency in novel geological environments.