Distributed fiber optic sensing technology holds unparalleled advantages in oil and gas development.In this paper, we delve into the fundamental principles of distributed fiber optic sensing and borehole deployment methods, and provide an overview of its application in various petroleum engineering fields, e.g.hydraulic fracturing monitoring, production logging with coiled tubing, adjacent well fracturing surveillance, injection and production monitoring, gas storage safety monitoring, and casing damage detection.Our study reveals that fiber optic monitoring is an economically efficient form of surveillance with the potential to provide real-time monitoring across the entire lifecycle of oil and gas wells, and represents a cutting-edge, scientifically sound, and highly secure production logging technology.The paper also highlights that the future application of distributed fiber optic sensing in oilfields should concentrate on sensor performance enhancement, more application scenarios of fiber optic monitoring, better data processing and interpretation, and cost reduction to strengthen its application in oil and gas field development.

Nodal seismic data acquisition is a significant transformation in the technical development of seismic prospecting.Due to their multiple advantages and characteristics, nodal seismometers have been widely used in seismic data acquisition, which significantly improves the adaptability and flexibility of seismic prospecting in complex surface environments such as mountains.This technology reduces the labor intensity and workload of field operation, and it also improves production efficiency and HSE performance.However, nodal seismic prospecting still faces a series of challenges.The development of nodal seismic technology indicates not only a change in seismic instruments, but also the changes in the way of seismic data acquisition, processing, and utilization.Nodal seismometers will develop toward the directions of sustained performance optimization, serialization and diversification, integrated observation of multiple physical fields, feasibility for complicated environments, and related supporting equipment.The change from centralized to distributed seismic data acquisition by utilizing nodal seismometers have optimized and altered the workflow and management of field operation.The way of distributed nodal acquisition is evolving towards long-term continuous recording, integrated acquisition of active and passive sources, coexistence of dense and sparse acquisition, real-time monitoring and real-time data transmission, automatic node deployment and recovery.Nodal seismic data processing will evolve towards automated processing workflow and intelligent analysis, which would be supported by seismic processing of active-source and passive-source data as well as data of irregular geometry.The innovative incremental data processing and corresponding workflow is a completely new way of processing, which can be carried out using a type of real-time automated processing workflow in some scenarios.Nodal seismic data will be utilized in a diversity of fields and scenarios, including in-depth utilization of ambient noises from passive sources and integrated utilization of signals from active and passive sources, with an integrated software and hardware system for seismic data acquisition, processing and interpretation.Further efforts should focus on three aspects.The first is a system of nodal seismic instruments, acquisition, processing and application.The second is the integrated development of nodal seismic technology and the new generation of information communication technology guided by application scenarios.The third is an open-source architecture and ecosystem for the application of nodal seismometers and nodal seismic data.

In marine seismic exploration,obtaining accurate air-gun source wavelets is of great significance for high-resolution processing and source quality monitoring.To address the problem that existing algorithms of air-gun source wavelet simulation cannot fully consider the real-time state of the air gun,leading to great differences between the simulated wavelet and the actual situation,a method is proposed to simulate the far-field wavelets of air-gun sources in the frequency domain based on the near-field hydrophone signals.Some internal and external factors taken into account involve hydrophone sensitivity measured by using single-gun excitation tests,relative bubble motion calculated by using bubble upward-floating timing tests and real-time navigation data of gun array,as well as ghost reflections from sea surface.The impact and applicability of these factors on the simulation results are also tested.Compared with the conventional method for wavelet simulation based on source design parameters,the far-field wavelet with high fidelity simulated using the proposed method can reflect the real excitation state of an air-gun source.The simulated wavelet can be used for seismic wavelet processing to mitigate bubble effect and improve seismic resolution significantly,which is of great practical significance.

Geophysical technology has been extended to reservoir development for several decades.Dramatic progresses have been made in target-oriented imaging, rock physics, multi-constrained inversion, seismic constrained and driven modeling, reservoir dynamics monitoring and characterization, and multi-data integration and artificial intelligence.The extension of geophysical technology to reservoir development and engineering is a natural trend and needs for geophysics itself, but also is a need for better reservoir development and production.Reservoir geophysics has its own characteristics due to the changes in data condition, dominant technical problems and targets compared to exploration geophysics.This paper reviews and summarizes the development and status for reservoir geophysics.The goal is to share author's thinking for future trends and development in this area.Hope this could inspire readers for this area.

The approach of 90° phase shift has been espoused as one of the core techniques in seismic-based sedimentology since 1998, when systematic researches were launched on the theories and application techniques of seismic-based sedimentology.However, its practical implementation is unsatisfactory.According to the philosophy of seismic prospecting, seismic events represent stratigraphic interfaces with impedance contrasts.The practice of 90° phase shift is intended to transform interfacial events into rock formations.Subsurface complexities often show as frequently interbedded thin layers, the interfaces of which could not be one-to-one correlated with seismic reflections as per forward modeling.This means that a reflection is the combination of seismic responses from a certain number of thin layers.Due to the limitation of seismic resolution, the operation of 90° phase shift cannot convert seismic data from interfacial events into lithologic sections; besides, such an operation is geologically meaningless and cannot yield good results for structure and lithology interpretation.Consequently, it may not be treated as a key technique in the study of seismic-based sedimentology, as it neither improves resolving power of seismic data nor yields lithologic sections from seismic sections.

In China, seismic surveys deal with increasingly small and complex targets. Improved seismic resolution requires the downsizing of underground sampling grids. Conventional regular sampling methods are extremely costly. Compressed-sensing irregular sampling can design non-equally spaced shot and receiver points, without increasing investment, to obtain a uniform discrete distribution of CMP points and an irregular 3D data volume. The regularized reconstruction of irregular data with higher density has become a key issue in imaging. There are various reconstruction methods, most of which cannot balance accuracy and efficiency. Based on the compressed sensing theory, this paper uses a reconstruction method based on 3D curvelet transform, which can effectively capture the anisotropic and orientation features of seismic events for their optimal sparse representation. An algorithm of projection onto convex sets (POCS) is introduced to improve reconstruction accuracy. An optimization strategy with f-x domain conversion and OpenMP parallel acceleration is used to improve computational efficiency. This method realizes the reconstruction of irregularly acquired data with high density, high efficiency, and high precision based on compressed sensing. The application to the Guangli-Qingnantan shallow sea survey in Shengli Oilfield shows that the proposed method has high accuracy, high computational efficiency, and better imaging with improved resolution than a conventional regularly sampled high-density survey.

Xinjiang with multi-package organic-rich shales is among the key regions for shale oil and gas exploration and development in China.Among 34 sedimentary basins in Xinjiang, 6 basins were evaluated to have potential shale oil and gas resources; however, systematic resources assessment has as yet not been performed.This paper systematically reviews the progress of shale oil and gas exploration and development in Xinjiang, and summarizes the geological characteristics of shale oil and gas in the Paleozoic and Mesozoic Erathems.In terms of prospect optimization and resources assessment by using the probabilistic volumetric method for shale oil and gas in the Cambrian, Ordovician, Carboniferous, Permian, Triassic and Jurassic Systems in the Junggar, Yanqi, Tuha, Tarim, Yili and Santanghu Basins in Xinjiang, there are 5 shale oil prospect areas and 23 shale gas prospect areas, with potential resources of 650.19×10⁸ t and 32.13×10¹² m³, respectively.There are six favorable areas for shale oil exploration and six favorable areas for shale gas exploration, with potential resources of 70.06×10⁸ t and 2.30×10¹² m³, respectively.According to hierarchical evaluation results and the present situation of exploration and development, it is suggested that the Permian System in the Junggar and Santanghu Basins may contribute to shale oil reserves and production increase, and shale gas exploration may concentrate on the Permian system in the piedmont zone of the Bogda Mountain, where there is shale gas associated with shale oil, and additional areas with multi-layer shale gas distribution.

Interbed multiples suppression is a thorny problem in seismic exploration. To promote its development, we investigate the evolution of interbed multiples prediction and adaptive subtraction and give an overview of the methods of high concern, which include common focus point, surface data separation, inverse scattering series, virtual event, and Marchenko autofocusing; we also discuss their advantages and disadvantages. For the common-focus-point method, the layer-related algorithm has stronger adaptability than the boundary-related algorithm, but its computational expense is also high. Inverse scattering series method has high accuracy of multiples prediction, but its application is limited by the large amount of computation. Despite their high computational efficiency, surface data separation and virtual event methods rely too much on manual operation to accomplish data separation. Marchenko autofocusing method is unproved in practical use. The limitations of adaptive subtraction in the context of non-orthogonal primary waves and multiples leads to the generation of inversion-based interbed multiples suppression. The focus is how to improve computational efficiency and practicability on the premise of high accuracy. A potential direction is to combine interbed multiples suppression with waveform inversion and deep learning.

Due to low broadside sensitivity of conventional straight DAS,distributed acoustic sensing has seldom been applied to surface seismic acquisition to receive reflected signals with the ray direction almost perpendicular to the fiber axis.As an upgrade,the helical wound cable (HWC) with enhanced broadside sensitivity makes it possible to popularize DAS in surface seismic acquisition.Based on the analysis of the relationship between the relative strain of an optical fiber and its winding angle and incident angle as well as the technical difficulties and production cost of current distributed HWCs,a pilot test is performed using a distributed HWC of 2km long with two winding angles of 30° and 60° laid in a river in Jiangsu Province.Through the comparative analysis of HWC and nearby node data as well as shot gathers and stacked sections of HWC data at different winding angles,distributed HWCs with winding angle 30° can receive seismic reflections with clear continuous wavetrain features.The underwater experiment provides technical support for seismic exploration using distributed HWCs.

Due to the advancements of seismic data acquisition and imaging with "wide band, wide azimuth, and high density", thin layers, small-scale (fractured-vuggy) anomalous bodies, and small-throw faults (strike-slip faults), etc. have become the main target geological bodies for high-fidelity and high-resolution imaging. In current seismic exploration, reasonable observation system design has received sufficient attention, but how to achieve customized high-resolution wavelets for the customers is rarely discussed. In theory, vibroseis is a method of obtaining the amplitude spectrum of expected seismic wavelet through the accumulation of single-frequency energy within time intervals in the frequency and time domain. Based on this, we propose the concept of customized high-resolution seismic wavelets and corresponding sweep signals for the customers. The first step is to generate (or customize) a zero-phase wavelet, which meets the requirements of high-fidelity and high-resolution on a specific target layer, and obtain its corresponding amplitude spectrum. The second step is to establish the mapping relationship between the amplitude spectrum of the customized wavelet and the vibrator sweep signals. Finally, the vibrator sweep signals are designed under the assumption of linear sweep frequency. We use formula derivation and model tests to demonstrate the feasibility of our method. Forward modeling and migration tests also show that the customized high-resolution wavelet could be used in high-resolution processing.

The classical least-squares migration is implemented based on a waveform misfit function in the data domain to accomplish quantitative inversion of subsurface reflectivity.However,it is hard to achieve remarkable results due to unknown wavelet,inaccurate migration velocity,and unacceptable computational cost.To address these issues,we provide a practical least-squares reverse-time migration method in the image domain.This method adopts a background velocity field and a given wavelet with real frequency band to compute the globally spatial-varying point-spread function,and uses an image-domain high-dimensional spatial deconvolution algorithm for high-resolution imaging.This method sidesteps seismic waveform misfit,and focuses on the illumination of the point-spread function at different wavenumbers.As a result,the accuracy of the seismic wavelet and migration velocity does not make a strong impact on imaging.Moreover,one-iteration imaging with high computational efficiency facilitates its application to 3D seismic exploration in ultra-deep zones.Two case studies dealing with different reservoir types in northwestern China demonstrate that this method can improve imaging resolution for the description of fracture-cave structures and thus offer technical support to reserves and production increase in ultra-deep carbonate reservoirs.

This article first proposes that several linear structures (which can be regarded as local plane waves) float in local high-dimensional data volume with different probability distribution characteristics, which is the core conceptual mode of seismic signal processing.It is believed that modeling and optimal prediction of linear structures in local high-dimensional data volumes, in order to solve the problems such as denoising, data regularization, and deblending, are the very basic steps in seismic data processing.It is considered that the optimal modeling and prediction of linear signals include model-driven and data-driven methoel.The former represents the signals contained in the local high-dimensional data volume by the linear superposition of pre-selected local plane wave basis functions.The latter uses the data matrix (tensor) decomposition method to infer the linear structure contained in the local high-dimensional data volume.Then, the basic theories of high-dimensional Wiener filtering method, autocorrelation matrix and Hankel matrix orthogonal decomposition method (SSA method), high-dimensional linear Radon transform method (high-dimensional Beamforming method), and tensor decomposition method in the frequency space domain were comprehensively analyzed, and a theoretical foundation for linear signal prediction and various applications in local high-dimensional data volume is built.Finally, it is pointed out that the coherent noise and incoherent noise in the real data of the piedmont zone and other complex surface exploration areas often seriously deviate from the theoretical assumptions of linear signal modeling and prediction, developing nonlinear denoising methods is also inevitable.

Micro-logging is widely employed in near-surface velocity investigations in seismic exploration,but its lateral resolution is low due to limited site conditions and operating cost.In this paper,we use an artificial neural network (ANN) to link cone penetration test (CPT) resistance with the P-wave velocity of near-surface layers to predict large-scale velocity distribution based on a small number of paired logging-CPT data.This method includes the following steps:①using lithology as the separation condition,depth and cone resistances as inputs,and velocity as the target output;②updating hidden layer neurons through a feedforward mechanism;③obtaining near-surface velocity profiles by inputting CPT data into the trained ANN model.A case study in northern Jiangsu proves that the precision of lithologic division and the size of the training sample set determine our model performance.The ANN method is superior to empirical formula methods in reliability,resolution,and robustness,and the accuracy of shallow P-velocity prediction is over 90%.Using this method,it is easier to locate the ghosting interface and weathered layer close to the surface and perform near-surface velocity investigation more accurately and efficiently.

Noise suppression is an important research topic in seismology and seismic signal processing. Accurately suppressing seismic noises and extracting effective signals is a key step in seismological research and seismic monitoring. Traditional denoising methods have some shortcomings, such as insufficient flexibility, difficulty in dealing with complex noises, information loss, and dependence on manual feature extraction. In order to overcome these shortcomings, this paper probes in a method of time-frequency domain transform combined with deep learning and its application to noise reduction. Five neural network models (including FCN, Unet, CBDNet, SwinUnet and TransUnet) are constructed for noise suppression after the time-frequency transformation of seismic data. This paper introduces three indicators: peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and root mean square error (RMSE) for quantitative evaluation of denoising performance. Numerical experiments show that the convolutional neural network(CNN) method based on time-frequency transform can effectively suppress common noise types (including random noises, swell noises and surface waves) and improve the signal-to-noise ratio of seismic data. The introduction of the Transformer module can further reduce above-mentioned noises and enhance the denoising performance of the CNN model. Further research will focus on an improved network structure for more complex seismic signals and the combination with other advanced techniques to improve denoising performance.

The concept of seismic resolution has very important guiding significance in seismic data acquisition, seismic imaging and seismic geological interpretation.The description method of seismic resolution using dominant frequency, dominant frequency band and even octave of seismic wavelet is insufficient for seismic exploration targeting lithologic reservoirs at present and in the future; so it is necessary to give new connotation to seismic resolution.According to the theory of seismic inversion imaging, the resolution of imaging results is determined by the Hessian operator, which is also called point spread function (PSF), or imaging wavelet, in imaging analysis.The factors that determine the Hessian operator (or imaging wavelet) include source wavelet and wavelet at receiver point, acquisition aperture (wide azimuth and long offset), migration velocity field and migration operator.For the imaging wavelet itself, its complete amplitude spectrum determines its resolution.This article starts with the definition of imaging resolution and discusses the connotation and influencing factors of seismic resolution.Seismic resolving power comes down to the resolving power of the imaging wavelet.Further analysis suggests that imaging wavelets with broadband amplitude spectra dominated by low to medium frequencies exhibit stronger resolving power.Based on the physical interpretation of depth-domain fidelity and high-resolution imaging wavelet, as well as the requirements for true resolution at a target layer, we propose the concept of expected imaging wavelet, based on which fidelity resolution is defined to be the main-lobe amplitude of a reflection wavelet from an adjacent formation.Fidelity resolution is a necessary condition for high-precision broadband elastic parameter (broadband wave impedance) reconstruction.The above new concepts related to imaging resolution have a clearer guiding significance for the acquisition, imaging and geological interpretation of seismic data in the new era represented by seismic data acquisition with wide azimuth, wide frequency band, high density and high signal-to-noise ratio and FWI inversion.

A multimodal neural network-based microseismic event detection method is proposed to address the problem that the time series of effective microseismic signals has severe limitations. First, the multichannel time-domain mode with the target channel as the axis symmetry is established using gather data correlation, and the S-domain modal characteristics are obtained by using time-frequency analysis for the target channel. Then, the neural network for microseismic event detection is designed by combining the time-domain mode and S-domain mode. Multimodal features are synthesized for training and learning to improve the accuracy of detection. Finally, method validation is performed through the analyses of synthetic low-SNR and small-amplitude data and actual oil-well microseismic events. The results showed that our method could detect low-SNR and weak microseismic signals effectively. Compared with SVM, CNN, and supervised machine learning, our method has improved anti-noise performance and accuracy.

Due to the lack of long-offset and usable low-frequency signals in most conventional seismic data,classical full waveform inversion is unable to retrieve long-wavelength components of models in middle and deep zones.Based on the idea of multiscale inversion,a wave-equation reflection inversion method based on model decomposition is used to reconstruct a wide-spectrum velocity model.Meanwhile,an adaptive structure-constrained model regularization method is proposed to optimize inversion results and improve robustness.An experiment using the Sigsbee model illustrated how this approach can effectively reconstruct the macro-velocity model (long wavelength) and velocity disturbance or reflectance structure (short wavelength).In a case study of streamer data acquired in the East China Sea,we employed a wave-equation reflection inversion strategy that combines traveltime and waveform to construct a macroscopic background model more consistent with structures and obtained high-resolution imaging sections simultaneously.Imaging gathers were less noisy and stacked sections were more continuous compared with legacy data,and an improved image of deep basement was yielded.

Random noises contaminate seismic signals and reduce the signal-to-noise ratio of seismic data, which will affect subsequent seismic data processing. A denoising method based on supervised deep learning usually requires a large number of labels to train the network, but it is very challenging to make noise-free labels using observed seismic data. To attenuate random noises and extract useful signals from multi-dimensional seismic data, we propose an end-to-end neural network based on unsupervised deep learning, which consists of a fully connected module, an encoder module and a decoder module. A skip connection similar to a residual structure is added between the encoder and decoder to improve the performance of denoising. To strengthen the network further, a data enhancement method is used to segment large-scale multi-dimensional noisy seismic data into a large number of small-scale one-dimensional data for iteration. Appropriate slicing and sliding sizes for data enhancement could improve the calculation efficiency and denoising effect of the network. The application to synthetic data and actual data acquired in Bohai oilfield shows that the proposed method is better than traditional denoising techniques in random noise attenuation and signal extraction.

Karst fractured-vuggy units are the main type of carbonate reservoirs and have strong heterogeneity;thus,it is difficult to accomplish quantitative prediction.Conventional seismic inversion methods are suitable for quantitative prediction of clastic reservoirs with weak heterogeneity.However,quantitative karst reservoir prediction with high precision is a much tougher problem.The idea of facies-controlled inversion is a plausible solution.By using discontinuity attributes,such as amplitude curvature,to identify carbonate reservoirs qualitatively,a low-frequency model is established to constrain the inversion process.The inversion results can further improve the resolution of the fracture-cavern reservoirs.On this basis,a deterministic facies-controlled inversion method based on gradient structure tensor is proposed.The method includes three steps.Firstly,based on the gradient structure tensor,reservoir facies and non-reservoir facies are divided to reflect the contour of the carbonate fractured-vuggy units.Secondly,a low-frequency model is established with seismic facies as the constraints.Finally,the low-frequency model is applied to the inversion process to obtain seismic attributes sensitive to reservoir properties and realize quantitative prediction of heterogeneous carbonate reservoirs.The results of model tests and a field application to Area M in the Tarim Basin show that the method is feasible for reservoir prediction with strong heterogeneity,as it pinpoints different types of carbonate reservoirs and predicts inter-cavity connectivity.Seismic prediction agrees with well drilling results and production performance.It lays the foundation for systematic quantitative study of carbonate reservoirs.

The accuracy of traditional reconstruction methods is often insufficient to solve the problem of data missing or distortion on acoustic log curves.Deep learning has a strong ability of data characterization,but model building suffers from hyperparameter uncertainties and time cost.To solve these problems,the asynchronous successive halving algorithm (ASHA) is combined with the long short-term memory neural network (LSTM) to formulate hyperparameter optimized LSTM for data reconstruction.A case study in Daqing Oilfield involves 6 wells.Through a correlation analysis,natural gamma ray,density and compensated neutron are selected as input characteristic parameters to build the LSTM learning model,for which hyperparameter optimization is performed using the ASHA.In view of efficiency and accuracy,we compare ASHA optimization with Bayesian optimization and particle swarm optimization.The portfolio of optimized hyperparameters is finally applied to the LSTM model.Compared with multiple regression,GRU and BILSTM models,the ASHA can determine model hyperparameters with improved efficiency and accuracy and less time and labor costs.The ASHA optimized LSTM model could reconstruct acoustic log curves with high accuracy.

In the exploration and development of oil and gas, permeability is an important parameter for reservoir evaluation, development, and production as it reflects fluid flow capacity in reservoirs.The critical path analysis (CPA) method combines the permeability and conductivity of the reservoir to predict permeability based on electrical exploration results.However, the accuracy of scalar-based CPA is insufficient to characterize unconventional oil and gas reservoirs with strong anisotropy (such as fractured shales).A tensor CPA method based on equivalent electrical parameters and permeability tensor is proposed for anisotropic media.The pore network of the core sample is extracted from a 3D real digital core, and the connectivity matrix is used to represent the connectivity and fluid flow in the pore network.The critical pore radius tensor is obtained by using matrix operation to predict permeability.According to the application results for isotropic and anisotropic media, the tensor CPA method can characterize the overall structure of anisotropic media by introducing physical parameters in the form of tensor and thus significantly improve permeability prediction for anisotropic media.

Noises in land seismic data, which can be classified into linear and nonlinear coherent noises, incoherent noises, and random noises, may come from near-surface formations, externally sourced wavefield, and other sources. The basic idea of noise suppression is to establish a model to predict signals or coherent noises and then remove coherent, random, and incoherent noises from seismic data. Some high-precision imaging methods, such as full waveform inversion and least-squares reverse time migration, should be accomplished using seismic data with high signal to noise ratios. This paper presents an overview of denoising theories and techniques, and develops a conceptual model of seismic data to show that signals or coherent noises with linear and/or nonlinear structures float in random noises that satisfy a certain probability distribution. Based on the conceptual model, various denoising methods are discussed, including AR model predictor, linear Radon transform, K-L transform, and Hankel matrix for the prediction of linear signals or coherent noises and Radon transform and polynomial fitting for the prediction of nonlinear (hyperbolic) signals or coherent noises. The fundamental point of denoising is optimal signal modeling, which is the basic idea for most denoising methods in commercial processing systems. The comparative analysis in this paper provides further insights on denoising theories to improve the effect of data processing.

High-resolution processing is a crucial step in seismic data processing. Deconvolution is one of the most commonly used methods for high-resolution processing, but it overlooks the dynamic range of seismic data; thereby it is difficult to balance resolution and signal-to-noise ratio (SNR). This paper proposes a wavelet replacement high-resolution processing method based on seismic data dynamic range and quadratic spectrum. We use the quadratic spectrum of seismic data for high-precision source wavelet inversion, and then substitute the amplitude spectrum of the inverted wavelet with that of the expected source wavelet within the effective frequency band. Synthetic and actual data processing demonstrate enhanced resolution and SNR by using the proposed method.

Secondary positioning of ocean bottom seismometers is one of the key steps in submarine seismic exploration, and positioning accuracy has a direct impact on seismic imaging.Focusing on the requirements of submarine seismic exploration, the key algorithms for acoustic positioning, ultra-short baseline (USBL) positioning and first-break positioning were designed in detail, followed by the development of the software system, including overall architecture, functional modules, and programming.The positioning accuracy of the software was verified and analyzed based on field data.Experimental results show 1m differences in the N and E directions in acoustics, USBL and first-break positioning results between the self-developed software and Gator, which is a dominant business software system.This means that the accuracy of positioning is sufficient for submarine seismic imaging.In addition, three types of positioning results show good consistency, further verifying the reliability of the software.

To predict ultra-deep carbonate reservoirs accurately, an important way is geologic modeling followed by seismic forward modeling.However, conventional modeling methods are usually too simplified to consider the control of deep-seated strike-slip faults on fractured-vuggy units and the influence of near-surface and overlying formations on reservoirs.Therefore, it is hard to establish satisfactory responses consistent with complicated subsurface seismic signatures.To cope with complex seismic-geologic conditions, we discuss a novel integrated modeling approach that includes complex near-surface modeling using multi-source information, modeling of complex igneous rocks, random fractured-vuggy reservoir modeling with geologic constraints, and model fusion with geologic constraints.We use this approach to construct a comprehensive seismic-geologic model for ultra-deep marine carbonate reservoirs, which is more geologically effective and can be used to obtain complex seismic responses for a better understanding of complex wave propagation in fractured-vuggy reservoirs and consequently improved seismic acquisition, processing and interpretation.

Source location is a key technique in microseismic research,and microseismic sources can be located by reverse time propagation of microseismic signals.In the process of reverse time location in elastic media,coupled P-waves and S-waves in microseismic signals will produce artifacts in source focusing results,which have a negative impact on picking source locations.We propose an elastic-wave reverse-time microseismic location algorithm based on the Tene-cor focusing imaging operator.Elastic wave equations with decoupled P- and S-waves are used to obtain the reverse-time wave fields of P- and S-waves,which are then imaged through cross correlation using the Tene-cor imaging operator to obtain source locations.The algorithm is tested using synthetic data and field data.Numerical examples show that the location algorithm proposed in this paper can focus the radial artifacts generated by interference imaging in addition to suppressing the crosstalk noises caused by wavefield coupling in the imaging results.Compared with traditional algorithms,our method can effectively improve the resolution of source location in the context of different receiver conditions,missing frequency bands,and inaccurate velocity models.

It is challenging to identify strike-slip faults in a Craton basin owing to their small displacement, small deformation, and complicated tectonic features, particularly in the context of low seismic resolution and false images in deep zones.According to typical seismic signatures of intracraton strike-slip faults in the Tarim and Sichuan Basins and geologic factors, we identify misconceptions of seismic interpretation and extract three pitfalls.The first is that precipitous cliffs at basement, step-like fractures, and faulted horsts may be misinterpreted as flower structures; similar folds occurring vertically between the basement and overburden may be treated as strike-slip structures by mistake.The second is false images of underlying rock masses as steeply dipping strike-slip faults on seismic sections caused by the occurrence of eruptive rocks and evaporites with lateral thickness and lithology variations, leading to sudden interval velocity change laterally.The third is strike-slip fault-like responses related to some geologic phenomena, e.g.large vertical ravines, karst landform, and lithofacies change.To avoid these pitfalls, the impacts of evaporite and igneous-rock velocities, landform, and lithofacies should be mitigated in enhanced-resolution seismic data interpretation.Seismic attribute maps are the top selection for strike-slip fault identification to exclude false images of strike-slip faults on seismic sections and establish a correct model for seismic-geologic interpretation of strike-slip faults.

A denoising method using adaptive time window vector decomposition (ATW-VD) is developed based on the differences between effective signal and noise vectors.The instantaneous attributes of seismic records are employed to solve time window coefficients;the scale of time window is adaptively adjusted based on instantaneous attributes,which indicate seismic characteristics,to balance vector decomposition denoising and signal amplitude preservation in different regions.Based on a noisy data set,we compare ATW-VD denoising with the classical denoising method using fixed time window vector decomposition (FTW-VD) and verify the correctness and effectiveness of the new method.The application to field data processing shows that our method can accurately extract the vector-direction features of seismic signals and effectively utilize signal-noise vector-angle differences for noise reduction.It is feasible for suppressing spatially directionless noises with short duration,especially random noises,cable waves of 50Hz,and bursts.ATW-VD denoising can significantly improve the signal-to-noise ratio of seismic data while preserving true amplitude and waveform.

Shale brittleness is an important parameter in shale gas resources assessment,and electrical properties of rocks are the basis of electromagnetic exploration.However,the relationship between shale brittleness and electrical properties is ambiguous.To answer this question,we investigate shale samples acquired from the Upper Permian Wujiaping Formation in the eastern Sichuan Basin,based on experimental results of scanning electron microscopy,TOC testing,X-ray diffraction-based bulk-rock analysis,porosity testing,and complex resistivity testing.In addition to the controls on conductivity and polarization,we also discuss internal factors affecting shale electrical discrepancies with different degrees of brittleness.The results indicate that low brittle shales have high clay content and low porosity,while high brittle shales exhibit high quartz content and high porosity.Quartzs,which are mainly biogenic quartzs,make a significant contribution to shale brittleness.Resistivity is influenced by pyrite content and organic matter-hosted porosity,while polarizability is mainly related to pyrite content.Strong compaction leads to a porosity reduction in low brittle shales and consequent hindered ion movement in pores.Pyrite content is low.Thus,shales exhibit high resistivity and low polarizability.For high brittle shales,high TOC content facilitates the development of organic matter-hosted pores,which could be well preserved in the biogenic quartz frame for conducting ion movement.Pyrite content is high,and shales exhibit low resistivity and high polarizability.The regression models based on electrical parameters could be used to evaluate shale brittleness.The research provides an electrical method for shale brittleness evaluation.

Seismic data acquired in a sea area with deep rugged seabed or complex structures suffer from serious diffracted multiples, which cannot be effectively attenuated using traditional techniques that depend on the similarity between modeled multiples and observed multiples. When there are great difference between the model and the real data, the attenuation effect is poor. This paper proposes a deep learning method using a target detection network to detect the regions with diffracted multiples. The method of dictionary learning is then used to transform multiples attenuation into the dictionary learning and reconstruction of multiples, so as to alleviate model-observation dissimilarity. The application of forward modeling data and actual data shows that this method can effectively locate residual diffracted multiples and thus greatly improving the efficiency and accuracy of attenuation, which solves the problem of inaccurate prediction and poor attenuation of diffracted multiples and greatly improves the quality of deep-water seismic data.

The problem of fluid factor inversion lies in the forward modeling using the reflectivities of inelastic media instead of elastic media for an accurate description of propagation. For seismic wave attenuation in inelastic media, a Q-based elastic fluid factor is obtained by disturbing the elastic fluid factor term with attenuation coefficient. The scattering equation of the Q elastic fluid factor at the interface is derived under the assumptions of similar media and weak attenuation. The expression highly correlates with the P-velocity, S-velocity, and density terms in the context of complex number. The relationships between Q elastic fluid factor and P- and S-velocities are derived to obtain the equation of P-reflectivity of Q elastic fluid factor under above assumptions, and the relation between this equation and the reflectivity equation of perfectly elastic media is analyzed. Our method eliminates false bright spots on inversion sections, and thus it is possible to obtain accurate fluid responses under the assumption of attenuation elastic media. Field data application to Shengli oilfield shows more distinguishable fluid responses from surrounding rock responses compared with the routine method of fluid factor inversion. Our method mitigates the uncertainties of fluid detection to some extent and eliminates the artifacts generated by single-factor detection merely using amplitude.

The quality factor Q characterizes the absorption and attenuation of seismic waves as they spread through underground media.It is an important indicator of hydrocarbon accumulation.Estimating Q based on time-frequency analysis has emerged as one of the most common and effective methods in seismic data analysis.The short-time Fourier transform (STFT) is a popular time-frequency localization technique with a predetermined size and geometry of the time window.However,the fixed window function limits its adaptability.In order to accurately capture both low and high-frequency components of the signal,it is necessary to adjust the window width based on the signal's characteristics.A wider time window is needed to represent low-frequency components,whereas a narrower time window is required for high-frequency components.We propose an adaptive window length method for the short-time Fourier transform to calculate instantaneous frequency and determine the Q value using the peak-frequency shift method.The core idea is to employ a large window for the short-time Fourier transform to extract the instantaneous center frequency as the initial frequency,which is then used to dynamically select the optimal window length for subsequent calculation.By adapting the window length based on the initial frequency,a better balance between frequency resolution and time resolution is achieved.Both synthetic data tests and field data application demonstrate that the short-time Fourier transform with adaptive window length outperforms the traditional fixed-window short-time Fourier transform in Q estimation with higher vertical resolution and lateral consistency.Our approach enhances the accuracy and reliability of Q-value estimation,which may lead to comprehensive understanding of underground media and potential oil and gas resources.

As an effective method to address the cycle skip problem in traditional full waveform inversion (FWI), 2-Wasserstein distance-based FWI (OT-FWI) typically requires appropriate data regularization. Therefore, the Sigmoid function is introduced for data regularization in OT-FWI to form the Sigmoid-based OT-FWI method. The Sigmoid function constrains the data within a specific range by mapping the portions below zero into the values close to zero. Compared to common data regularization methods like affine scaling and exponential normalization, the Sigmoid function can better utilize the information of phases below zero in seismic data to further enhance inversion accuracy. A test using a 1D Ricker wavelet demonstrates that the Sigmoid regularization-based method can effectively enhance the convexity of the objective function and increase low-frequency information in the conjugate sources. Testing with synthetic model data and seismic data acquired in an exploration area in Eastern China shows that this approach, compared to conventional data regularization methods, can further improve the accuracy of model inversion.

P- and S-wave decomposition is essential for imaging multi-component seismic data in elastic media, but conventional decomposition methods suffer from low accuracy and imaging artifacts.A data-driven workflow is proposed to obtain a set of neural networks that are highly accurate and artifact-free for decomposing the P- and S-waves in two-dimensional (2D) isotropic elastic media.The neural networks are fully-convolutional neural networks (FCN) working as a spatial filter to decompose P- and S-waves with a high accuracy.Different from the P-S decomposition algorithms using the Fourier transform, the spatial filters are more flexible in decomposing P- and S-waves at any time step and at any spatial position, which makes this method suitable for target-oriented imaging.Snapshots of synthetic data show that the network-tuned spatial filters can decompose P- and S-waves with improved accuracy compared with other space-domain P-S decomposition methods.Elastic-wave reverse-time migration using P- and S-waves decomposed by the proposed algorithm shows reduced artifacts where there is a high velocity contrast.

Time-lapse seismology is one of the most important techniques for reservoir monitoring and subsequent development plan adjustment.In an offshore time-lapse seismic survey, we use a legacy streamer dataset to reconstruct the gaps, which were related to the restriction of platform and subsea pipelines, in the ocean bottom node (OBN) seismic data acquired recently.Prestack consistency processing and poststack global matching are employed to obtain the time shifts which reflect gas features effectively.The nonlinear mapping relationship between two datasets after consistency processing, which is generated using a neural network, could then be used to reconstruct missing OBN data.The method proposed in this paper can recover missing seismic data effectively and meanwhile preserve time-lapse seismic characteristics as much as possible.A field application demonstrates a good result.

Establishing accurate velocity models is crucial for seismic imaging and interpretation.Deep learning offers a new way to build precise velocity models,but the samples available for training the velocity-modeling network are severely limited.To address this issue,we propose a method that utilizes random curves to simulate the subsurface velocity model and automatically generates a large number of samples for deep learning training.A set of random numbers is generated and interpolated to form random sequences,which are then transformed into random curves using trigonometric functions to simulate subsurface interfaces and produce a layered velocity model.The velocity model is complicated by incorporating such features as faults,velocity anomalies,and bedding angles.We adopt Deeplabv3+ as the velocity prediction network.The Deeplabv3+ network is optimized by adding convolutional layers to address the issue of blurry boundaries caused by direct output after upsampling.We applied the proposed method to both synthetic and real data,and evaluated its stability under conditions of noises,wavelet variation,and data missing.The results demonstrate that our approach effectively mitigate the impact of wavelet variation,noises,and partial data absence,and show reliable generalization and robustness.

How to locate microseismic events accurately and efficiently is an important problem in microseismic monitoring.However, the traveltime-based linear inversion, a routine method for event locating, suffers from some problems such as shadow area and caustics during ray tracing in complex heterogeneous media and thus fails to obtain accurate ray paths.Additionally, local minima in travel time calculation are also an obstacle to accurate positioning.We propose a method to locate microseismic events based on an improved multi-stencils fast marching (MSFM) algorithm.The approach utilizes a spherical wave approximation algorithm to compute the travel times at grid points near the seismic source, followed by solving the Eikonal equation using MSFM to calculate travel times at the remaining grid points.Finally, a global search of event positions is conducted using the travel time difference as the objective function.The model test results show that the improved method proposed in this paper outperforms the Fast Moving Method (FMM) and conventional MSFM in travel time calculation, especially in the vicinity of the source.This method can also achieve accurate ray tracing and positioning in layered and complex velocity models.The application to Weiyuan shale gas field in the Sichuan Basin shows more accurate results of event positioning and fracture delineation compared to the Geiger method.

Total organic carbon (TOC) content is the most important parameter in determining the quality of source rocks. In a case study of a transitional shale gas reservoir in the Longtan Formation at Well C1 in area Q, the Sichuan Basin, our efforts focus on how to improve TOC calculation for coal-measure shale gas reservoirs. Based on the geological characteristics of the study area and natural gamma ray spectral log data, the Δlog R method is modified to formulate a dual Δlog R method based on natural gamma ray spectroscopy logging. The reservoir is lithologically divided into two types: coal seam and non-coal seam, according to core TOC data. Th/K and Th/U correlations with TOC contents in coal seams and non-coal seams are analyzed to select the data with the highest correlations for TOC calculation using Δlog R at coal seams and non-coal seams. The results show improved correlations up to 0.78 and 0.85, decreased absolute errors to 0.01% and 0.01%, and decreased relative errors to 14.93% and 12.53% for TOC calculation at coal seams and non-coal seams, Well C1, respectively compared with the traditional method. The dual Δlog R method based on natural gamma ray spectroscopy logging is also successfully applied to the Y Formation at Well C2 in area S, the Sichuan Basin. The improved method is feasible in the context of high clay content, strong heterogeneity, and black coal seams in the study area; the problem of the low correlation between TOC content and U content could also be solved. This method could be applied to similar prospects and offer technical support to shale gas exploration and production.

An accurate seismic wavelet is crucial to full waveform inversion, migration, reservoir prediction, and hydrocarbon detection.Due to deep energy attenuation and geological complexities, seismic wavelets are time-variant and space-variant.Traditional time-varying wavelet extraction methods are single-trace approaches disregarding trace-to-trace wavelet variation.Moreover, traditional spatiotemporal wavelet extraction methods heavily rely on such prior information as log data, which limits their practical application.To address aforementioned issues, an improved method called Spatiotemporal Graph Convolutional Neural Network (STGCN) is proposed for extracting spatiotemporal wavelets.Firstly, based on the distribution characteristics and non-stationary nature of seismic data in the target area, synthetic training data is generated with non-stationary seismic data as inputs and spatiotemporal wavelets as labels.Subsequently, the traditional empirical mode decomposition (EMD) method is used to extract wavelets on a trace-by-trace basis in the target area, and actual training data is constructed with seismic data as inputs and spatiotemporal wavelets as labels in the target area.The improved STGCN is trained using these two types of training data to extract the spatiotemporal features of wavelets in the target area effectively.The processing results of both synthetic data and actual data demonstrate the effectiveness and high accuracy of the proposed method in extracting deep spatiotemporal wavelets.Compared to traditional methods, the proposed method exhibits superior performance and significant practical value.

Viscoelastic and anisotropic characteristics of subsurface media cause phase dispersion and amplitude dissipation of seismic waves.If these undesired effects are ignored during seismic processing, distorted events and migration artifacts may appear in imaging results.The traditional pseudo-acoustic qP-wave equation for viscoacoustic TTI media can be used to simulate seismic-wave propagation in viscoacoustic anisotropic media.However, this equation produces shear wave artifacts, and its application is limited by model parameters (ε>δ).To address this issue, a pure qP-wave equation for viscoacoustic TTI media is derived by combining the pure qP-wave dispersion relation based on acoustic approximation with the constant-Q attenuation model.The newly derived wave equation contains decoupled phase dispersion and amplitude loss terms, and it is conducive to the implementation of attenuation-compensated reverse time migration.Based on the newly derived wave equation, the finite-difference low-rank decomposition strategy is proposed to realize pure qP-wave forward modeling for viscoacoustic TTI media.The numerical simulation results show that the newly derived wave equation overcomes the limitation of pseudo-acoustic qP-wave equation for viscoacoustic TTI media and can simulate seismic-wave propagation in viscoacoustic anisotropic media accurately and stably.In addition, the finite-difference low-rank decomposition strategy developed in this paper inherits the high efficiency of finite-difference solutions and has higher computational efficiency than traditional low-rank decomposition methods.