DAS VSP Data Research Articles

DAS-VSP coupled noise suppression based on U-Net network

Abstract The emerging distributed fiber-optic acoustic sensing (DAS) technology has broad prospects for application in vertical seismic profiles (VSP). However, the acquired DAS-VSP data often suffers from coupled noise that seriously affects data quality. Traditional methods for suppressing coupled noise are usually time-consuming and not suitable for the large-scale denoising of DAS-VSP data. To address this, a coupled noise suppression method based on the U-Net network is proposed, and a self-attention (SA) block is introduced to enhance the denoising ability of the network. Transfer learning is employed to achieve coupled noise suppression from synthetic data to field data. Denoising results demonstrate that the network can effectively suppress coupled noise in DAS-VSP data while preserving signal energy to a certain extent, exhibiting strong generalization capability. Upon completion of network training, denoising results can be obtained within seconds, making it more convenient and efficient compared to traditional methods.

Fine-Amplitude Structure Localization Using Correlation Coefficients Between DAS VSP Data and Surface Seismic Data at the Same Interface

Fine-Amplitude Structure Localization Using Correlation Coefficients Between DAS VSP Data and Surface Seismic Data at the Same Interface

Seismic Interpolation Transformer for Consecutively Missing Data: A Case Study in DAS-VSP Data

Seismic Interpolation Transformer for Consecutively Missing Data: A Case Study in DAS-VSP Data

A Universal Denoiser for DAS-VSP Data Based on Semantic Mask and Random Mask Training

A Universal Denoiser for DAS-VSP Data Based on Semantic Mask and Random Mask Training

Diffusion Model for DAS-VSP Data Denoising.

Distributed acoustic sensing (DAS) has emerged as a transformational technology for seismic data acquisition. However, noise remains a major impediment, necessitating advanced denoising techniques. This study pioneers the application of diffusion models, a type of generative model, for DAS vertical seismic profile (VSP) data denoising. The diffusion network is trained on a new generated synthetic dataset that accommodates variations in the acquisition parameters. The trained model is applied to suppress noise in synthetic and field DAS-VSP data. The results demonstrate the model's effectiveness in removing various noise types with minimal signal leakage, outperforming conventional methods. This research signifies diffusion models' potential for DAS processing.

Interpretable denoising of distributed acoustic sensing vertical seismic profile data using adaptive consistent prior net

Distributed Acoustic Sensing (DAS) is vital in seismic exploration, especially Vertical Seismic Profile (VSP) studies, due to its high sensitivity, fine velocity modeling, and resistance to harsh conditions. However, DAS VSP data often suffer from low quality due to complex noise sources. Previous studies show that convolutional neural network (CNN) denoising methods can achieve good results, but they often lack interpretable mechanisms for denoising, posing a challenge in understanding and interpreting the results. To overcome these limitations, we propose an adaptive consistency prior network (ACPNet) algorithm, based on a model-guided design. First, we introduce an adaptive consistency prior algorithm to form a model-based denoising method that guides the network design, leading to an end-to-end trainable and interpretable denoising network. We constructed a comprehensive training dataset, and the network is trained using the L1 loss function to achieve effective signal-to-noise separation. The combined synthetic data and field data denoising results demonstrate that the proposed method can effectively suppress various types of noise in DAS-VSP data and has a strong signal recovery capability. Furthermore, the denoising performance of the proposed method surpasses that of traditional methods and several deep learning techniques.

A Global and Multiscale Denoising Method Based on Generative Adversarial Network for DAS VSP Data

A Global and Multiscale Denoising Method Based on Generative Adversarial Network for DAS VSP Data

Joint Obn and 3D Das-Vsp Data Acquisition and Processing in the East China Sea

Borehole and surface joint seismic exploration is a 3D seismic exploration method formed by the combination of surface seismic and VSP survey simultaneously. Using the 3D DAS-VSP data, accurate time-depth relationship, formation velocity, deconvolution operator, spherical diffusion compensation factor, absorption attenuation factor, anisotropy parameters and high-resolution structural imaging around the wellbore can be obtained. These parameters can be used to significantly enhance the surface 3D seismic data processing. This paper describes the 3D DAS-VSP data acquired by a downhole armoured optical cable simultaneously with an OBN data acquisition project in East China Sea, and the results of 3D DAS-VSP data imaging processing. The usual 3D VSP data imaging processing steps include: observation system definition, preprocessing, first arrival picking, static correction, amplitude compensation, deconvolution, wavefield separation, velocity analysis and imaging. Based on the characteristics of offshore 3D VSP downgoing multiples, the offshore 3D VSP downgoing multiple imaging technique has been innovatively developed. This greatly expands the 3D DAS-VSP imaging range and improves the overall 3D DAS-VSP imaging quality. The 3D DAS-VSP downgoing multiple reflection wave imaging shows significant imaging quality improvement in comparison with the vintage 3D OBC data imaging. The detailed structure, tracing of the reservoir and formation tops becomes easier and less ambiguous using both new OBN data imaging and 3D DAS-VSP data imaging when compared with the vintage 3D OBC data imaging.

Advanced time-lapse processing of continuous DAS VSP data for plume evolution monitoring: Stage 3 of the CO2CRC Otway project case study

Active time-lapse seismic monitoring technology is essential for carbon storage projects due to its ability to track the CO2 plume in space and time. A particularly attractive implementation of this technology is permanent seismic reservoir monitoring (PRM) using permanent sources and receivers, which can track subsurface changes in near-real-time over decades. As the number of source points in such a setup is likely to be limited, repeatability and signal to noise ratio need to be improved by processing all the multiple vintages together rather than separately.We explore the advantages of this approach using a PRM dataset acquired to monitor injection of 15 kt of CO2 into a saline aquifer at the Otway International Test Centre (Australia). The monitoring employs continuous acquisition of multi-well offset VSP using nine permanent sources (surface orbital vibrators) and fibre-optic distributed acoustic sensors installed in five monitoring wells producing a vintage every two days for eighteen months before, during and after the injection.Since data reveals seasonal repeatability variations, mainly created by seasonal variations in precipitation levels, for each monitor, an optimal baseline is the one acquired in the same season. The signal-to-noise ratio is further improved by wavefield decomposition of P and S waves. The consistency of the source signature is improved using Wiener filtering. These algorithms improve the data repeatability from about 15–20% to 10–15% normalised root mean square. The results show the CO2 plume signal on the second day of the injection and subsequent time-lapse changes of a stable CO2 plume created at the same reservoir 650 m up-dip five years earlier as a part of the previous field experiment (Stage 2C) at the site.

Monitoring Dynamic Changes in Reservoir Production Using Time-Lapse Walkaway Das-Vsp Data

With the rapid development of optical fibre sensing in recent years, Distributed Acoustic Sensing (DAS) is gradually considered as a viable alternative to borehole geophone arrays for the acquisition of borehole seismic or Vertical Seismic Profile (VSP) data. The data collected by an optical cable permanently deployed behind casing have a high degree of consistency and high SRN, which is less affected by receiving factors such as the cable coupling issues and the tube wave. This paper describes the time-lapse Walkaway DAS-VSP data acquisition in Dagang Oilfield of China and the multi-stage time-lapse Walkaway DAS-VSP data consistency processing procedures. Through the fine imaging processing of multi-stage Walkaway DAS-VSP data and detailed geological interpretation, it is possible to identify and map the range of fluid migration around the wellbore due to production, perform cross-validation during the monitoring period using the borehole oil production statistics information, and analyse the dynamic changes of reservoir fluids within the monitoring field. The Walkaway DAS-VSP survey has become an important development of the borehole seismic technique due to its advantages of high density, high efficiency, low cost and good consistency, which can be carried out with repeated observations and also for permanent monitoring. Therefore, the use of time-lapse Walkaway DAS-VSP surveys is proposed for the monitoring of oil and gas production in mature fields. The repeated time-lapse Walkaway DAS-VSP surveys can provide high-quality borehole seismic data for the study of dynamic changes of the reservoir fluid around the wellbore to a certain range. The key points of this paper mainly include: (1) Time-lapse Walkaway DAS-VSP data acquisition; (2) Special time-lapse Walkaway DAS-VSP data processing procedures; (3) Time-lapse Walkaway DAS-VSP data inversion; (4) Map reservoir fluid dynamic changes during the oil and gas production. In the application process of the time-lapse Walkaway DAS-VSP, the high-accuracy velocity fields, high-resolution structure imaging and multiple attribute inversion of the reservoir formation around the wellbore have been obtained. In addition, it also provides reservoir fluid identification and mapping based on high-accuracy Walkaway DAS-VSP and surface seismic data, which provide a basis for the establishment of multi-domain and multi-dimensional geological and reservoir models.

Background Noise Suppression for DAS-VSP Records Using GC-AB-Unet

Distributed acoustic sensing (DAS) is one of the most popular sensors for seismic acquisition. Compared with traditional seismic acquisition technology, DAS has the advantages of full-well coverage, high density, high efficiency, high sensitivity, low cost, strong resistance to high temperature and high pressure, and anti-electromagnetic field interference. However, the DAS vertical seismic profile (VSP) data are contaminated by strong noise interference, which brings challenges for practical applications and difficulties to seismic inversion and interpretation. A deep learning method named U-net with Global Context Block and Attention Block (GC-AB-Unet) is proposed to suppress the background noise and increase the data quality for DAS-VSP records without knowing any prior information. In GC-AB-Unet, several dropout layers are added to the U-net to avoid overfitting. Meanwhile, to speed up the network training, the residual units are set as the output of the network. Furthermore, GC-Block is introduced for better capturing shallow and deep features by extracting global context information. In addition, Attention Block is used to emphasize seismic event features and restrain irrelevant details in seismic data. We also construct a training dataset by utilizing the synthetic data and real noise of DAS-VSP data. The denoising results for both synthetic data and field DAS-VSP data show that compared to original U-net and Damped Rank Reduction (DRR) method, the GC-AB-Unet network is able to preserve the effective signals with almost no energy leakage while suppressing a large amount of background noise.

RCEN: A Deep-Learning-Based Background Noise Suppression Method for DAS-VSP Records

Recently, distributed optical fiber acoustic sensing (DAS) is regarded as a transformative technology in seismic exploration. However, both various complex background noise and weak desired signals significantly limit its practical application. To explore an effective denoising method for the vertical seismic profile (VSP) record received by DAS, we propose an improved residual encoder–decoder deep neural network (RED-Net) enhanced by deep iterative memory block (DMB) and channel aggregation block (CAB), called residual channel aggregation encoder–decoder network (RCEN). Here, DMB uses the weight accumulation theory to improve the feature extraction ability and achieve accurate noise elimination. Meanwhile, CAB, using the multi-channel analysis architecture, enhances the weak signal retention performance. In addition, we leverage both the synthetic data obtained by forward modeling and real DAS noise data to construct a sufficient training dataset with high authenticity, thereby meeting the requirement of network training. Both the synthetic and field DAS-VSP data processing results demonstrate the advantage of RCEN compared with competing algorithms, including singular value decomposition (SVD), conventional RED-Net, and feed-forward denoising convolutional neural network (DnCNN).

Random and Coherent Noise Suppression in DAS-VSP Data by Using a Supervised Deep Learning Method

Distributed fiber-optical acoustic sensing (DAS) is a new and booming technology in seismic exploration. DAS technology has been gradually applied to the exploration of vertical seismic profile (VSP) due to its strong resistance to high temperature and pressure, high sensitivity, high precision (trace interval can be accurate to about 1 m), and so on. However, real DAS-VSP data are always contaminated by both random and coherent noises, which greatly affects the quality of DAS-VSP data. In order to suppress the background noise and increase the signal-to-noise ratio (SNR), a convolutional neural network (CNN) based on leaky rectifier linear unit (ReLU) and forward modeling is proposed and named L-FM-CNN. In terms of network architecture, Leaky ReLU is adopted as the activation function of CNN, which can enhance the recovery ability of trained CNN denoising model to the weak effective signals. As for the training data set, we construct a high-authenticity theoretical pure seismic data set for DAS-VSP data through the complexity of forward models and the diversification of physical parameters. In addition, we propose a new mean square error (MSE) loss function combined with an energy ratio matrix (ERM). The ERM can adjust the SNR between the signal patch and noise patch during the network training and thus increase the robustness of trained CNN denoising model for the DAS-VSP data with different SNRs, especially the DAS-VSP data with extremely low SNR. Both synthetic and real experiments prove the effectiveness of the proposed L-FM-CNN.

Enhanced surface seismic data processing using simultaneous acquired DAS-VSP data

Distributed Acoustic Sensor (DAS) is increasingly recognized as a viable alternative to downhole geophone arrays for the acquisition of borehole seismic (VSP – Vertical Seismic Profile) or DAS-VSP data. When acquiring surface seismic data in the field, simultaneously acquired DAS-VSP data in an available well or wells can provide full well VSP imaging and significantly enhance surface seismic data processing. Borehole seismic data can provide higher resolution structural images around the borehole as the receivers are located inside a borehole and near the reservoir. In addition, the formation velocity, deconvolution operator, absorption, attenuation (Q value), anisotropy parameters and other reservoir formation information in a certain range around the wellbore can be obtained directly from the VSP data. These can be used to significantly enhance the surface 3D seismic data processing, such as velocity model calibration and modification, static correction, deconvolution, demultiple processing, high frequency restoration, anisotropic migration, and Q-compensation or Q-migration. In this project, Q-migration was conducted with the enhanced Q-field data volume obtained from the joint inversion of both the near surface 3D Q-field data volume from uphole data and the mid-deep layer Q-field data volume from VSP data. The Q-migration results show much sharper and more focused faults and fracture zones. A new 3D formation top structural model was built with much finer details based on the Q-migration data, and three new borehole locations were recommended to the client. The final drilling results on these three new boreholes are very successful with high volume of commercial oil flow.