Seismic Acquisition Research Articles

SALT-DOME STRUCTURES MODELING IN DEPTH DOMAIN USING RAY TRACING AND SEISMIC ATTRIBUTE ANALYSIS

Background. Increasing the resource base and hydrocarbon exploration is the main goal of performing seismic data acquisition. Due to the presence of salt diapirs, normal and reverse faults as well as other discontinuities in the geological subsurface, there appear characteristic features of seismic signals – a break in phase continuity and significant attenuation of the amplitude without a break in phase continuity. These geological features are sometimes distinguished by attenuation of the seismic signal or even an absence of wave field reflections. In areas of salt-dome tectonics, it is often completely impossible to trace any reflective horizons. To accurately map the fault location, deep or vertical horizons, it is necessary to improve the approach to obtaining and interpreting data in faulted areas with complex geology. Methods. The research presents an integrated approach to modeling the rays from each receiver of seismic signals to each bin on the reflective boundary. The reflected beams then propagate from the reflective boundary to the simulated position of the seismic receiver. Since the nature and velocity of beam propagation vary, it is possible to obtain additional information from zones shielded by faults or sub-vertical horizons, to trace the trajectories of seismic energy propagation and its focusing/defocusing zones. Verification of the seismic image and its geological content was performed using seismic attribute analysis. Results. Ray tracing allowed analyzing poor illumination zones below salt wings. During further steps such as processing and interpretation, ray tracing provided additional information for diapir mapping. Seismic attribute analysis was used as an additional tool to define the boundaries of the salt structure. This makes it possible to analyze the dynamic and kinematic parameters of the seismic field and map the salt body's boundaries based on these characteristics. Conclusions. An integrated approach involving several methods will solve the problem of mapping seismic horizons in areas surrounding fault zones with a weak seismic signal. A more reliable geological image can only be obtained by using complex sequences, including seismic processing, ray tracing, and seismic attribute analysis. The integrated application of the techniques demonstrates consistent geological results and has implications for discovering new deposits and hydrocarbon traps confined to the zones of development of salt-dome tectonics in the Dnipro-Donets basin.

Quality control measures for seismic exploration data acquisition in front of Longmen Mountain

In front of Longmen Mountain, there is a mountainous area with large terrain elevation differences and dense vegetation. The coupling between detectors in limestone and gravel exposed areas and the surface layer is poor. The completion of blasting wells in gravel areas, as well as the layout and burial of seismic survey lines and detectors in mountainous areas, pose difficulties in seismic exploration. The article dissects the factors that affect the quality of collected data, such as shot point measurement, shot point drilling, detector burial, mountainous survey line layout, and interference source control. Targeted measures to enhance GPS measurement signals, gravel area shot well drilling, and mountainous survey line layout are proposed to effectively improve the quality of collected data and work efficiency, which can provide reference for seismic exploration in mountainous areas.

Design of Undersampled Seismic Acquisition Geometries via End-to-End Optimization

Design of Undersampled Seismic Acquisition Geometries via End-to-End Optimization

Improved Cepstrum Analysis for Multiplicative Noise in Borehole Seismic Acquisition Records

Improved Cepstrum Analysis for Multiplicative Noise in Borehole Seismic Acquisition Records

Has the Importance of ‘Signal’ been Forgotten in the Signal-to-Noise Ratio of Land Seismic Acquisition?

Has the Importance of ‘Signal’ been Forgotten in the Signal-to-Noise Ratio of Land Seismic Acquisition?

High Precision 3D Seismic Acquisition Practice in the YKD Block of Tabei Uplift, Tarim Basin

High Precision 3D Seismic Acquisition Practice in the YKD Block of Tabei Uplift, Tarim Basin

High-density analysis of surface wave profile imaging based on a multiple coverage common-midpoint signal-couple array

Surface wave exploration technology has been extensively used in the inspection of construction engineering quality and shallow surface surveys. To enhance the efficiency of surface wave exploration field acquisition and achieve high-precision and high-density surface wave profile imaging, a wireless distributed seismic surface wave signal acquisition system has been developed based on the principles of active source transient surface wave signal acquisition and dispersion curve calculation methods. For the purpose of achieving rapid multiple coverage signal acquisition and enhancing fieldwork efficiency, a method for rapidly configuring common-midpoint signal couples (CMCs) for multiple coverage common-shot signal acquisition has been devised, and a high-precision visualization method for dispersion curve calculation based on the CMC array has been formulated. When compared with the multichannel analysis of the surface wave method under identical conditions, the CMC array can effectively enhance surface wave dispersion curve survey station density and lateral resolution, thereby enabling high-density analysis of surface wave profile imaging. Through model analysis and field examples related to construction quality detection, including foundation compactness and earth and rock mixture compactness, it has been demonstrated that this method offers significant advantages in terms of high accuracy, high density, and a wide application range. These advantages greatly enhance the efficiency of surface wave exploration and the accuracy of profile imaging for the construction of engineering projects.

Third‐order elasticity of transversely isotropic field shales

AbstractThe formations above a producing reservoir can exhibit large mechanical changes, creating a risk of significant subsidence and loss of rock integrity. These changes can be monitored by time‐lapse seismic acquisition, which measures the corresponding velocity changes via time‐shifts. Third‐order elastic theory can be used to connect subsurface strains and stress changes to these seismic attribute changes. Existing models assume isotropic strain dependence of the dynamic stiffness in shales. It is important to re‐evaluate this isotropic assumption considering the inherent anisotropy of shales and their abundance in the overburden. Thus, we instead propose a third‐order elastic model with a transversely isotropic strain dependence of the dynamic stiffness. When calibrated, this new model satisfactorily predicted P‐wave velocity changes determined in undrained laboratory experiments conducted on overburden field shales, covering a wide range of propagation directions and stress variations. The shales exhibit anisotropic dynamic strain sensitivity, resulting in a significantly higher strain sensitivity predicted for Thomsen's anisotropy parameters epsilon and delta subjected to a uniaxial strain parallel to the horizontal bedding plane compared to the vertical direction. Geomechanical modelling, considering a depleting disk‐shaped reservoir surrounded by shales, was employed to predict the dynamic stiffness changes of the overburden using the laboratory‐calibrated third‐order elastic model. The overburden time‐shifts increased with offset angle, peaking at about 45°, suggesting a strong influence of shear strains on the time‐shifts. In contrast, a corresponding model with an isotropic third‐order elastic tensor, calibrated to the same data, exhibited a significantly lower sensitivity to the shear strains. These results underscore the importance of considering the anisotropic strain dependence of the dynamic stiffness when studying shales. Interpreting offset‐dependent trends in pre‐stack time‐lapse seismic data, along with geomechanical modelling and an appropriate strain‐dependent rock physics model, can assist in quantifying subsurface strains and stress changes.

Seismic Velocity Inversion via Physical Embedding Recurrent Neural Networks (RNN)

Seismic velocity inversion is one of the most critical issues in the field of seismic exploration and has long been the focus of numerous experts and scholars. In recent years, the advancement of machine learning technologies has infused new vitality into the research of seismic velocity inversion and yielded a wealth of research outcomes. Typically, seismic velocity inversion based on machine learning lacks control over physical processes and interpretability. Starting from wave theory and the physical processes of seismic data acquisition, this paper proposes a method for seismic velocity model inversion based on Physical Embedding Recurrent Neural Networks. Firstly, the wave equation is a mathematical representation of the physical process of acoustic waves propagating through a medium, and the finite difference method is an effective approach to solving the wave equation. With this in mind, we introduce the architecture of recurrent neural networks to describe the finite difference solution of the wave equation, realizing the embedding of physical processes into machine learning. Secondly, in seismic data acquisition, the propagation of acoustic waves from multiple sources through the medium represents a high-dimensional causal time series (wavefield snapshots), where the influential variable is the velocity model, and the received signals are the observations of the wavefield. This forms a forward modeling process as the forward simulation of the wavefield equation, and the use of error back-propagation between observations and calculations as the velocity inversion process. Through time-lapse inversion and by incorporating the causal information of wavefield propagation, the non-uniqueness issue in velocity inversion is mitigated. Through mathematical derivations and theoretical model analyses, the effectiveness and rationality of the method are demonstrated. In conjunction with simulation results for complex models, the method proposed in this paper can achieve velocity inversion in complex geological structures.

Experiment and Application of A Aparker Source in Seismic Exploration in Coastal Tidal Flats

We investigate a field experiment in coastal tidal flats in Jiangsu Province using a sparker source, to test the seismic data acquisition system and detect the shallow layers in the real scenarios. We also compare the geophones with 60Hz and 10Hz natural frequency in shot gather as well as in stacked profiles. We process the different data separately, and then process them together in a wide-line mode. As predicted, the fold is doubled showing a higher signal to noise ratio. Through the routine multi-domain integrated noise removal and effective data processing, the signal-to-noise ratio and resolution of the seismic data get largely improved, and thus we successfully obtain the high-quality seismic profiles. The practical experiment results are of importance for shallow geological and environmental detections, as well as for geo-hazard prevention study in the related areas.

UAV Seismic Source Traveltime Tomography Technology in Steep Mountainous Areas

Active source seismic exploration is one of the most important high-resolution methods to obtain information about underground structures in tunnel engineering, and the seismic source is the key equipment to acquire high-quality data. In steep mountainous areas with complex geological structures, surface undulation, and drastic changes, the use of traditional seismic sources with high cost can seriously affect the efficiency and accuracy of detection. In this paper, a survey experiment of tunnel engineering is carried out in Lijiang, Yunnan Province, completing a fast, high-quality seismic data acquisition through utilizing the convenient, environmentally friendly, and economical UAV (Unmanned Aerial Vehicle) seismic source as the shot and wireless node instrument as the receiver. The experiment shows that the UAV seismic source can achieve low-cost and environmentally friendly excitation of seismic waves in complex mountainous areas, making a breakthrough in seismic source equipment. We obtain a high-precision velocity model by traveltime tomography, identifying the thickness of the overburden layer in the study area, and providing an important reference for tunnel engineering.

Numerical Simulation and Characteristics Analysis of VSP Seismic Data in Complex Loess Plateau Area

The numerical simulation and characteristic analysis of VSP seismic data in the complex Loess Plateau area is of great significance to the design of seismic acquisition systems and the velocity modeling in near-surface areas. In this paper, we establish a velocity model of complex Loess Plateau and design three VSP acquisition systems in this model. Then, a finite difference method (FDM) for seismic modeling in the complex topographical condition is used to simulate the VSP data in this model with the above acquisition systems. Finally, based on the analysis to these data, some basic characteristics of VSP data in the complex Loess Plateau area are obtained and their significances to seismic exploration in this area are given.

Weighted Adjoint-state Crosshole Traveltime Tomography

The adjoint-state method of first arrival traveltime tomography, featured with implicit ray tracing calculation, was used to compute the gradient of the misfit function without introducing Fréchet derivatives. However, the majority of velocity gradients in the adjoint-state method focus on the high velocity area due to high raypath density. Thus, the current adjoint-state method faces challenges of imaging low velocity anomalies in the near-surface by crosshole seismic acquisition. In this paper, we proposed a weighted adjoint-state Eikonal equation-based crosshole traveltime tomography method. A preconditioning operator is provided to fix the ray path density influence. The weighted method was tested on a homogeneous model, a checkerboard model, and a reduced-scale Marmousi model. Both misfit cures and velocity logs are used to make a comparison between the weighted method and the traditional method. The results show the weighted method has better resolution, faster convergence speed, and lower final residual, which demonstrate the method can mitigate the ray path effect and reduce the inverted velocity error of low velocity anomalies.

Achieving Robust Compressive Sensing Seismic Acquisition with a Two-Step Sampling Approach

The compressive sensing (CS) framework offers a cost-effective alternative to dense alias-free sampling. Designing seismic layouts based on the CS technique imposes the use of specific sampling patterns in addition to the logistical and geophysical requirements. We propose a two-step design process for generating CS-based schemes suitable for seismic applications. During the first step, uniform random sampling is used to generate a random scheme, which is supported theoretically by the restricted isometry property. Following that, designated samples are added to the random scheme to control the maximum distance between adjacent sources (or receivers). The null space property theoretically justifies the additional samples of the second step. Our sampling method generates sampling patterns with a CS theoretical background, controlled distance between adjacent samples, and a flexible number of active and omitted samples. The robustness of two-step sampling schemes for reallocated samples is investigated and CS reconstruction tests are performed. In addition, using this approach, a CS-based 3D seismic survey is designed, and the distributions of traces in fold maps and rose diagrams are analyzed. It is shown that the two-step scheme is suitable for CS-based seismic surveys and field applications.

Attenuating free‐surface multiples and ghost reflection from seismic data using a trace‐by‐trace convolutional neural network approach

AbstractThe presence of the air–water interface (or free‐surface) creates two major problems in marine seismic data for conventional seismic processing and imaging: free‐surface multiples and ghost reflections. The attenuation of free‐surface multiples remains one of the most challenging noise attenuation problems in seismic data processing. Current solutions suffer from the removal of the primary events along with the multiple events especially when the primary and multiple events overlap (e.g., adaptive subtraction). The effective attenuation of ghost reflections (or deghosting) requires acquisition‐ and/or processing‐related solutions which generally address the source‐side and receiver‐side ghosts separately. Additionally, an essential requirement for a successful implementation of free‐surface multiple attenuation and seismic dehosting is the requirement of dense seismic data acquisition parameters which is not realistic for two‐dimensional and/or three‐dimensional marine cases. We present a convolutional neural network approach for free‐surface multiple attenuation and seismic deghosting. Unlike the existing solutions, our approach operates on a single trace at a time, and neither relies on the dense acquisition parameters nor requires a subtraction process to eliminate free‐surface multiples, and it removes both the source ghost and receiver ghost simultaneously. We train a network using subsets of the Marmousi and Pluto velocity models and make predictions using subsets of the Sigsbee velocity model. We show that the convolutional neural network predictions give a correlation coefficient of 0.97 on average with the numerically modeled data for the synthetic examples. We illustrate the efficacy of our convolutional neural network–based technique using the Mobil AVO Viking Graben field data set. The application of our algorithm demonstrates that our convolutional neural network–based approach removes different orders of free‐surface multiples (e.g., first and second orders) and recovers the low‐frequency content of the seismic data (which is essential for, for instance, full‐waveform inversion applications and broadband processing) by successfully removing the ghost reflections while preserving and increasing the continuity of the primary reflection.

Wide azimuth seismic data processing technology and application: a case study of tight gas reservoirs in western China

As the difficulty of oil and gas field exploration and development increases both domestically and internationally, onshore exploration targets have gradually shifted from the shallow to the deep and from conventional oil and gas reservoirs to unconventional ones. Particularly in the exploration and development of unconventional oil and gas horizontal wells, there is an increasing demand for higher precision and quality of seismic data to better identify formation lithology, rock fractures, and improve the characterization of reservoirs, reservoir positioning, and connectivity. Wide-azimuth seismic exploration possesses significant technical advantages in addressing exploration challenges such as lithologic exploration, small fault imaging, and detailed characterization of oil and gas reservoirs. Wide azimuth seismic data reduces blind spots in seismic acquisition and improves the imaging accuracy of small faults. Notably, there exist distinct anisotropic characteristics in fault areas and fractured reservoirs. Wide azimuth seismic data is particularly advantageous for studying amplitude variation with variations in amplitude with offset (AVO), incident angle (AVA), or azimuth (AVAZ), as well as velocity with azimuth (VVA). These variations aid in identifying faults, fractures, and changes in formation lithology. As the focus of oil and gas exploration gradually shifts to complex lithological reservoirs and unconventional oil and gas reservoirs, narrow azimuth seismic exploration has been gradually replaced by wide azimuth exploration. However, as observation azimuth increases, challenges related to velocity variations with azimuth, azimuth-related traveltime differences, and azimuth-related anisotropy arise. Based on wide-azimuth seismic data from tight gas reservoirs in western China, this study conducted wide-azimuth anisotropic velocity analysis, OVT domain data regularization processing, OVT domain prestack time/depth migration, and horizontally transverse isotropy (HTI) azimuth anisotropy correction techniques. After applying specialized processing to the wide-azimuth seismic data, significant improvements were observed in the S/N and resolution of the target layer. The delineation of fractures related to hydrocarbon sources also became more distinct. These advancements not only provided high-quality results for high-fidelity, high-resolution imaging of tight gas reservoirs but also provided azimuth volume corresponding to fast and slow wave velocities for seismic data interpretation, facilitating velocity variation with azimuth (VVAZ) fracture detection and AVO analysis research.

Three-component Seismic Data Acquisition System Base on STM32 and ADS1285

In the three-component seismic VSP seismic data acquisition, high-resolution and low-power seismic wave acquisition is an important research content of seismic exploration methods. In view of the problems of insufficient resolution and signal attenuation of existing geophones, which lead to distortion of seismic signals collected in complex strata. A digital downhole digital three-component detector based on ADS1285 is proposed. The design uses STM32F405 as the microcontroller, ADM2582E as the RS485 transmission chip, and uses the PPP communication protocol to achieve high-speed communication with the host computer. Experimental results show that the dynamic range of the data acquisition system based on ADS1285 is 135dB, and the average analog-to-digital conversion equivalent noise level is below 0.5μV, which can effectively improve the acquisition accuracy of field seismic exploration.

Research and Application of Excitation Method Optimization in Areas with Low Signal-to-Noise Ratio of Seismic Data

In areas with a low signal-to-noise ratio of seismic data, the continuity of the seismic reflection waves in the exploration target layer is very poor, which will reduce the imaging accuracy and make it impossible to solve certain geological tasks. This article suggests an approach to address the issue of seismic acquisition by optimizing excitation parameters. It involves conducting a detailed investigation of the surface structure, enhancing the observation system, increasing the coverage appropriately, and transitioning from combined-well excitation to single-well excitation. Additionally, the use of technical tools like qualitative evaluation of the observation system and forward modeling are employed to determine the final optimized seismic acquisition plan. The effectiveness of this approach is evident from the seismic profile obtained in an exploration area in Inner Mongolia.

Removing abnormal environmental noise in nodal land seismic data using deep learning

In field seismic data, abnormal environmental noise from the acquisition environment often is unavoidable, and its characteristics are complex. Abnormal environmental noise has a strong amplitude (tens of thousands times the average seismic amplitudes) and masks the effective seismic signal. The traditional method for removing such abnormal environmental noise relies on energy scanning to identify the noise location. However, energy scanning cannot always accurately identify such noise due to the sudden energy change at the locations of first-breaks and surface waves. A deep learning method for removing abnormal environmental noise has been developed. The presence of environmental noise with large amplitude also results in an uneven energy distribution. The existing denoising networks rarely consider this situation. Based on the characteristics of environmental noise received by the nodal seismic acquisition system, a workflow for automatically generating the training data sets has been established. The network is built based on the Unet with the incorporation of a residual block. The Batchnorm layer is specifically omitted to adapt to the uneven energy distribution. Synthetic and field data testing have confirmed the effectiveness and applicability of the proposed method. The new method indicates better denoising performance and greater flexibility than the traditional method. Comparison between different networks also indicates that the new Residual Block Connected Unet is much more effective in denoising seismic data that contain abnormal environmental noise.

Applications of deep neural networks in exploration seismology: A technical survey

Exploration seismology uses reflected and refracted seismic waves, emitted from a controlled (active) source into the ground, and recorded by an array of seismic sensors (receivers) to image the subsurface geologic structures. These seismic images are the main resources for energy and resource exploration and scientific investigation of the crust and upper mantle. We survey recent advances in applications of machine-learning methods, more specifically deep neural networks (DNNs), in exploration seismology. We provide a technically oriented review of DNN applications for seismic data acquisition; data preprocessing tasks such as interpolation/extrapolation, denoising, first-break picking, velocity picking, and seismic migration; data processing tasks such as geologic and structural interpretations; and data modeling tasks such as the inference of subsurface structures and lithologic and petrophysical properties. DNNs have entered almost every sector of exploration seismology. They have outperformed many traditional algorithms for the automation of seismic data acquisition, data preprocessing, data processing, interpretations, and data modeling tasks. However, despite the impressive performances of DNN-based approaches, the out-of-distribution generalization and interpretability of these models remain challenging. To overcome these challenges, incorporating domain knowledge into the DNNs is a promising path and a focus of current deep-learning research in seismology.