Conventional Seismic Data Research Articles

Cross‐equalization for time‐lapse sparker seismic data

AbstractTime‐lapse seismic data processing is an important technique for observing subsurface changes over time. The conventional time‐lapse seismic exploration has been conducted using a large‐scale exploration system. However, for efficient monitoring of shallow subsurface, time‐lapse monitoring based on the small‐scale exploration system is required. Small‐scale exploration system using a sparker source offers high vertical resolution and cost efficiency, but it faces challenges, such as inconsistent waveforms of sparker sources, inaccurate positioning information and a low signal‐to‐noise ratio. Therefore, this study proposes a data processing workflow to preserve the signal and enhance the repeatability of small‐scale time‐lapse seismic data acquired using a sparker source. The proposed workflow has three stages: pre‐stack, post‐stack and machine learning–based data processing. Conventional seismic data processing methods were applied to enhance the quality of the sparker seismic data during the pre‐stack data processing stage. In the post‐stack processing stage, the positions and energy correction were performed, and the machine learning–based data processing stage attenuated random noise and applied a matched filter. The data processing was performed using only the seismic signals recorded near the seafloor, and the results confirmed the improvement in the repeatability of the entire seismic profile, including that of the target area. According to the repeatability quantification results, the predictability increased and the normalized root mean square decreased during data processing, indicating improved repeatability. In particular, the repeatability of the data was greatly improved through vertical correction, energy correction and matched filtering approaches. The processing results demonstrate that the data processing method proposed in this study can effectively enhance the repeatability of high‐resolution time‐lapse seismic data. Consequently, this approach could contribute to a more accurate understanding of temporal changes in subsurface structure and material properties.

Distributed acoustic sensing for seismic surface wave data acquisition in an intertidal environment

The application of distributed acoustic sensing (DAS) for shallow marine seismic investigations is assessed, in particular with respect to the collection of seismic surface wave data in an intertidal setting. Appropriate selection and directional sensitivity of fiber-optic cables is considered and the measured data is validated with respect to conventional seismic data acquisition approaches, using geophones and hydrophones, along with independent borehole and seismic cone penetration test (SCPT) data. In terms of cable selection, a reduction in amplitude and frequency response of an armored cable is observed, when compared with an unarmored cable. For seismic surface wave surveys in an offshore environment where the cable would need to withstand significant stresses, the use of the armored variant with limited loss in frequency response may be acceptable from a practical perspective. The DAS approach also has indicated good consistency with conventional means of surface wave data acquisition, and the inverted [Formula: see text] also is very consistent with downhole SCPT data. Observed differences in phase velocity between high tide (Scholte wave propagation) and low tide (Rayleigh wave propagation) are not thought to be related to the particular type of interface wave due to shallow water depth. These differences are more likely to be related to the development of capillary forces in the partially saturated granular medium at low tide. Overall, this study demonstrates that our novel approach of DAS using seabed fiber-optic cables in the intertidal environment is capable of rapidly providing near-surface S-wave velocity data across considerable spatial scales (multikilometer) at high resolution, which is beneficial for the design of subsea cables routes and landfall locations. The associated reduction in deployment and survey duration, when compared with conventional approaches, is particularly important when working in the marine environment due to potentially short weather windows and expensive downtime.

Using 3D Seismic Technology to Accurately Identify Faults and Reservoir Characteristics

With the accelerating development of the Chinese economy, the demand for oil and gas energy is increasing day by day. The accuracy and efficiency of fault interpretation are crucial for the exploration and development of oil and gas reservoirs. The presence of oil and gas in reservoirs will inevitably cause changes in the geophysical response characteristics, and different types of reservoirs have different response characteristics in logging curves such as sound, discharge, and electricity. 3D seismic technology is currently one of the most effective techniques for obtaining fault structural features and identifying small faults, but conventional seismic data processing techniques are unable to meet the accuracy requirements of current seismic exploration. With the development of computer technology, more and more scholars are applying deep learning (DL) algorithms to the identification of faults and reservoirs to further improve the recognition effect. This article is based on the DL algorithm and utilizes 3D seismic technology to design a method for automatic and accurate recognition of fault and reservoir features. Obtain labeled seismic amplitude faults, reservoir data, and seismic amplitude data to be identified from the 3D seismic amplitude data volume, and construct a CNN training model to identify the fault and reservoir data. The experimental results show that the method designed in this article can improve the efficiency and accuracy of fault and reservoir identification.

Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning

Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. However, its distinct characteristics, such as unknown ground coupling and high noise level, pose challenges to signal processing. Existing machine learning models optimized for conventional seismic data struggle with DAS data due to its ultra-dense spatial sampling and limited manual labels. We introduce a semi-supervised learning approach to address the phase-picking task of DAS data. We use the pre-trained PhaseNet model to generate noisy labels of P/S arrivals in DAS data and apply the Gaussian mixture model phase association (GaMMA) method to refine these noisy labels and build training datasets. We develop PhaseNet-DAS, a deep learning model designed to process 2D spatio-temporal DAS data to achieve accurate phase picking and efficient earthquake detection. Our study demonstrates a method to develop deep learning models for DAS data, unlocking the potential of integrating DAS in enhancing earthquake monitoring.

New wiggles from old cables: Acquisition, cable, and data aspects

Seismic imaging is essential for detailed characterization and understanding of the subsurface. Traditionally, marine seismic data have been acquired either using towed streamers or ocean-bottom receivers. Distributed acoustic sensing (DAS) is a novel and fast-emerging technology that makes it possible to use fiber-optic cables for acoustic measurements. The technology turns the fiber-optic cable into a densely sampled receiver array. A controlled source experiment comparing DAS and conventional towed streamer data for seismic imaging purposes is described. An existing 130 km telecommunication fiber-optic cable was interrogated to acquire DAS data to analyze the signal at long distances and for subsurface imaging. A seismic source vessel was used to create seismic data along the full cable length, while at the same time acquiring conventional towed streamer data for comparison. In addition, the telecommunication fiber-optic cable was used to acquire passive DAS data. The results show that seismic signals in the telecommunication fiber-optic cable are detectable at distances above 100 km from the source point. Furthermore, comparisons of the DAS data and towed streamer data show that the DAS technology allows for the acquisition of large offset seismic data that are comparable with conventional seismic data. Analysis of the passive DAS data shows that the infragravity waves correlate with the observed weather conditions during the acquisition.

Analysis of spatial structure and filling mechanism of a paleokarst channel against a background of denudation: A case study in the Tahe Oilfield, Tarim basin

Accurate delineations of the formation chronology and sedimentary characteristics of underground rivers are crucial for prospecting karst reservoirs. Based on drilling records, core samples, seismic data, and thin-section microscopy, qualitative and quantitative analyses of the fillings within the S65 karst channel, located within the erosional zone of the Tahe Oilfield Conventional seismic data, acoustic impedance inversion seismic data, mud inversion seismic data, and well drilling data were used to elucidate the lateral variations and combinations of fillings. Key coring wells were selected, and cores were calibrated using logging data on acoustic time difference (AC), density (DEN), deep-shallow dual lateral resistivity (RD-RS), natural gamma ray (GR), and shale content (Vsh). A range of quantitative benchmarks were thus established to distinguish between various types of fillings. Discrimination between breakdown breccia and calcite fillings was achieved using AC-DEN crossplots. The former exhibited elevated DEN values (2.64–2.72) coupled with moderate AC values (>49.5). Conversely, the latter displayed higher DEN values (2.66–2.76) and lower AC values (<49.5). Mechanical components and unfilled or low-filled components were identified using GR-RS or △R-Vsh crossplots, where △R signified the absolute difference between deep and shallow resistivity measurements, with a GR value of 20 API and a Vsh value of 5% within these plots representing the boundary between the two. The filling characteristics of the underground river fillings, and the mechanisms underlying the formation of these fillings were explained. A comparative analysis of the channels during different geological epochs revealed the following: (i) Sandstone and sandstone-cemented breakdown breccia that were primarily filled with a combination of mechanical and collapse fillings developed in the doline. Mudstone and calcite developed in the corridor region, whereas breakdown breccia developed at the intersections of fault lines. Uncemented breakdown breccia was frequently encountered in the branches of the underground river, with calcite developed on the blind side. (ii) The top channel of the S65 underground river formed during a period of rising sea levels in the Carboniferous. The poor lateral reservoir connectivity resulted from the extensive infiltration of mud. In contrast, the bottom channel, primarily consisting of Silurian sandstone and sandstone-cemented breakdown breccia, formed during the denudation phase of the early Hercynian. This bottom channel played a significant role in the substantial oil and gas production within the underground river system.

Revealing subsurface subtle channel boundaries and fluid discrimination by employing time-frequency analysis on pre-stack seismic data, a case study: Simian Field, Offshore Nile Delta, Egypt

It is quite difficult to determine the interior depositional architecture and fluid signature of slope channel systems in offshore deltas by using conventional seismic data as occasionally the subsurface channels might not give a clear response to the seismic images. Utilizing the spectrum decomposition approach, the Pre-stack seismic data is highly beneficial for depositional and hydrocarbon highlights. Far-angle stacks are also better for fluid identification than near-angle stacks because they have more low-frequency components and a lower signal-to-noise ratio (SNR) than near-angle stacks, which are more marked by a depositional signature.In the present study, the spectral decomposition approach, which concentrates on processing Discrete Fourier Transform (DFT) around a specified structural map, is implemented to transpose the amplitude or phase data into the frequency domain. Red-Green-Blue (RGB) blending map was extracted from the near and far-stack seismic data, through which the channel depositional geometries become clear. However, utilizing RGB on the far stack made the fluid signature fairly obvious.

Low-order fault identification method of Putaohua oil layer of X block in Songliao Basin

With the exploration and development of oil fields to the later stage, the distribution of remaining oil is highly scattered, and the oil control area becomes smaller. It is necessary to carry out fine identification and description of small geological interfaces, so as to improve the accuracy of reservoir description. Therefore, low-level fault identification has become an extremely important link. However, due to the influence of seismic data resolution, it is often difficult to directly interpret small faults on conventional seismic profiles. In conventional seismic data, fault interpretation is often based on seismic attribute analysis. At present, there are many kinds of seismic attributes used for fault interpretation, such as coherence class, curvature body attribute, ant attribute body and so on. By comprehensively utilizing the time slices and seismic profiles of these seismic attributes, small faults can be identified more effectively. In the process of studying the three-dimensional work area of Putaohua oil layer of X block in Songliao Basin, the above methods are used in combination with various information such as seismic profiles to identify small faults that are difficult to identify on conventional seismic data volume and coherence volume, and good application results have been achieved.

HDF5eis: A storage and input/output solution for big multidimensional time series data from environmental sensors

Modern high-performance computing (HPC) tasks overwhelm conventional geophysical data formats. We describe a new data schema called HDF5eis (read H-D-F-size) for handling big multidimensional time series data from environmental sensors in HPC applications and implement a freely available Python application programming interface (API) for building and processing HDF5eis files. HDF5eis augments the popular Hierarchical Data Format 5 with a minimal set of additional conventions that facilitate fast and flexible data input and output protocols for regularly sampled (in time) data with any number of dimensions. HDF5eis supports arbitrary ancillary data (e.g., metadata) storage in columnar format or as UTF-8 encoded byte streams alongside time series data. Our HDF5eis API enables simple and efficient access to big data sets distributed across a potentially large number of small heterogeneous files through a single point of access. HDF5eis outperforms conventional seismic data formats by up to two orders of magnitude in terms of random read access times. We contribute HDF5eis as an operational tool and an experimental draft proposal that will help establish the next generation of data standards in the earth sciences.

Application of deconvolution interferometry to receiver ghost reflection in vertical cable seismic survey

AbstractMarine vertical cable seismic is to probe targets near the submarine with use vertical hydrophone array suspending in deep sea water. Despite imaging of primary reflections from vertical cable seismic data can provide seismic profiles with high resolution, its submarine illumination is much narrower than that of conventional towed‐streamer seismic owing to the irregular geometry. In view of the fact that imaging of multiples can provides better subsurface illumination with fine resolution, we present a strategy for imaging of receiver ghost reflections from vertical cable seismic data, based on seismic interferometry by deconvolution. This method can convert first‐order receiver ghosts into virtual primaries from the towed‐streamer data without velocity model and source‐receiver position. We illustrate the deconvolution interferometry for the receiver ghost reflections with the real vertical cable seismic data in Shenhu area, South China Sea and further obtain virtual primary reflections. Finally, we use these virtual primaries to successfully construct a stacked image and obtain a post‐stack migration image with conventional seismic data process method. This stacked and migrated image significantly extends the submarine illumination with higher resolution and it is useful in characteristics recognition of hydrate‐bearing sediments and free gas.

Deep learning-based attenuation for shear-wave leakage from ocean-bottom node data

The high-amplitude shear wave in the z-component of ocean-bottom node (OBN) data challenges conventional seismic data imaging and interpretation. The process of shear-wave attenuation from the z-component data can be regarded as a coherent noise reduction problem. However, conventional noise attenuation methods, such as filtering or matching and subtraction, are time-consuming or have limited accuracy for field data processing. Unlike conventional methods with indirect feature recognition, pretrained neural networks provide end-to-end denoising frameworks by directly predicting and subtracting the shear-wave-free data from the original z-component data. Here, we have developed a deep learning-based method to address the shear-wave leakage problem and adopted a training set generation strategy based on the field data. Denoised z-component data are not needed to train the network. The generation of the training set and the extraction of noise features are based on the data of the x- and p-components. A residual learning strategy is introduced to extract the noise from the field data other than those in the training set. Synthetic examples confirm the excellent performance of our training set generation strategy. Field 4C OBN examples show that our method can attenuate shear-wave noise without harming the signals and, therefore, can be integrated into multicomponent field data processing.

A multi-scale dense-connection denoising network for DAS-VSP records

Due to high spatial resolution, low cost, and wide bandwidth, distributed optical fiber acoustic sensing (DAS) is regarded as a potential tool for data acquisition in vertical seismic profile (VSP) surveys. However, in real DAS-VSP records, desired signals are often seriously plagued by various noise, which does not appear in the conventional seismic data received by electronic geophones. Exploring a high-performing attenuation method for the background noise can significantly improve the quality of DAS-VSP records and has essential impacts on the following imaging and interpretation. Deep-learning-based methods, especially convolutional neural network (CNN), have shown remarkable performance in seismic data denoising. However, the conventional CNN-based methods may degrade when dealing with DAS-VSP records in low signal-to-noise ratio. In this study, we propose a novel multi-scale dense-connection denoising network (MDD-Net) to achieve high-accuracy processing of the complex DAS background noise. Unlike conventional multi-scale networks, MDD-Net utilizes widen convolution block to capture the multi-scale features of the analyzed data. On this basis, dense connection operations are employed to fuse the features and improve the network efficiency. Meanwhile, an enhanced spatial attention (ESA) block is designed to reinforce the features, which are helpful for noise suppression and weak signal recovery. Both synthetic and field DAS-VSP records are processed to verify the effectiveness of MDD-Net. Meanwhile, we also compare the denoising results with other competing methods. The experimental results demonstrate that MDD-Net can significantly attenuate the complex DAS background noise and restore the desired signals, even for the weak upgoing signals.

Multi-scale interactive network in the application of DAS seismic data processing

Distributed acoustic sensing (DAS) is regarded as a novel acquisition technology for seismic data. Compared with conventional electrical geophones, DAS has a series of obvious advantages including low-cost, high spatial resolution, good coverage, and strong resistance to the harsh environment. Noise attenuation is an essential step in seismic data processing. However, there are two main difficulties faced by the denoising task of DAS seismic data. On the one hand, some background noise in DAS seismic data, such as optical low-frequency noise, horizontal noise, and fading noise, is unique and not presented in the conventional seismic data; on the other hand, the signal-to-noise ratio (SNR) of DAS seismic data is relatively low. Recently, a convolutional neural network (CNN) has shown superior denoising performance compared to the traditional method. To follow this promising trend, we propose a multi-scale interactive convolutional neural network (MSI-Net) and apply it to denoise the challenging DAS seismic data. Different from most of the existing CNN architecture used in seismic data denoising, the MSI-Net considers both coarse-scale and fine-scale features by improving the inherent serial convolution to multi-scale parallel convolution, which is beneficial to recover detailed information. Moreover, we utilize some connections to achieve the information interaction between different scales, which promotes the flow of information and enables the network to extract more informative multi-scale features from the DAS seismic data. Moreover, both synthetic and real examples demonstrate that the proposed MSI-Net can effectively attenuate a variety of unique DAS background noise and also completely recover the weak signals. Compared with conventional CNN architecture, MSI-Net exhibits better performance in global SNR and local details.

RMCHN: A Residual Modular Cascaded Heterogeneous Network for Noise Suppression in DAS-VSP Records

Distributed optical fiber acoustic sensing (DAS) is an emerging acquisition technology in seismic exploration. However, DAS records are always affected by the complex background noise, resulting in a low signal-to-noise ratio (SNR). In addition, the DAS background noise has different properties from the noise existing in conventional seismic data. Thus, conventional denoising methods may degrade the record when dealing with complex DAS data. To improve the denoising capability, a novel denoising network, called residual modular cascaded heterogeneous network (RMCHN), is proposed. In general, the network is based on the idea of heterogeneous convolution and modular convolutional neural networks. Specifically, different modules are designed to extract the discriminatory features of the DAS data through effective information integration. On this basis, heterogeneous convolution combined with long and short path feature learning strategy is employed to fuse the captured features, thereby improving the feature expression capability and avoiding the information loss. Both synthetic and field denoising results indicate that RMCHN can suppress the DAS background noise with excellent performance in signal restoration, even for the weak signals form deep strata.

Rotational Components of Normal Modes Measured at a Natural Sandstone Tower (Kane Springs Canyon, Utah, U.S.A.)

AbstractModal analysis of freestanding rock formations is crucial for evaluating their vibrational response to external stimuli, aiding accurate assessment of associated geohazards. Whereas conventional seismometers can be used to measure the translational components of normal modes, recent advances in rotational seismometer technology now allow direct measurement of the rotational components. We deployed a portable, three-component rotational seismometer for a short-duration experiment on a 36 m high sandstone tower located near Moab, Utah, in addition to conducting modal analysis using conventional seismic data and numerical modeling. Spectral analysis of rotation rate data resolved the first three natural frequencies of the tower (2.1, 3.1, and 5.9 Hz), and polarization analysis revealed the orientations of the rotation axes. Modal rotations were the strongest for the first two eigenmodes, which are mutually perpendicular, full-height bending modes with horizontal axes of rotation. The third mode is torsional with rotation about a subvertical axis. Measured natural frequencies and the orientations of displacements and rotation axes match our numerical models closely for these first three modes. In situ measurements of modal rotations are valuable at remote field sites with limited access, and contribute to an improved understanding of modal deformation, material properties, and landform response to vibration stimuli.

High-resolution imaging: an approach by compensating absorption and dispersion using inverse Q filtering

We have presented an inverse filtering scheme that can compensate absorption and dispersion caused by intrinsic attenuation in subsurface media with a heterogeneous model. We have adopted two methods to suppress high-frequency noise at the same time, one is to design a compensation operator with a fixed gain limit, and the other is to introduce an adaptive frequency-varying band calculation method. We use VSP data and seismic velocity data to estimate model of the whole work area in a unique way. The proposed scheme can be incorporated into conventional seismic data processing workflow. Tests on synthetic and real data set demonstrate effectiveness of the proposed inverse filtering.

From Seismic Pre-Stack Elastic Attributes to Rock Properties. A Case Study in the Permian Basin, Onshore USA

The Permian Basin contains significant oil and gas-bearing shale deposits. Extending over 55 counties in West Texas and southeast New Mexico, it covers a vast region. It is the largest contributor to the oil shale boom in the US and in February 2022 accounted for more than 40% of US oil, Figure 1 (Federal Reserve Bank of Dallas 2022). Figure 2 displays the increase in produced oil from this basin from 2018 to 2022, Chevron News Room 2022. During the first years of production from these tight shales, seismic data weren’t considered to be essential for locating and drilling high-producing wells. However, the value of seismic data are now recognized and extensively used to design and execute lateral drilling programmes into the most prolific shale units. The shale deposits are widespread but highly heterogeneous in composition. Specifically notable is the lateral variability of Total Organic Content (TOC) and rock properties, such as porosity within the formations. In addition to varying shale properties, carbonate units were deposited simultaneously and are difficult to distinguish when only using reflectivity. Traditionally, the sonic and density values were inputted into supervised and non-supervised neural network applications to estimate rock properties. However, it is difficult to calculate density from conventional seismic data, and the results are typically questionable and have a high uncertainty element. To calculate density from seismic data, we need long offsets that are usable; most of the time, these data are not available (Aki, K., 1980). This study showcases a unique approach and its application using the LambdaRho-MuRho cross plot to estimate rock properties. The LambdaRho and MuRho values are derived from pre-stack inversions and are correlated to existing well data over the zone of interest. The results indicate that this is a more elegant and valid methodology, especially since it seems to produce a better, more accurate distinction between shale and carbonate domains.

Enhancing body waves in passive seismic reflection exploration: A case study in Inner Mongolia, China

Among all geophysical exploration methods, seismic exploration is undoubtedly the most important due to its ability to allow depth exploration at high resolutions. Traditionally speaking, the method needs an active seismic source, such as dynamite, to generate energy and perform reflection and refractions. An active source usually means high cost, and it also can be quite difficult to implement when surface conditions are particularly complex. The use of passive seismic for reflection exploration does not require an active seismic source. It has the potential of providing a low-cost alternative technique in some exploration areas. The main issues related to passive-source body-wave exploration include suppressing the surface waves retrieved from sources located at or near the surface, as well as enhancing the body waves from random sources at depth. We address these problems by developing a preprocessing workflow to suppress the surface waves and the other unwanted coherent noise events in the original data without seriously affecting the remaining body waves. We also propose a method for separating surface and body waves based on the signal-to-noise ratio of the frequency-domain signals. Next, we use crosscorrelation to generate virtual shot gathers, just like the active ones, and use conventional seismic data processing steps to generate the final seismic imaging. By analyzing the passive data set collected from Inner Mongolia, we have verified the applicability of the proposed method, and the retrieved final stack section indicates good consistency with an active seismic stack section along the same line. Accordingly, we assert that the application of this data processing method will contribute to the body-wave imaging and the inversion analysis of passive seismic records.

Diffraction imaging: A valuable complement to reflection imaging for sand injectite interpretation in the Norwegian North Sea

Interpretation of sand injectite reservoirs from conventional reflection seismic data is a complicated exercise. The complex geometries of injectites are challenging to detect and resolve by using conventional seismic processing due to their lateral and vertical variability in thickness and dip. Steeply dipping injection dikes tend to appear blurred or distorted on reflection images, and interpretation of their positioning and thickness carries an important level of uncertainty. Structural attributes derived from reflection images are used to support interpretation, but they suffer from a lack of spatial resolution and vertical continuity. We introduce a novel interpretation workflow that includes a diffraction image in addition to the conventional reflection image to improve the detectability and resolution of steep injection dikes and small-scale features. The diffraction image is generated in a migrated dip-angle domain where diffracted and reflected energy can be separated. A case study from a large injection complex in the Norwegian North Sea illustrates the superiority of the diffraction image over conventional structural attributes to support the interpretation and characterization of thin and steeply dipping injectites. The complementarity of the reflection and diffraction images is exploited by visualization techniques (e.g., corendering) or by combining interpretations from separate images, providing a complete picture of the injection complex geometries. Diffraction imaging proved to be a valuable tool to improve the level of detail and confidence in the interpreted injectites, both for modeling and well-planning purposes.

A Numerical Experiment of Full Waveform Inversion of Complex Structures Concealed Around a Horizontal Hydraulic Fracturing Well Using Perforation Seismic Data

It is difficult to image the details of complex structures concealed around a horizontal hydraulic fracturing well using seismic data from the ground surface. In this paper, an approach is proposed to solve this problem by non-linear full waveform inversion (FWI) using perforation seismic data. The feasibility of the approach was investigated using numerical modeling based on an experimental model built from the well-known SEG/EAGE overthrust model, which contains complex geological structures with faults. First, seismic modeling was performed to produce experimental synthetic data, including three sets of perforation seismic data recorded by different acquisition systems deployed in observation wells and on the ground surface, and another set of conventional seismic reflection data with both sources and receivers deployed on the ground surface. Then, FWI was performed separately on each data set using an initial velocity model which was heavily smoothed to remove the target structures. The inversion results show that the concealed complex structures around the well were successfully recovered by FWI using perforation data, while the benchmark image from the FWI using conventional seismic data was poor. Particularly, the experiments also demonstrated that FWI using perforation seismic data can image the faults around a horizontal hydraulic fracturing well, while this is unable to achieve using conventional ground surface seismic data. This conclusion was also proved to be valid for noisy data deteriorated either by synthetic Gaussian or field noises. Further experiments demonstrated that FWI using perforation data recorded from wells outperformed that of surface data in terms of structure imaging accuracy characterized by quantitative errors.