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- Volume 69, Issue 2, 2021
Geophysical Prospecting - Volume 69, Issue 2, 2021
Volume 69, Issue 2, 2021
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Kirchhoff pre‐stack depth scalar migration in a simple triclinic velocity model for three‐component P, S1, S2 and converted waves
By Václav BuchaABSTRACTMigration of multi‐component elastic data in anisotropic models is difficult and has not been reasonably addressed. Thus we test three‐dimensional ray‐based Kirchhoff pre‐stack depth scalar migration and calculate migrated sections in a simple anisotropic velocity model. We generate ray‐theory seismograms for separate phases of reflected P, S1, S2 and converted waves. The velocity model is composed of two homogeneous layers and one curved interface. The anisotropy of the upper layer is triclinic and the bottom layer is isotropic. We apply a scalar imaging separately to each component of the elementary wave in a single‐layer velocity model with the same triclinic anisotropy as in the upper layer of the velocity model used to calculate the recorded wave field. Results of Kirchhoff pre‐stack depth scalar migration indicate big differences for individual elementary wave components. We observe very good migrated interface for all three components of reflected PP wave, radial component of PS1 converted wave and transversal component of PS2 converted wave. For other components and elementary waves, the migrated interface is imaged, only partially, correctly.
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Order‐controlled closed‐loop focal beams and resolution comparison of primary and multiple reflections for seismic acquisition geometries
Authors Wei Wei and Gerrit BlacquièreABSTRACTFocal beam analysis has built a bridge between the acquisition parameters on the surface and the image quality of underground targets. However, as a practical matter, it is still difficult to answer how to choose a proper acquisition geometry according to the complexity of medium, especially considering the contradictory effects of multiple reflections on spatial resolution as they can be considered to be either potential signal or additional noise, depending on the envisioned imaging technology. We introduce an order‐controlled, closed‐loop focal beam method in which the migration operator and the resolution function can be analysed in the process of the closed‐loop migration with full control over the order of the surface and internal multiples considered. This method highlights the effects of primary and different‐order multiple wavefields on the imaging resolution for different acquisition geometries and various overburden strata. We apply the method to analyse the predicted resolution of seismic acquisition geometries considering multiples as either noise or signal. Results show, in the acquisition geometry design, that when the primaries cannot provide a complete spatial illumination for the subsurface target, e.g. because of the limited‐aperture acquisition geometries or the complicated overburden, we should use the closed‐loop focal beam analysis to assess the contradictory effects of multiples as both signal and noise, in which the maximum order of multiples ought to be chosen according to the core aim of the acquisition analysis. We can apply the second‐order closed‐loop focal beam analysis to quantify the effects of acquisition geometries on multiple‐wave suppression and can also perform the high‐order closed‐loop focal beam analysis to quantify the effects of acquisition geometries on high‐resolution imaging (migration). This method can also be used to choose the optimal order of multiples in the closed‐loop migration.
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Seismic data interpolation using deep learning with generative adversarial networks
Authors Harpreet Kaur, Nam Pham and Sergey FomelABSTRACTWe propose an algorithm for seismic trace interpolation using generative adversarial networks, a type of deep neural network. The method extracts feature vectors from the training data using self‐learning and does not require any pre‐processing to create the training labels. The algorithm also does not make any prior explicit assumptions about linearity of seismic events or sparsity of the data, which are often required in the traditional interpolation methods. We create the training labels by removing traces from different receiver indices of the original datasets to simulate the effect of missing traces. We adopt the framework of the generative adversarial networks to train the network and add additional loss functions to regularize the model. Numerical examples using land and marine field datasets demonstrate the validity and effectiveness of the proposed approach. With minimal computational burden and proper training, the proposed method can be applied to three‐dimensional seismic datasets to achieve accurate interpolation results.
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Unified elimination of 1D acoustic multiple reflection
Authors Evert Slob and Lele ZhangABSTRACTMigration, velocity and amplitude analysis are the employed processing steps to find the desired subsurface information from seismic reflection data. The presence of free‐surface and internal multiples can mask the primary reflections for which many processing methods are built. The ability to separate primary from multiple reflections is desirable. Connecting Marchenko theory with classical one‐dimensional inversion methods allows to understand the process of multiple reflection elimination as a data‐filtering process. The filter is a fundamental wave field, defined as a pressure and particle velocity that satisfy the wave equation. The fundamental wave field does not depend on the presence or absence of free‐surface multiples in the data. The backbone of the filtering process is that the fundamental wave field is computed from the measured pressure and particle velocity without additional information. Two different multiples‐free datasets are obtained: either directly from the fundamental wave field or by applying the fundamental wave field to the data. In addition, the known schemes for Marchenko multiple elimination follow from the main equation. Numerical examples show that source and receiver ghosts, free‐surface and internal multiples can be removed simultaneously using a conjugate gradient scheme. The advantage of the main equation is that the source wavelet does not need to be known and no pre‐processing is required. The fact that the reflection coefficients can be obtained is an interesting feature that could lead to improved amplitude analysis and inversion than would be possible with other processing methods.
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Elastic wave‐mode separation in 2D transversely isotropic media using optical flow
Authors Xiaoyi Wang, Yongming Lu and Jianfeng ZhangABSTRACTWave‐mode separation is a critical step in anisotropic elastic‐wave imaging. To avoid high computational costs and additional corrections, we apply a separation formula in the space domain that projects Cartesian components of the elastic wavefield onto the polarization vectors in the orthogonal direction. By solving the Christoffel equation, we implement the conversion from the phase velocity direction to the polarization direction. We use similarity between the seismic wave propagation and the optical flow problem to calculate the phase angle based on the Horn–Schunck algorithm. Compared with the conventional Poynting vector, the optical flow vector is more robust, particularly for complex underground structures. We demonstrate the performance of the proposed approach for two‐dimensional transversely isotropic media with three numerical examples and discuss the potential extension to three‐dimensional media.
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Stress prediction and evaluation approach based on azimuthal amplitude‐versus‐offset inversion of unconventional reservoirs
Authors Bingyi Du, Guangzhi Zhang, Jing Zhang, Jianhu Gao, Xueshan Yong, Wuyang Yang, Ren Jiang, Shujiang Wang, Hailiang Li and Enli WangABSTRACTA novel approach of unconventional reservoir stress evaluation is proposed to enhance the accuracy of fracture development prediction. Differential horizontal stress ratio can reflect the stress characteristic of fractured reservoir. To calculate the differential horizontal stress ratio more intuitionistic and simpler, we re‐derive its formula with Poisson's ratio and fracture density. Meanwhile, a new azimuthal PP‐wave amplitude versus offset equation based on Poisson's ratio and fracture density was derived for inverting above elastic parameters directly. Therefore, two steps are needed to realize stress evaluation. First, amplitude‐versus‐azimuthal angle inversion in the Bayesian framework in constraint of prior information such as well log data or rock physics information is executed for elastic and fracture parameters using pre‐stack angle gathers of different azimuth angles. The second procedure is to estimate differential horizontal stress ratio with Poisson's ratio and fracture density. Finally, a real data set is studied to test the new approach and the result demonstrated that the estimated differential horizontal stress ratio can reflect the stress property and it agrees with geological law and the new drilled well interpretation. Therefore, we can conclude that the combination of newly derived differential horizontal stress ratio and azimuthal PP‐wave amplitude versus offset equation in this study provides an available method for estimating the differential horizontal stress ratio of unconventional reservoirs, and the new approach can offer reliable geophysical information for stress evaluation.
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The impact of seismic inversion methodology on rock property prediction
Authors Mariana Martinho, Timothy Tylor‐Jones and Leonardo AzevedoABSTRACTSeismic inversion is a central step within the seismic reservoir characterization workflow. In seismic inversion, seismic amplitudes are inverted to predict the spatial distribution of the subsurface elastic properties. In subsequent steps of the geomodelling workflow, the inverted models are then converted into rock properties and facies. The methodology to perform the seismic inversion does have an impact on the predictions about the spatial distribution of the rock properties of interest. Each method has advantages and limitations depending on data quality, availability and the objective of the study. This work compares the application of deterministic and stochastic elastic inversion in a challenging real dataset. The deterministic approach is based on a sparse spike approximation, while the stochastic one is a global geostatistical seismic amplitude‐versus‐angle inversion. Both methods are applied to a real dataset acquired over a producing field located offshore Greece. Both the complex geological setting, where one target is close to seismic resolution, and data quality affect the performance of the seismic inversion methods. The results of both inversion methods are compared in terms of facies probability using Bayesian classification over the inverted elastic models and by comparing predictions to direct measurements at a blind well location. This application example shows the ability of deterministic inversion to retrieve an inverted solution with broader geological predictions which matched gross features in the wells. On the other hand, geostatistcal inversion was able to predict thin continous sand bodies, which correspond to the finer details at and below the seismic resolution of the observed data.
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Elastic properties of a reservoir sandstone: a broadband inter‐laboratory benchmarking exercise
ABSTRACTLow‐frequency forced‐oscillation methods applied to a reservoir sandstone allowed determination of the Young's modulus and Poisson's ratio (from axial loading), bulk modulus (by oscillation of the confining pressure) and shear modulus (from torsional‐forced oscillations) for comparison with conventional ultrasonic data. All tests were performed on a common sandstone core sample from an oil reservoir offshore West Africa. The results show a steady increase in ultrasonic velocities and shear modulus of the dry specimen as functions of pressure, which suggests a progressive closure of the inter‐granular contacts. An increase of bulk and Young's moduli and Poisson's ratio is observed on decane saturation of the sample when tested with a sufficiently small dead volume. This observation, consistent with Gassmann's theory, suggests that such measurements probe undrained (saturated isobaric) conditions. Diminution or absence of such fluid‐related stiffening for low‐frequency measurements with dead volumes comparable with the pore volume of the specimen indicates partially drained conditions and highlights the critical role of experimental boundary conditions. Directly measured bulk and shear moduli are consistent with those derived from Young's modulus and Poisson's ratio. These results of the inter‐laboratory testing using different measurement devices are consistent in terms of the effect of frequency and fluid saturation for the reservoir sandstone specimen. Such broad consistency illustrates the validity of forced‐oscillation techniques and constitutes an important benchmarking of laboratory testing of the elastic properties of a porous medium.
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Deep learning assisted well log inversion for fracture identification
Authors Miao Tian, Bingtao Li, Huaimin Xu, Dezhi Yan, Yining Gao and Xiaozheng LangABSTRACTManual fracture identification methods based on cores and image logging pseudo‐pictures are limited by the expense and the amount of data. In this paper, we propose an integrated workflow, which takes the fracture identification as an end‐to‐end project, to combine the boundary detection and the deep learning classification to recognize fractured zones with accurate locations and reasonable thickness. We first apply the discrete wavelet transform algorithm and a boundary detection method named changing point detection to enhance the fracture sensibility of acoustic logs and segment the whole logging interval into non‐overlapping subsections by estimating boundaries. The deep neural network based auto‐encoders and the convolutional neural network classifier are then implemented to extract the hidden information from logs and categorize the subsections as the fractured or non‐fractured zones. To validate the feasibility of this workflow, we apply it to the logging data from a real well. Compare with the benchmarks provided by the support vector machine , random forest and Adaboost model, the one‐dimensional well profile predicted by the proposed changing point detection‐deep learning classifier is more consistent with the manual identification result.
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Subsurface structures of the Xiaorequanzi deposit, NW China: new insights from gravity, magnetic and electromagnetic data
Authors An Shaole, Zhang Zhixin, Zhou Kefa and Wang JinlinABSTRACTThe Xiaorequanzi copper–zinc deposit, located in the eastern Tianshan, is considered as a medium‐sized polymetallic deposit in Xinjiang, NW China. Understanding the structural framework and delineating the location of intrusions as well as the distribution of ore‐controlling strata in this mining area are vital for identifying new potential exploration areas. This paper aims to provide new information on the subsurface structures of the Xiaorequanzi copper–zinc deposit and to explore new target area for the ore prospecting based on comprehensive interpretation of multiple geophysical datasets. Several potential field filtering methods were used to study the spatial distribution of the geological structure. The structural lineament was delineated based on various source boundary detection results. Euler deconvolution method was used to estimate the field source depth, and the structure model was constructed from 2.5D forward gravity modelling, constrained by borehole and electromagnetic data. The results indicate that the local gravity highs may be the concentration of mineralized rocks with high density extending to depths of several hundred meters; the magnetic highs are related to mineralized rocks/intrusive rocks that are close to the surface. The tectonic framework of the whole ore bed is mainly controlled by NW‐SE, E‐W and NE‐SW structures, in which NE‐SW structures are related closely to mineralization and may provide optimum conditions for magma emplacement and fluid migration. Our research will help to study the tectonic evolution and guide further exploration of the deposit.
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Denoising of magnetotelluric data using K‐SVD dictionary training
Authors Jin Li, Yiqun Peng, Jingtian Tang and Yong LiABSTRACTMagnetotelluric is one of the mainstream exploration geophysical methods, which plays a vital role in studying deep geological structures and finding deep hidden blind ore bodies. The seriousness of human electromagnetic noise causes a large number of abnormal waveforms in the time series of measured magnetotelluric data, and the data can no longer objectively reflect the underground electrical distribution. In this work, we propose a magnetotelluric time series data processing method based on K singular value decomposition dictionary training. First, a training matrix and a to‐be‐processed matrix are built with the pending magnetotelluric signals. Then, let the K singular value decomposition dictionary training process the training matrix to obtain an over‐complete dictionary reflecting the characteristics of the pending signal. Lastly, orthogonal matching pursuit is combined with an over‐complete dictionary updated in real time to sparsely represent the to‐be‐processed matrix and remove human electromagnetic interference in the signal. Experimental results show that the method can update the over‐complete dictionary in real‐time according to the pending magnetotelluric signals, realize the self‐learning signal–noise separation of magnetotelluric signals, and effectively retain low‐frequency information. Compared with method of directions dictionary learning, remote reference method, and orthogonal matching pursuit method, the reconstructed data of the proposed method can more accurately reflect the underground electrical structure information.
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Improved controlled source audio‐frequency magnetotelluric method apparent resistivity pseudo‐sections based on the frequency and frequency–spatial gradients of electromagnetic fields
Authors Ming Zhang, Colin G. Farquharson and Changsheng LiuABSTRACTAlthough most electromagnetic data can be inverted to actual resistivity, ways of quickly getting a real‐time interpretation of a data set are still valuable. Such methods are useful when we are testing instrumentation or assessing data quality during a survey, or when we need to get a general understanding of the geological structure during a field survey. Apparent resistivity is a good way to satisfy these desires. However, one of the disadvantages of apparent resistivities is that the traditional apparent resistivity formulations are poor at recognizing boundaries, mainly because abnormal responses get stretched into deeper parts of the image (a shadow effect). In order to improve the recognition ability of boundaries, we propose improved apparent resistivity pseudo‐sections based on the formulae for the frequency and frequency–spatial gradients of the fields in the far‐field region of frequency‐domain controlled‐source audio‐frequency magnetotelluric surveys. The new pseudo‐sections are found to be better than those produced from a traditional method when applied to a number of 3‐D examples. The performance of this apparent resistivity method is closely related to using an appropriate transmitter–receiver distance: when a proper value is used, good results can be obtained in which the horizontal locations of vertical boundaries and the positions of top and bottom boundaries can be identified clearly. Finally, the usefulness of the proposed method for practical applications is evaluated with a field‐data example, for which the results of the proposed apparent resistivity imaging method are compared with traditional apparent resistivities, as well as with the results from a 2‐D inversion of DC resistivity data from the same survey line and with what is known about the geology of the area. This comparison demonstrates the improved capabilities of the new apparent resistivities over traditional approaches, including an improved capability to accurately reveal the bottoms of targets.
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Volumes & issues
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Volume 72 (2023 - 2024)
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Volume 71 (2022 - 2023)
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Volume 70 (2021 - 2022)
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Volume 69 (2021)
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Volume 68 (2020)
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Volume 67 (2019)
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Volume 66 (2018)
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