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Joint Self-Potential and Fluid Flow Inversion for Imaging Permeability Structure and Detecting Fractures 关节自电位与流体流动反演在渗透率结构成像与裂缝探测中的应用
IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Pub Date : 2026-01-26 DOI: 10.1111/1365-2478.70118
Saleh Al Nasser

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.

准确探测地下储层裂缝位置和渗透率结构,对优化生产动态具有重要意义。在地下地下水或油气储层中实现峰值性能取决于创建精确的地图,该地图详细描述了从历史匹配过程中获得的储层特征。然而,这个过程需要重复的正演模拟,直到结果与历史生产数据一致,通常会消耗大量资源,并可能产生非独特的油藏模型。采用井底压力数据与地面自电位测量数据相结合的方法,对渗透率结构进行同步反演。自电位法是一种经济有效的地球物理技术,可以根据下伏电阻率结构反演地下自电位源。通过一系列综合实验,我们证明将井眼压力数据与地面自电位测量相结合可以显著增强储层特征,提供更可靠、更准确的地下模型。通过使用分辨率矩阵,我们进一步证实了当两组数据集集成时,解具有更高的精度。该方法不仅提高了储层测绘的精度,而且减少了传统方法的不确定性,为优化生产性能提供了更有效、更可靠的工具。
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引用次数: 0
Pressure-Dependent Anisotropic Elastic Properties of Cracked Artificial Shale With Varying Crack and Background Porosity 裂隙与背景孔隙度变化的裂隙人工页岩压力相关各向异性弹性特性
IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Pub Date : 2026-01-21 DOI: 10.1111/1365-2478.70136
Tongcheng Han, Zixuan Du

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, Vp(0°) and Vsh(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.

裂缝的表征是页岩油气中的一个关键问题,在油气工业中已变得越来越重要。地震勘探是页岩储层裂缝表征的常用方法。然而,准确解释页岩储层裂缝特征的地震数据仍然是一个重大挑战,主要是因为人们对地下压力如何影响裂缝页岩的各向异性弹性特性了解不足。为了解决这一知识差距,本研究系统地研究了围压对裂缝人造页岩各向异性弹性特性的影响,特别关注了背景孔隙度和裂缝孔隙度的不同作用。在不同裂缝和背景孔隙度的人造页岩样品上分别测量了5种各向异性弹性速度,并推导了相应的各向异性参数、杨氏模量和泊松比作为围压的函数。结果表明,裂纹孔隙度对降低速度和增强弹性各向异性的影响要明显大于背景孔隙度。值得注意的是,通过裂纹的速度Vp(0°)和Vsh(0°)对压力变化表现出最大的敏感性,特别是在高裂纹孔隙率的样品中。因此,各向异性参数随围压的增加呈指数级降低,且在背景孔隙度最低或裂缝孔隙度最高的页岩中降低最为显著。与压力相关的地质力学特性(杨氏模量和泊松比)表明,平行于裂缝的方向仍然是水力压裂最有利的路径,特别是在低围压和高裂缝孔隙度的岩石中。这些发现为改进地震数据的定量解释、裂缝特征和优化页岩储层水力压裂设计提供了重要见解。
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引用次数: 0
CKDSR: Seismic Super-Resolution Through Contrastive Knowledge Distillation 基于对比知识蒸馏的地震超分辨率
IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Pub Date : 2026-01-20 DOI: 10.1111/1365-2478.70129
Yun-Peng Shi, Lin-Rong Wang, Fan Min

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%$6.8%$ parameters and 37.5%$37.5%$ training data compared to the reference model.

提高地震资料的分辨率是地质解释和成像的关键步骤。深度学习驱动的分辨率增强主要依赖于复杂的网络架构和广泛的数据集。提出了一种基于对比学习和知识蒸馏的轻量级地震超分辨模型。知识蒸馏是通过训练一个紧凑的学生网络来模拟一个强大的教师模型来实现的,从而减少了对大量数据集和复杂架构的依赖。利用对比学习将来自教师网络的瓶颈特征编码与来自不同噪声输入的学生网络的瓶颈特征进行对齐。学生网络的总损失包括一个带有真值标签的监督损失,一个带有教师伪标签的蒸馏损失,以及一个来自两个网络的瓶颈特征的特征匹配损失。对比实验采用4个现场数据集和从800对合成三维地震立方体中提取的3200对切片进行。实验结果表明,该模型在噪声抑制和弱信号恢复方面达到了与参考模型相似或更好的性能,即使参数仅为6.8%,训练数据仅为37.5%。
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引用次数: 0
Vector-Based and Machine Learning Approaches for Pore Network Parameters Analysis 基于向量和机器学习的孔隙网络参数分析方法
IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Pub Date : 2026-01-19 DOI: 10.1111/1365-2478.70117
José Frank V. Gonçalves, José J. S. de Figueiredo, João Rafael B. S. Da Silveira, Pedro T. P. Aum, Daniel N. N. Da Silva

Accurate characterization of pore structures in carbonate rocks is critical for evaluating fluid flow and storage capacity in subsurface reservoirs, a key concern in geophysical exploration and reservoir engineering. This study proposes a hybrid digital rock physics workflow that integrates deep learning–based segmentation, vectorial geometric analysis and clustering techniques to investigate pore-scale features using x-ray micro-computed tomography at resolutions of 22 and 42 μ$mu$ m. A convolutional neural network (CNN) enhances the segmentation ofcomplex pore geometries, addressing the limitations of conventional thresholding methods. To estimate the representative elementary volume, two-dimensional porosity (ϕ$phi$) distributions were integrated into three-dimensional space using Riemannian methods. Pore connectivity (Z¯$bar{Z}$) was quantified via the coordination number (Z$Z$), derived from a vector-based analysis of local tangents and orthogonals, enabling precise identification of throats and pore networks. CNN models were trained on two carbonate samples (IL033 and IL636), achieving training accuracies of 0.9850 and 0.9914 and validation accuracies of 0.9854 and 0.9918, respectively. Total porosity (ϕt$phi _t$) estimates from the CNN and classical segmentation approaches were compared to experimental data, with the deep learning approach showing superior performance, especially in capturing isolated or poorly connected pores at higher resolutions. This integrated methodology offers a powerful framework for quantifying microstructural heterogeneity and its influence on pore connectivity and geometry, contributing to more realistic geophysical modelling and reservoir simulation.

碳酸盐岩孔隙结构的准确表征是评价地下储层流体流动和储集能力的关键,是地球物理勘探和储层工程研究的重点。该研究提出了一种混合数字岩石物理工作流程,该工作流集成了基于深度学习的分割、矢量几何分析和聚类技术,利用分辨率为22和42 μ $mu$ m的x射线微计算机断层扫描研究孔隙尺度特征。卷积神经网络(CNN)增强了复杂孔隙几何形状的分割。解决传统阈值方法的局限性。为了估计具有代表性的基本体积,使用黎曼方法将二维孔隙度(ϕ $phi$)分布整合到三维空间中。孔隙连通性(Z¯$bar{Z}$)通过配位数(Z $Z$)进行量化,配位数来源于基于矢量的局部切线和正交线分析,从而能够精确识别喉道和孔隙网络。在两种碳酸盐样品(IL033和IL636)上训练CNN模型,训练精度分别为0.9850和0.9914,验证精度分别为0.9854和0.9918。将CNN和经典分割方法估计的总孔隙度(ϕ t $phi _t$)与实验数据进行比较,深度学习方法表现出卓越的性能,特别是在以更高分辨率捕获孤立或连接不良的孔隙方面。这种综合方法为量化微观结构非均质性及其对孔隙连通性和几何形状的影响提供了强大的框架,有助于更真实的地球物理建模和油藏模拟。
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引用次数: 0
On the Scaled Boundary Finite Element Method for Magnetotelluric Modelling 大地电磁建模的尺度边界有限元法
IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Pub Date : 2026-01-19 DOI: 10.1111/1365-2478.70122
VS Suvin, Sachin Gunda, Ean Tat Ooi, Chongmin Song, Sundararajan Natarajan

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.

大地电磁方程的解用于确定视电阻率和模拟地球内部的电磁场行为。本文将尺度边界有限元法推广到大地电磁方程的求解中。该框架的显著特点是通过四叉树分解捕获内部特征和边界。SBFEM在不需要附加约束或形状函数的情况下,有效地将产生的悬挂节点作为局部细化的一部分进行处理。此外,我们使用模式来加速基本矩阵的计算,而不影响精度。将本文方法的结果与其他方法进行了比较,结果表明SBFEM框架不仅有效而且准确。算例验证了该方法的有效性和鲁棒性。
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引用次数: 0
Multiple Elimination Using Model-Driven Self-Supervised Learning With an Attention Mechanism 基于注意机制的模型驱动自监督学习多重消除
IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Pub Date : 2026-01-19 DOI: 10.1111/1365-2478.70128
Ying shi, Peinan Bao, Wei Zhang

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.

在常规地震偏移中,多次波通常被认为是相干噪声。如果没有从观测到的反射数据中适当地分离或消除它们,可能会导致迁移图像出现明显的伪影,这将对后续的构造解释和储层描述产生不利影响。受最先进的模型驱动深度学习方法的启发,我们提出了一种用于多重消除的自监督深度学习方法。该方法包括两个部分。第一种方法是利用传统的多次反射预测方法来预测与地表相关的初始多次反射。第二部分是建立基于深度神经网络的多重消除模型。在深度神经网络中,输入被设置为传统方法预测的初始表面相关倍数,标签被设置为具有原色和倍数的观测反射数据。因此,所提出的方法是一种自监督深度学习多重消除模型。我们提出的方法的深度神经网络组件可以被解释为传统方法中的校正器,对预测的倍数进行幅度和相位校正。结合L1和L2损失函数的优点,引入注意机制,提高自监督深度网络的反演效率和精度。通过对综合数据和现场数据的实验,我们证明了所提出的自监督深度学习方法可以有效地消除观测数据中的倍数。与传统方法相比,该方法在精度和效率方面都具有优势。
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引用次数: 0
Efficient Bayesian Active Learning with Langevin Dynamics for Reservoir Porosity Inversion 基于Langevin动力学的高效贝叶斯主动学习储层孔隙度反演
IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Pub Date : 2026-01-18 DOI: 10.1111/1365-2478.70130
Runhai Feng, Daniele Colombo, Ersan Turkoglu, Ernesto Sandoval-Curiel

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.

我们提出了一种新的物理指导的地球物理反演深度学习框架,该框架集成了Langevin Monte Carlo (LMC)采样来量化模型参数中的不确定性。采用统计抽样策略,在保持多样性和信息量的同时,减少了所需样本的数量,提高了计算效率。监督学习网络的训练数据通过随机采样器的输出及其相应的观察响应进行迭代扩展,以确保模型空间的代表性覆盖。采用Jensen-Shannon散度作为损失函数训练网络模型,其中采用高斯假设进行解析计算。开发的工作流程在储层孔隙度反演中进行了评估,成功地重建了地下孔隙度模式,所得结果与参考模型非常吻合。与应用于整个数据立方体的传统LMC算法相比,所提出的方法通过利用主动学习策略来识别和利用有限但具有代表性的观测子集,从而获得了可观的计算效率。结果证明了该方法的有效性,突出了其在广泛的地球物理反演问题中的应用潜力。
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引用次数: 0
Robust Subsurface Velocity and Density Estimation via Seismic Inversion With Global Misfit Minimisation and Full-Offset Moveout 基于全球失配最小化和全偏移位移的地震反演鲁棒地下速度和密度估计
IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Pub Date : 2026-01-13 DOI: 10.1111/1365-2478.70124
Vita Kalashnikova, Rune Øverås
<div> <p>Accurate estimation of seismic velocity and density models is essential for subsurface imaging and characterisation. We propose a global optimisation framework to estimate non-linked P-wave velocity (<i>V<sub>p</sub></i>) and density (<i>ρ</i>) for post-stack seismic data, and <i>V<sub>p</sub></i>, <i>ρ</i> and S-wave velocity (<i>V<sub>s</sub></i>) for pre-stack data, by minimising the misfit between non-corrected normal moveout (non-NMO) observed and synthetic seismic gathers. We demonstrate that a non-linear, underdetermined and complex problem can be addressed by introducing an additional constraint on one of the parameters, using non-linear forward modelling combined with global optimisation algorithms. The synthetic seismic gathers are iteratively generated by randomly and simultaneously updating initial models. Updates are accepted on the basis of the simulated annealing method, a global optimisation technique that helps to prevent entrapment in local minima. Optimisation is performed by minimising an L2-norm misfit function. For the pre-stack case, the observed seismic data are a real gather. For the post-stack case, the observed gather is formed from the full stacked seismic trace, taken as a near trace, and the reflectors are spread along moveout curves that are computed from the smoothed log of <i>V<sub>p</sub></i>. A two-parameter search (<i>V<sub>p</sub></i>, <i>ρ</i>) is launched in the post-stack case, whereas a three-parameter search (<i>V<sub>p</sub></i>, <i>ρ</i> and <i>V<sub>s</sub></i>) is used when pre-stack data are available, with both starting from smoothed initial models. To reduce the number of iterations by at least one order of magnitude and to increase computational efficiency, initial estimates of the <i>V<sub>p</sub></i> and <i>ρ</i> models can first be obtained from the post-stack process using well logs. Then, the full three-parameter search is performed, starting with initially estimated models for <i>V<sub>p</sub></i>, <i>ρ</i> and smoothed <i>V<sub>s</sub></i>. The proposed methodologies offer an approach for estimating elastic properties and for overcoming the limitations of conventional seismic inversion. The approach eliminates reliance on regression techniques, which often oversimplify the complex, nonlinear relationships between seismic data and subsurface properties; avoids linearisation of the inversion process, which can introduce errors again due to the inherently nonlinear nature of geological properties; and bypasses the need for normal moveout (NMO) correction, which can cause amplitude stretching and distort critical amplitude information, because the algorithm utilises the moveout. By working with fully migrated 1D gathers, we assume a constant velocity across all offsets, which allows us to bypass full wave-equation modelling while maintaining acceptable accuracy. Additionally, the framework supports the implementation of alternative global optimisation strategies.</p>
准确估计地震速度和密度模型对地下成像和表征至关重要。我们提出了一个全局优化框架,通过最小化观测到的非校正正常移动(non-NMO)和合成地震聚集之间的不匹配,来估计叠后地震数据的非链接纵波速度(Vp)和密度(ρ),以及叠前数据的Vp、ρ和s波速度(Vs)。我们证明了一个非线性的、不确定的和复杂的问题可以通过在其中一个参数上引入额外的约束来解决,使用非线性正演建模结合全局优化算法。合成地震道是通过随机同步更新初始模型来迭代生成的。在模拟退火方法的基础上接受更新,这是一种全局优化技术,有助于防止陷入局部最小值。优化是通过最小化l2范数错配函数来实现的。对于叠前情况,观测到的地震数据是真实的收集。对于叠后情况,观测到的聚集是由全叠地震道形成的,作为近道,反射体沿着从Vp的光滑对数计算得到的移出曲线扩散。在叠后情况下启动双参数搜索(Vp, ρ),而在叠前数据可用时使用三参数搜索(Vp, ρ和Vs),两者都从平滑的初始模型开始。为了将迭代次数减少至少一个数量级并提高计算效率,可以首先使用测井曲线从叠后过程中获得Vp和ρ模型的初始估计。然后,从Vp、ρ和平滑vs的初始估计模型开始,进行完整的三参数搜索。所提出的方法提供了一种估计弹性特性的方法,并克服了传统地震反演的局限性。该方法消除了对回归技术的依赖,回归技术通常会过度简化地震数据与地下属性之间复杂的非线性关系;避免了反演过程的线性化,由于地质性质固有的非线性性质,线性化可能再次引入误差;并且绕过了正常移出(NMO)校正的需要,这可能导致幅度拉伸和扭曲关键幅度信息,因为该算法利用了移出。通过处理完全迁移的一维数据集,我们假设所有偏移量的速度都是恒定的,这使得我们可以绕过完整的波动方程建模,同时保持可接受的精度。此外,该框架支持可选的全局优化策略的实施。
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引用次数: 0
Pre-Stack Seismic Inversion of Dual-Porosity Geometry in Deep Coalbed Methane Reservoirs Based on Decoupled Equivalent Medium Theory 基于解耦等效介质理论的深部煤层气储层双重孔隙几何结构叠前地震反演
IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Pub Date : 2026-01-10 DOI: 10.1111/1365-2478.70131
Fei Gong, Zhaoji Zhang, Suping Peng, Qiang Guo, Guowei Wang

Seismic inversion quantitatively extracts reservoir properties from seismic data, which has gained increasing attention in assisting the exploration and evaluation of deep coalbed methane (DCBM) reservoirs. However, the accuracy of seismic prediction is limited because the DCBM reservoirs exhibit complex pore geometries governed by a dual-porosity system. To address this limitation, the study presents a dual-porosity parameter seismic inversion method based on decoupled equivalent medium theory. A dual-porosity rock physics model is constructed and then decoupled to derive a linear forward operator that links matrix porosity, crack porosity and crack aspect ratio to the corresponding elastic parameters. To account for lithological variability, a Gaussian mixture model is employed to describe the joint prior probability distribution of dual-porosity parameters. Well-log data are applied to invert matrix porosity, crack porosity and crack aspect ratio, which serve as prior constraints in the iterative Bayesian inversion framework, thereby enhancing the stability and accuracy of the forward operator. By explicitly treating dual-porosity parameters as inversion targets, the proposed method effectively captures the spatial heterogeneity of pore geometries in DCBM reservoirs. Borehole-side synthetic seismic gather validation results demonstrate that the proposed approach significantly enhances inversion accuracy compared with conventional equivalent-porosity inversion methods. The application to pre-stack seismic data demonstrates the ability of the method to capture the dual-porosity geometry.

地震反演从地震资料中定量提取储层物性,在辅助深部煤层气储层勘探评价方面越来越受到重视。然而,由于DCBM储层具有复杂的孔隙结构,受双重孔隙系统的控制,地震预测的准确性受到限制。针对这一局限性,提出了一种基于解耦等效介质理论的双孔隙度参数地震反演方法。建立双孔隙度岩石物理模型,对其进行解耦,推导出将基质孔隙度、裂缝孔隙度和裂缝纵横比与相应弹性参数联系起来的线性正演算子。为了考虑岩性的可变性,采用高斯混合模型来描述双孔隙度参数的联合先验概率分布。利用测井数据反演矩阵孔隙度、裂缝孔隙度和裂缝纵横比,作为迭代贝叶斯反演框架的先验约束,提高了正演算子的稳定性和准确性。该方法明确将双重孔隙度参数作为反演目标,有效捕捉了DCBM储层孔隙几何形状的空间非均质性。井侧合成地震集验证结果表明,与传统等效孔隙度反演方法相比,该方法显著提高了反演精度。叠前地震数据的应用表明,该方法能够捕获双重孔隙度的几何形状。
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引用次数: 0
Seismic Attribute-Oriented Post-Stack Seismic Inversion 面向地震属性的叠后地震反演
IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Pub Date : 2026-01-07 DOI: 10.1111/1365-2478.70125
Ying Lin, Zhangbo Xiao, Siyuan Chen, Ming Zhang, Yue Zhao, Wenjun Xing

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.).

地震反演可以有效地建立地震资料与地下储层参数之间的联系。针对目前确定性反演精度低的问题,提出了一种面向地震属性的确定性反演方法。该方法与大多数反演算法相似,由建模和反演两部分组成。该模型的创新之处在于提取地震属性,并基于支持向量回归(SVR)算法学习井侧地震属性与待反演参数之间的映射关系。然后,利用映射关系实现非井区待反演参数的建模。在该模型的约束下,采用马尔可夫链蒙特卡罗(MCMC)方法进行地震反演,反演结果与相应的地震响应具有较强的一致性。由于多道结构属性包含更多的高频信息,基于该模型反演的参数分辨率也更高。我们继续利用叠后地震数据进行反演试验。结果表明,面向地震属性的反演在反演分辨率上优于部分确定性的反演算法(基于模型的反演、稀疏尖峰反演等)。
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Geophysical Prospecting
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