Geophysical Prospecting最新文献

Accurately detecting the locations of fractures and the permeability structure within a subsurface reservoir can significantly improve the optimization of production performance. Achieving peak performance in subsurface groundwater or hydrocarbon reservoirs depends on creating an accurate map that details the reservoir's characteristics derived from the history-matching process. However, this process involves repeated forward modelling simulations until the results align with historical production data, often consuming significant resources and potentially yielding non-unique reservoir models. An integration approach between bottom-hole pressure data and surface self-potential measurements was used to perform simultaneous inversion for the permeability structure. The self-potential method, a cost-effective geophysical technique, allows for the inversion of subsurface self-potential sources based on the underlying resistivity structure. Through a series of synthetic experiments, we demonstrate that combining borehole pressure data with surface self-potential measurements significantly enhances reservoir characterization, providing more robust and accurate subsurface models. By using the resolution matrix, we further confirm that the solution achieves higher accuracy when both data sets are integrated. This approach not only improves the precision of reservoir mapping but also reduces the uncertainty typically associated with traditional methods, offering a more efficient and reliable tool for optimizing production performance.

Characterization of cracks is a key issue in shale oil and gas that have become increasingly important in the hydrocarbon industry. Seismic exploration is frequently employed for the characterization of cracks in shale reservoirs. However, the accurate interpretation of seismic data for characterizing cracks in shale reservoirs remains a significant challenge, primarily due to an insufficient understanding of how subsurface pressure affects the anisotropic elastic properties of cracked shales. To address this knowledge gap, this study systematically investigates the effects of confining pressure on the anisotropic elastic properties of cracked artificial shales, with a specific focus on decoupling the distinct roles of background porosity and crack porosity. The five anisotropic elastic velocities were measured on manufactured shale samples with varying crack and background porosity, respectively, and the corresponding anisotropic parameters, Young's moduli and Poisson's ratios were derived as a function of confining pressure. The results demonstrate that the influence of crack porosity on reducing the velocities and on enhancing the elastic anisotropy is significantly more pronounced than that of background porosity. Notably, the velocities across the cracks, V_p(0°) and V_sh(0°), exhibit the greatest sensitivity to pressure changes, especially in samples with high crack porosity. Consequently, all the anisotropic parameters reduce exponentially with increasing confining pressure, with the reduction being most significant in shales with either the lowest background porosity or the highest crack porosity. The pressure-dependent geomechanical properties (Young's moduli and Poisson's ratios) reveal that the direction parallel to cracks remains the most favourable path for hydraulic fracturing, particularly under low confining pressure and in rocks with high crack porosity. These findings provide critical insights for improving the quantitative interpretation of seismic data for characterizing cracks and for optimizing hydraulic fracturing design in shale reservoirs.

Enhancing seismic data resolution is a crucial step for geological interpretation and imaging. Deep learning-driven resolution enhancement primarily depends on sophisticated network architectures and extensive datasets. A lightweight seismic super-resolution model based on contrastive learning and knowledge distillation is proposed. Knowledge distillation is implemented by training a compact student network to mimic a powerful teacher model, thereby reducing reliance on extensive datasets and complex architectures. Contrastive learning is leveraged to align the bottleneck features encoded from the teacher network with the ones from the student network across different noisy inputs. The student network's total loss comprises a supervised loss with ground-truth labels, a distillation loss with the teacher's pseudo-labels and a feature-matching loss derived from the bottleneck features of both networks. The comparative experiments were conducted on four field datasets and 3200 pairs of slices extracted from 800 pairs of synthetic three-dimensional seismic cubes. Experimental results demonstrate that the proposed model achieves similar to or better performance than the comparison models for noise suppression and weak signal recovery, even with only $��6.8�\%�$ parameters and $��37.5�\%�$ training data compared to the reference model.

The solution of magnetotelluric equations is used to determine the apparent resistivity and to model the electromagnetic field's behaviour within the Earth. In this paper, we extend the scaled boundary finite element method (SBFEM) to compute the solutions of magnetotelluric equations. The salient features of the proposed framework are that internal features and boundaries are captured through a quadtree decomposition. The SBFEM handles the resulting hanging nodes as a part of local refinement efficiently without needing additional constraints or shape functions. Further, we employ patterns to speed up the computations of the essential matrices without compromising accuracy. The results from the present approach are compared with other approaches, and it is seen that the SBFEM framework is not only efficient but also accurate. The efficacy and robustness are demonstrated with a few examples.

Multiples are generally considered coherent noise in conventional seismic migration. If they are not appropriately separated or eliminated from the observed reflection data, it can result in significant artefacts of the migration image, which will adversely affect subsequent structural interpretation and reservoir description. Inspired by the state-of-the-art model-driven deep learning methods, we have proposed a self-supervised deep-learning approach for multiple elimination. The proposed method contains two parts. The first one is that we use the conventional multiple prediction approach to predict the initial surface-related multiple reflections. The second part is to build a multiple-elimination model based on a deep neural network. In the deep neural network, the input is set as the predicted initial surface-related multiples from a conventional method and the label is set as the observed reflection data with primaries and multiples. Therefore, the proposed approach is a kind of self-supervised deep-learning multiple-elimination model. The deep neural network component of our proposed approach can be interpreted as a corrector in conventional methods that performs amplitude and phase correction on predicted multiples. Moreover, we combine the advantages of L₁ and L₂ loss functions and introduce the attention mechanism to improve the inversion efficiency and accuracy of self-supervised deep networks. Through some experiments with synthesized and field data, we demonstrate that the proposed self-supervised deep-learning approach can effectively and efficiently eliminate multiples from the observed data. It excels in both accuracy and efficiency compared to traditional method.

We propose a novel physics-guided deep learning framework for geophysical inversion that integrates Langevin Monte Carlo (LMC) sampling to quantify uncertainties in model parameters. A statistical sampling strategy is employed to enhance computational efficiency by reducing the number of required samples while preserving diversity and informativeness. The training data for the supervised learning networks are iteratively expanded with outputs from a stochastic sampler and their corresponding observed responses, ensuring representative coverage of the model space. The Jensen–Shannon divergence is adopted as the loss function for training the network model, in which the Gaussian assumption is applied to enable analytical computation. The developed workflow is evaluated on reservoir porosity inversion, where it successfully reconstructs porosity patterns in the subsurface, yielding results that closely match the reference model. Compared to traditional LMC algorithm applied to the entire data cube, the proposed approach attains substantial computational efficiency by leveraging an active learning strategy that identifies and utilizes a limited yet representative subset of the observations. The results demonstrate the effectiveness of the proposed method, highlighting its potential for application to a wide range of geophysical inverse problems.

Seismic inversion can effectively establish the connection between seismic data and underground reservoir parameters. Aiming at the current problem of low accuracy of deterministic inversion, a seismic attribute-oriented deterministic inversion method is proposed. The method is similar to most inversion algorithms and consists of two parts: modelling and inversion. The innovation of the model lies in extracting seismic attributes and learning the mapping between the seismic attributes of the well-side and the parameters to be inverted based on the support vector regression (SVR) algorithm. Then, the mapping relationship is used to realize the modelling of the parameters to be inverted in the well-free area. Under the constraints of this model, seismic inversion is implemented through the Markov Chain Monte Carlo (MCMC) approach, yielding inversion results that exhibit strong consistency with the corresponding seismic responses. As the multi-trace structural attributes contain more high-frequency information, the resolution of the parameters to be inverted based on this model is also higher. We continue to conduct inversion tests using post-stack seismic data. The results show that seismic attribute-oriented inversion has a significant advantage in inversion resolution over partially deterministic inversion algorithms (model-based inversion, sparse spike inversion, etc.).