Geophysical Prospecting最新文献

Seismic inversion plays a critical role in populating subsurface properties into geomodel; however, its effectiveness is often constrained by the resolution limits of seismic data. This study evaluates the effectiveness of stochastic versus deterministic acoustic-impedance inversion for estimating total porosity (Φ_total), effective porosity (Φ_e) and permeability (K) in the complex reefal carbonates of the Soğucak formation, Deveçatağı oil field—northwestern Thrace Basin, Türkiye—whose thickness ranges from 2 to 57 m. The wedge model indicates a tuning thickness of 60 m in Soğucak formation which limits the vertical resolution of the deterministic inversion that directly affects the geomodel resolution. To address this challenge, a stochastic inversion was performed to resolve beds below the tuning thickness, using a high-resolution 1 millisecond (ms) vertical sampling grid and producing non-unique, fine-layered impedance realizations. Petrophysical relationships were established using 75 core plugs showing strong porosity–permeability trends that were cross-validated with wireline logs. These relationships were applied to acoustic-impedance volumes derived from both deterministic and stochastic inversion. Correlations at well locations used in the model are naturally higher due to constraints from acoustic-impedance logs; therefore, we emphasized blind-well correlations to assess predictive performance at locations without well control. Blind-well tests at W-2, W-4, W-6 and W-11 demonstrate valid predictive capability, with stochastic inversion achieving correlations of 0.65–0.73 compared to 0.45–0.52 for deterministic inversion, effectively resolving sub-tuning thickness beds and reliably predicting porosity and permeability. By overcoming the resolution limitations of deterministic inversion, stochastic inversion supported by robust petrophysical relationships—when applicable—provides a reliable and field-proven tool that can be adapted to carbonate reservoirs in diverse geological settings worldwide.

Multicomponent seismic exploration technology comprehensively utilizes the dynamics and kinematics characteristics of seismic waves, which can reduce the non-uniqueness of seismic imaging and predict hydrocarbon-bearing reservoirs with high accuracy. However, multicomponent seismic data are often incomplete due to the constraints from field environment and acquisition costs. To address the problem of reconstructing multicomponent seismic data, conventional compressed sensing (CS)-based algorithms reconstruct each component independently, failing to exploit implicit relationships between different components. By mapping different components to the real and imaginary parts of complex numbers, we can quantify their intrinsic correlations through the mathematical structure of complex numbers and preserve the cross-information between different components. Therefore, under the framework of CS, we construct the objective function of joint reconstruction of two-component seismic data and propose a joint reconstruction method of two-component seismic data based on CS and complex Curvelet transform (CCT). First, we introduce complex numbers to establish the implicit relationship between different components by taking them as real and imaginary parts. The complex numbers constructed from different components are then subjected to the CCT as a whole. In this process, the joint sparsity of the two components is used as a priori information for data reconstruction. Finally, the proposed reconstruction model is solved using an improved fast projection onto convex sets. Experiments with synthetic and field data demonstrate that the proposed method effectively achieves the joint reconstruction of two-component seismic data. Compared with reconstruction methods only using a single component of seismic data, the proposed method exhibits higher computational efficiency and accuracy without increasing data dimensions.

A medium with homogeneous anisotropic layers that are thin compared with the elastic wavelength can be replaced with an equivalent anisotropic medium by the process of Backus averaging. The equivalent medium for isotropic and transversely isotropic layers (where the axis of symmetry is normal to the layering) is transversely isotropic. Transversely isotropic media can be described by the symmetry axis, two axial velocities and three dimensionless parameters. In this paper, we derive simple expressions for these dimensionless parameters in terms of differences of elastic parameters between the layers. We investigate the signs of the dimensionless parameters and conditions for zero parameters in layering with two isotropic media or an isotropic and a transversely isotropic medium.

Least-squares migration (LSM) is one of the most accurate imaging methods in seismic exploration. In recent years, image-domain LSM (ID-LSM) based on the approximated Hessian matrix has received widespread attention and development. How to effectively represent the Hessian matrix and implement the ID-LSM efficiently and stably remains challenging. This study proposes an efficient computation method for the Hessian matrix and develops a data-driven high-resolution imaging scheme to promote the application of LSM. Specifically, we first introduce the analytical expression of the Hessian matrix within the framework of inversion imaging, leveraging the sparsity of the Hessian matrix and using point-spread functions (PSFs) to approximate it. Then, considering the nonlinear characteristics of image-domain PSFs deconvolution, we employ deep learning to construct a data-driven imaging correction network. Finally, we incorporate features from the target data into the network, achieving efficient and faithful imaging of subsurface reflection coefficients. Through the computation cost analysis of PSFs construction, the developed method significantly reduces the computational costs, achieving only one-fourteenth of the modelling–migration method based on the wave equation. The synthetic and field data examples demonstrate the effectiveness of the proposed data-driven imaging scheme in both the spatial and wavenumber domains.

Analytical formulae for P- and SV-wave normalized attenuation coefficients play a key role in attenuation anisotropy parameter estimation in dissipative transversely isotropic media with a vertical symmetry axis. We revisit approximate formulae for the P- and SV-wave normalized attenuation coefficients. We use perturbation theory to derive accurate formulae for P- and SV-waves in such media. The proposed formulae can reduce to simple expressions for attenuative elliptical anisotropic media and isotropic media. From the perturbation-based formulae, we obtain the linearized formulae, the second-order formulae and the fractional formulae. We use numerical examples to test the accuracy of the proposed formulae and implement the perturbation-based formulae and the second-order formulae in an inversion scheme to estimate the attenuation anisotropy parameters. From the numerical examples, we analyse the validity of the approximate formulae in computing the normalized attenuation coefficients and attenuation anisotropy parameter estimation.

Prestack depth migration requires a velocity model that extends from the surface through shallow to deep layers. Generally, the shallow velocity model is built using first-arrival wave, under which the velocity model is built from reflected wave. These two models are integrated to form a continuous velocity model spanning from the surface to deep layers. Conventional shallow velocity model building methods are restricted by non-uniqueness of inversion, limited inversion depth and poorly defined reflection interface, which hinders their integration with deeper reflection-derived velocity models. Errors in the shallow velocity model can compromise the imaging quality of mid-deep. In this article, we propose a 3D shallow velocity model building method using the joint inversion of first-arrival and reflection traveltimes constrained by well data. This approach enables simultaneous inversion of velocity and reflection interfaces, thereby facilitating the integration of shallow and mid-deep velocity models. Combining the first-arrival and reflected waves enhances ray coverage angles and folds. By constraining the inversion for velocity and reflection interface with logging velocity and drilling depth, respectively, the method effectively mitigates the non-uniqueness of inversion, thereby improving the accuracy of shallow velocity model. The inverted reflection interface can also be used for integration of shallow velocity model and its underlying velocity model. The theoretical model test proves the feasibility of the method, and the field data application verifies the effectiveness of the method.