Prestack Seismic Inversion Research Articles

Porosity prediction from prestack seismic data via deep learning: incorporating a low-frequency porosity model

Abstract Porosity prediction from seismic data is of considerable importance in reservoir quality assessment, geological model building, and flow unit delineation. Deep learning approaches have demonstrated great potential in reservoir characterization due to their strong feature extraction and nonlinear relationship mapping abilities. However, the reliability of porosity prediction is often compromised by the lack of low-frequency information in bandlimited seismic data. To address this issue, we propose incorporating a low-frequency porosity model based on geostatistical methodology, into the supervised convolutional neural network to predict porosity from prestack seismic angle gather and seismic inversion results. Our study demonstrates that the inclusion of the low-frequency porosity model significantly improves the reliability of porosity predictions in a heterogeneous carbonate reservoir. The low-frequency information can be compensated to enhance the network's capabilities of capturing the background porosity trend. Additionally, the blind well tests validate that considering the low-frequency constraint leads to stronger model prediction and generalization abilities, with the root mean square error of the two blind wells reduced by up to 34%. The incorporation of the low-frequency reservoir model in network training also remarkably enhances the geological continuity of seismic porosity prediction, providing more geologically reasonable results for reservoir characterization.

Azimuthal Prestack Seismic Anisotropic Inversion on a Deep and Tight Carbonate Reservoir From the North Potwar Basin of Pakistan

Summary Quantitative characterization of deep, tight, and heterogeneous reservoirs plays an important role in identifying hydrocarbon pathways for effective and optimal reservoir field development. In this case study, we used an azimuthal prestack seismic anisotropic inversion approach to estimate attributes of horizontal transverse isotropy (HTI) caused by a set of vertical fractures, oriented cracks, and stress. Anisotropic inversion facilitated the conversion of interface properties to the corresponding layer-based properties, which led to the quantitative interpretation of reservoir properties related to azimuthal variation in seismic amplitudes. To estimate the anisotropy magnitude and the direction of the isotropy axis in HTI media, elastic properties (P-impedance and Vp/Vs) obtained from prestack seismic inversion (using six azimuth × four angle stack) served as inputs. The isotropic low-frequency model (LFM) is used as the foundation of the inversion for all azimuths, and the anisotropy effects are later added by updating the model along the azimuths. The direction of the isotropy plane resulting from the anisotropic inversion is determined by using the maximum horizontal stress as a prior constraint, which eliminates any inherent uncertainty. The workflow used effectively characterized the orientation and density of fractures from the recently discovered oilfield reservoirs of the Paleocene (Lockhart) formation located in Pakistan’s north Potwar Basin. It also helped improve the prediction accuracy for fractures in the study area. According to the observations (fractures) from the exploratory drilled well (D1) in the tight carbonate (Lockhart) reservoir, a significant amount of anisotropy magnitude is observed. This provides the basis for hydrocarbon exploration, field development, and reliable drilling decisions.

Lithology and Porosity Distribution of High-Porosity Sandstone Reservoir in North Adriatic Using Machine Learning Synthetic Well Catalogue

Reservoir characterization on offshore fields often faces specific challenges due to limited or unevenly distributed well data. The object of this study is the North Adriatic poorly consolidated clastic reservoir characterized by high porosity. The seismic data indicate notable differences in reservoir quality spatially. The only two wells on the field drilled the best reservoir area. Seismic data, seismic reservoir characterization, and accurate integration with scarce well data were crucial. This paper demonstrates how the application of machine learning algorithms, specifically a Deep Forward Neural Network (DFNN), and the incorporation of pseudo-well data into the reservoir characterization process can improve reservoir properties prediction. The methodology involves creating different reservoir porosity and thickness scenarios using pseudo-well data, synthetic pre-stack seismic data generation, seismic inversion, and DFNN utilization to improve porosity prediction. This study also highlights the importance of lithology discrimination in the geological model to better constrain reservoir properties distribution in the entire reservoir volume. Facies probability analysis was utilized to define interdependence between litho–fluid classes established from the well data and acoustic impedance volume. Apart from the field well data, seismic inversion results, and DFNN porosity volume as main inputs, acknowledgments from the neighboring fields also had an important role.

Coal Structure Prediction Based on Type-2 Fuzzy Inference System for Multi-Attribute Fusion: A Case Study in South Hengling Block, Qinshui Basin, China

The accurate prediction of coal structure is important to guide the exploration and development of coal reservoirs. Most prediction models are interpreted for a single sensitive coal seam, and the selection of sensitive parameters is correlated with the coal structure, but they ignore the interactions between different attributes. Part of it introduces the concept of the geological strength index (GSI) of coal rocks in order to achieve a multi-element macroscopic description and quantitative characterization of coal structure; however, the determination of coal structure involves some uncertainties among the properties of coal, such as lithology, gas content and tectonic fracture, due to their complex nature. Fuzzy inference systems provide a knowledge discovery process to handle uncertainty. The study shows that a type-2 fuzzy inference system (T2-FIS) with multi-attribute fusion is used to effectively fuse pre-stack and post-stack seismic inversion reservoir parameters and azimuthal seismic attribute parameters in order to produce more accurate prediction results for the Hengling block in the Shanxi area. The fuzzy set rules generated in this paper can provide a more reliable prediction of coal structure in the GSI system. The proposed system has been tested on various datasets and the results show that it is capable of providing reliable and high-quality coal structure predictions.

Multiple elastic attribute fusion approach based on affinity propagation clustering strategy for gas hydrate reservoir identification

Pre-stack seismic inversion based on sensitive elastic parameters is critical in reservoir lithology prediction and geofluid identification. The ability of a single elastic attribute to identify a reservoir depends on its sufficient sensitivity to distinguish the target reservoir from the surrounding sediments. In general, high-dimensional data space composed of multiple elastic attributes is more conducive to describing reservoir characteristics. Therefore, a multiple elastic attribute fusion method using affinity propagation clustering strategy for gas hydrate reservoir identification is proposed. Rock-physics modeling is the most effective tool to determine the influence of microscopic physical parameters on macroscopic elastic response and to quantitatively evaluate the sensitivity of elastic attributes. Consequently, a rock-physics model of hydrate sediments considering the non-negligible shear properties of hydrates is constructed to clarify reservoir-sensitive elastic parameters. Additionally, a clustering evaluation indicator is defined to determine the optimal data clustering dimension in terms of feasibility and economy of the proposed method, and to avoid bias in the results due to data redundancy. It is shown that the 3D elastic attribute space consisting of shear modulus, Young's modulus, and S-wave velocity has the best discrimination ability for hydrate reservoirs. The logging data are used to verify the feasibility and effectiveness of the proposed method. Finally, the hydrate reservoir development is accurately discriminated by using the multiple elastic attributes yield from the pre-stack seismic inversion and combined with the fusion strategy.

Fast pre-stack multi-channel inversion constrained by seismic reflection features

Classical multi-channel technology can significantly reduce the pre-stack seismic inversion uncertainty, especially for complex geology such as high dipping structures. However, due to the consideration of complex structure or reflection features, the existing multi-channel inversion methods have to adopt the highly time-consuming strategy of arranging seismic data trace-by-trace, limiting its wide application in pre-stack inversion. A fast pre-stack multi-channel inversion constrained by seismic reflection features has been proposed to address this issue. The key to our method is to re-characterize the reflection features to directly constrain the pre-stack inversion through a Hadamard product operator without rearranging the seismic data. The seismic reflection features can reflect the distribution of the stratum reflection interface, and we obtained them from the post-stack profile by searching the shortest local Euclidean distance between adjacent seismic traces. Instead of directly constructing a large-size reflection features constraint operator advocated by the conventional methods, through decomposing the reflection features along the vertical and horizontal direction at a particular sampling point, we have constructed a computationally well-behaved constraint operator represented by the vertical and horizontal partial derivatives. Based on the Alternating Direction Method of Multipliers (ADMM) optimization, we have derived a fast algorithm for solving the objective function, including Hadamard product operators. Compared with the conventional reflection features constrained inversion, the proposed method is more efficient and accurate, proved on the Overthrust model and a field data set.

A prestack seismic inversion method constrained by facies-controlled sedimentary structural features

The inversion of thin-bed reservoirs using seismic data is important for precise reservoir prediction. Most of the current thin-bed reservoir inversion methods are constrained by facies or sedimentary characteristics in reservoirs. The accuracy of the sedimentary characteristics severely affects the precision of the inversion results. There are differences in sedimentary patterns among different facies, but the same facies have similar sedimentary characteristics, and the similarity brings the sparsity of sedimentary structure within a facies. Thus, various representations should be used to describe the differences in the sedimentary patterns between facies. After analyzing the sparse representation (SR) patterns for different facies, a prestack seismic inversion method constrained by facies-controlled sedimentary structural features is developed. Our method first uses seismic attributes that provide rich details of seismic features to divide facies. Then, the sedimentary structural features within each facies are extracted. The inversion results are represented by structural features in the corresponding facies. We have used the represented results as the initial value of the inversion to iterate. Using this iterative inversion framework, we obtain results that are consistent with the sedimentary features of each facies. The representation of reservoir parameters by different SR patterns can avoid the mixing of sedimentary structural features in different facies. Synthetic and field examples are used to verify the effectiveness of our method. The method obtains inversion results that are consistent with seismic data and the distribution of thin-bed in well logs. Therefore, the method can be successfully applied to thin-bed sandstone reservoirs.

Domain knowledge-guided data-driven prestack seismic inversion using deep learning

The essential task of reservoir characterization is to predict elastic/petrophysical parameters or facies from observed seismic data and evaluate their uncertainty. Deep learning-based methods gain great popularity because of their powerful ability to obtain exact solutions for geophysical inverse problems. However, those deep learning methods that use seismic data as the only input lead to difficult training and unstable inversion results (i.e., transverse discontinuity or geologic unreliability). In such circumstances, it is beneficial if prior knowledge of the model domain can be incorporated into the deep learning framework. Therefore, we have developed prior-based loss functions in the learning step of the deep learning models to ensure that the predictions find low errors on the training sets and that their results are consistent with the known prior. In addition, the Monte Carlo dropout (MC-dropout) technique is introduced for the quantitative assessment of the uncertainty of the prediction results. We determine the effectiveness of our framework in the application of prestack seismic inversion, in which the initial model built from well-log interpolation is used to design the prior-based loss function. We first perform extensive experiments on the synthetic data and find that our method can yield more stable and reliable results compared with traditional methods. Combined with the transfer learning strategy, the application to real data further demonstrates that our deep learning framework obtains more reasonable inversion results with more horizontal continuity and greater geologic reliability than traditional approaches.

Adaptive Feature Map-Guided Well-Log Interpolation

As an irreplaceable quantitative interpretation method, prestack seismic inversion enables the effective estimation of subsurface elastic parameters for reservoir prediction. However, for the model-driven prestack seismic inversion, the band-limited characteristics and noise interference of observed seismic data result in its high dependence on the initial models. This suggests that reasonable initial models act as a supplement to reliable variation trends in formation and can reduce the non-uniqueness of inversion results. In this article, we introduce a well-log interpolation method with a feature map-guided non-local means algorithm, which is for establishing high-fidelity initial models used for prestack seismic inversion. First, we briefly review the basic theory of general model-driven prestack seismic inversion. Then, we use dictionary learning to split the poststack seismic record into patches, and represent them with sparse vectors, instead of directly using seismic record. The advantage of dictionary learning is that it can adaptively extract useful signals from noisy observed data and provide fine structures by sparse reconstruction. Therefore, the proposed feature extraction method can improve the noise immunity and reliability of the well-log interpolation. More accurate initial models are pre-constructed efficiently by our feature extraction method, which improves the reliability of prestack seismic inversion results. Two kinds of observed seismic data are used, including the poststack seismic record for well-log interpolation and prestack seismic data used for inversion. Synthetic and field data tests both demonstrate the favorable performance of the proposed well-log interpolation method. In summary, a novel and convenient initial model building approach is provided, which contributes to seismic exploration and geologic modeling.

AVO Inversion Based on Closed-Loop Multitask Conditional Wasserstein Generative Adversarial Network

Neural networks are commonly used for post-stack and pre-stack seismic inversion. With sufficient labelled data, the neural network-based seismic inversion results are more accurate than that use traditional seismic inversion methods. However, in the case of insufficient labeled data, the accuracy of neural networks-based seismic inversion results decreases and is even lower than those based on traditional inversion methods. In addition, the seismic inversion results based on neural networks generally suffer from lateral discontinuity. It further reduces the accuracy of the inversion results. To tackle these problems, we propose a pre-stack seismic amplitude variation with offset (AVO) inversion method based on Closed-Loop Multi-task conditional Wasserstein Generative Adversarial Network (CMcWGAN), which is a GAN-based AVO inversion method. CMcWGAN enables simultaneous and accurate inversion of P-wave velocity ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_p</i> ), S-wave velocity ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_s</i> ), and density ( ρ ). Moreover, it uses the low-frequency information of elastic parameters as a conditional input to alleviate the problem of lateral discontinuity in inversion results. Experimental results of simulated data show that the inversion results based on CMcWGAN have higher accuracy than those based on traditional AVO inversion methods. In addition, when the seismic angle gather is noisy, CMcWGAN has better robustness than the traditional methods. CMcWGAN can also obtain reasonable AVO inversion results in field seismic angle gather data.inversion results. Experimental results of simulated data show that the inversion results based on CMcWGAN have higher accuracy than those based on traditional AVO inversion method. In addition, when the seismic angle gather is noisy, CMcWGAN has better robustness than traditional method. CMcWGAN can also get reasonable AVO inversion results in field seismic angle gather data.

The 3-D Global Prestack Seismic Inversion in the Time–Frequency Mixed Domain

Traditional seismic inversion has the problems of low lateral resolution and poor anti-noise performance. To address these challenges, the advantages of inversion methods in time and frequency domains are combined; the Lp norm that can preserve more sparsity information is introduced into seismic inversion. Meanwhile, multi-trace simultaneous inversion has become the mainstream of inversion methods, which can effectively improve the quality of pre-stack seismic inversion. However, traditional multi-trace simultaneous inversion methods have high computational costs and are difficult to obtain the 3D result. Ensuring spatial continuity of inversion results is a challenging task. To address this issue, a 3D global pre-stack inversion method in the time-frequency mixed domain is proposed. The proposed method adds a frequency domain fidelity constraint the objective function of traditional time domain inversion to improve the resolution of the inversion results. To improve the computational efficiency of multi-trace simultaneous inversion, the anisotropic total variation regularization method under Lp norm constraint is employed. In addition, the 2D Sylvester equation is extended to 3D space to enable 3D global inversion. The method not only improves computational efficiency, but also ensures spatial continuity of the results. The stability and feasibility of the proposed method have been demonstrated through its application on both the 3D overthrust model and field pre-stack seismic data.

A Linearized Alternating Direction Method of Multipliers Algorithm for Prestack Seismic Inversion in VTI Media Using the Quadratic PP-Reflectivity Approximation

Periodic horizontal thin interlayers, or formations developing horizontally layered fractures in a homogeneous background, are commonly studied as a transverse isotropy with a vertical axis of symmetry (VTI) media. Anisotropy needs to be considered in predicting such special reservoirs, and can be inferred from the amplitude variation with offset (AVO) by the exact or linear reflectivity equations. However, the linear approximation is usually derived based on the assumptions of weak anisotropy and weak contrast, the accuracy suffers when the anisotropy or the contrast of two adjacent media increases. In addition, due to the high nonlinearity of the Graebner equation, the stability and efficiency of the exact inversion are limited. To address these issues, we propose a robust pre-stack seismic inversion for VTI media using quadratic PP-reflectivity approximation. Firstly, by deriving quadratic approximations for phase velocity and polarization, we analyze the advantage of the second-order terms at a higher-contrast interface. Then, based on perturbation theory, we derive a quadratic PP-reflectivity approximation from the exact equation. It consists of first- and second-order terms with isotropic and anisotropic components, and the accuracy is significantly improved even with enhanced anisotropy. Finally, we introduce the Hadamard product operator to linearly characterize the objective function, and develop an improved Alternating Direction Method of Multipliers (ADMM) algorithm to handle the nonlinearity of the quadratic equations. The feasibility and stability of the proposed method are tested on two datasets, that is, the logging curves used as numerical experiments and a field dataset from a shale reservoir.

An Unsupervised Inversion Method for Seismic Brittleness Parameters Driven by the Physical Equation

Brittleness is an important parameter characterizing the fracturing properties of shale reservoir, which can be predicted by the pre-stack seismic inversion. In order to overcome the low efficiency and ill-posed problems of the traditional pre-stack brittleness inversion, we propose a new unsupervised deep learning (DL) inversion method for seismic brittleness parameters based on the physical equation. This method integrates DL framework and the physical equation, and provides a DL inversion strategy without actual labels. We first input the original seismic data into the Fastformer network, and use the low-frequency model as the physical constraint to predict the brittle parameters. Then, the prediction results of brittleness parameters are sent to the forward modeling module (a linear approximation equation) to calculate the synthetic seismic data. Next, the error between the calculated seismic data and original seismic data is used to update the network prediction results. The network parameters are iteratively optimized to minimize the error, and the brittle prediction parameters are finally output. In the whole training process, it is not necessary to use the real brittle parameters as the labels. Through this method, the effect of approximate unsupervised learning is obtained. Finally, we apply the proposed method to the synthetic data and field data, and compared with the results inverted by the traditional L1 method. The experimental results show that the proposed method has higher inversion accuracy and efficiency than the traditional L1 method, which has a great potential in the practical application.

Hybrid-Driven High-Resolution Prestack Seismic Inversion

Hybrid-Driven High-Resolution Prestack Seismic Inversion

Generating Azimuth–Reflection Angle Gathers From Reverse Time Migration Using the High-Dimensional Local Phase Space Approximation of Seismic Wavefields

Amplitude-preserving angle gathers are ideal inputs for seismic prestack inversion. However, due to the limitation of computational efficiency, generating subsurface azimuth–reflection angle gathers from 3-D seismic imaging is still a very difficult task. In this article, we propose a new method to generate azimuth–reflection angle gathers from 3-D reverse time migration (RTM). The proposed method approximately reconstructs the source wavefield using high-dimensional wavelets and the excitation information. After using directional vectors to calculate the subsurface observation angles and applying the cross correlation imaging condition, we can generate azimuth–reflection angle gathers by angle binning. Without storing source wavefields or reconstructing source wavefields using boundary conditions, the proposed method has high computational efficiency. Numerical experiments on a synthetic model and a real marine seismic dataset demonstrate that compared with the excitation amplitude imaging condition, the proposed method can generate azimuth–reflection angle gathers with continuous complete events and high signal-to-noise ratio. The image quality and resolution of angle gathers are significantly improved. At the same time, the computational complexity does not increase much.

Direct Inversion Method of Brittleness Parameters Based on Reweighted Lp-Norm

Brittleness is an important factor that indicates shale properties, as well as fracturability, and it can be well-represented using the elasticity parameter Eρ. Seismic inversion allows direct access to the Eρ ofbrittleness parameters. Seismic inversion is a typical ill-posed problem that has an enormous multiplicity of solutions. In order to invert reservoir brittleness parameters more stably and reliably, a direct inversion method for determining brittleness parameters based on a reweighted Lp-norm is proposed, and the reweighted Lp method is introduced to brittleness parameter inversion for the first time. The alternating direction method of multipliers (ADMM) is used to establish the inversion structure and to optimize the objective function in blocks, which effectively improves the convergence speed. We first introduce a reweighted Lp method and establish a pre-stack inversion objective function based on the reweighted Lp method. Then, theoretical simulation data are applied to compare the inversion outcomes of the new method with those of the traditional method, and the effect of the method in this manuscript is verified. Finally, the feasibility of this method is further verified using actual data for experimental analysis. Through an analysis of the experimental results, we find that this method can be well-applied to seismic pre-stack inversion calculation and provides a new direct inversion method for the determination of brittleness parameters for exploration geophysics.

High resolution pre-stack seismic inversion using few-shot learning

We propose to use a Few-Shot Learning (FSL) method for the pre-stack seismic inversion problem in obtaining a high resolution reservoir model from recorded seismic data. Recently, artificial neural network (ANN) demonstrates great advantages for seismic inversion because of its powerful feature extraction and parameter learning ability. Hence, ANN method could provide a high resolution inversion result that are critical for reservoir characterization. However, the ANN approach requires plenty of labeled samples for training in order to obtain a satisfactory result. For the common problem of scarce samples in the ANN seismic inversion, we create a novel pre-stack seismic inversion method that takes advantage of the FSL. The results of conventional inversion are used as the auxiliary dataset for ANN based on FSL, while the well log is regarded the scarce training dataset. According to the characteristics of seismic inversion (large amount and high dimensional), we construct an arch network (A-Net) architecture to implement this method. An example shows that this method can improve the accuracy and resolution of inversion results.

Depth-domain angle and depth variant seismic wavelets extraction for prestack seismic inversion

Many prestack depth migration methods have been developed and widely used to generate depth-domain seismic images, resulting in a need for depth-domain prestack seismic inversion of the subsurface elastic properties for reservoir characterization. Time-domain inversion techniques often are used after the depth-domain data set is transformed to the time domain. We provide a new technique for directly inverting depth-domain prestack seismic data for subsurface elastic properties: P-impedance ([Formula: see text]), S-impedance ([Formula: see text]), and density ([Formula: see text]) in the depth domain. The proposed depth-domain workflow eliminates the need for time-depth/depth-time conversion, making it efficient and effective. Using a depth-wavenumber decomposition approach, the suggested workflow first extracts a collection of depth and angle varying wavelets to handle the potential nonstationarity of the depth-domain prestack seismic data. The extracted depth and angle variant wavelets are used in a basis pursuit inversion, which enhances the resolution of the inversion results. The workflow is tested on the Wenan 3D field data set, demonstrating its viability for practical applications.

AVA inversion for novel fluid indicator considering consolidation parameter

Abstract 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.

Seismic inversion and fracture prediction in tilted transversely isotropic media

Abstract The conventional Amplitude variation with offset (AVO) inversion has been mainly developed for the isotropic media and therefore it is generally inapplicable to the anisotropic fractured formations. A set of tilted fractures in isotropic medium can be regarded as a transversely isotropic (TTI) medium. The reflection coefficient equation in TTI media contains many parameters such as anisotropic parameters, velocities, azimuth and dip angles. The selection of objective functions can significantly affect its performance in searching for the optimal solution. The seismic inversion of the TTI medium remains challenging because of its many parameters, and is very complicated after conversion into solving function problems. The genetic algorithm (GA) provides a universal framework to solve the optimization problem in complex systems, and it is independent of the types and disciplines of problems. In this study, the GA-based seismic prestack inversion of the TTI medium is implemented by constructing the objective functions of anisotropic parameters based on the TTI medium reflection coefficient approximation. The inversion performance is shown to be satisfactory on the one-dimensional well logging model, modified Hess model and the field datum. Furthermore, fracture filling materials are well predicted according to the theories of the equivalent medium for fractured materials and fracture weakness. The inversion results demonstrate the proposed method to be feasible and robust. The findings of this research are expected to provide theoretical guidance for the inversion of elastic parameters of anisotropic media and fracture type identification in hydrocarbon reservoirs.