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.

The loess tableland of the Ordos Basin is a world-famous challenging area for seismic exploration.In this paper, we reviewed the history of seismic exploration in the loess tableland, key techniques of seismic data acquisition, processing and interpretation, application results of 3D seismic techniques in the loess tableland and major achievements of hydrocarbon exploration by Changqing Oilfield Company, and technical requirements for seismic exploration targeting different reservoir types in different prospect areas of the Ordos Basin.After 50 years of hydrocarbon exploration and development by several generations of people, 3D seismic techniques featuring wide azimuth, wide band, high density, and individual-geophone receiving have become strong support to major discoveries, great reserves increase, and high-efficiency production in Changqing Oilfield.Aiming at oil and gas demand, 3D seismic techniques should evolve toward the direction of high precision, high efficiency, low cost, and intelligence based on sustained efforts.Innovative techniques should be combined with proved techniques to effectively support stable production of 6600×10⁴t by Changqing Oilfield Company.

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.

It is important to improve seismic resolution for thin reservoir characterization.Conventional methods, e.g.deconvolution and inverse Q filtering, are not effective enough owing to the problems of rigorous assumptions, low signal-to-noise ratio, and low robustness.A new method based on matching pursuit algorithm was proposed, which can improve the seismic resolution while maintaining high signal-to-noise ratio.Firstly, according to the characteristics of seismic signals, a wavelet dictionary was constructed using a frequency-weighted-exponential (FWE) function, which performed well in fitting spectra of different shapes.Then, seismic data were decomposed as the linear superposition of the wavelets in the wavelet dictionary using the matching pursuit algorithm, followed by amplitude enhancement for those matched wavelets with high frequencies and high signal-to-noise ratios.Finally, the matched wavelets were summed up to obtain high-resolution seismic data.The application test showed that processed data with high resolution and high signal-to-noise ratio agreed with synthetic seismogram calculated using log data.Reliable results will facilitate subsequent thin reservoir characterization.

Five dimensional(5D) seismic interpretation plays an important role in fracture prediction and fluid identification.Gather quality is the basis of 5D interpretation.To address the issues of anisotropic moveout, low dominant frequency, and low signal-to-noise ratio, a portfolio of techniques, including gather denoising, resolution enhancement, residual moveout correction, azimuth gather conditioning, and high-precision well-tie calibration, are established for 5D high-resolution gather conditioning to improve the resolution, signal-to-noise ratio, and fidelity of 5D gathers.As per the case study of OVT data processing in Niuzhuang area, the Bohai Bay Basin in eastern China, stacked sections show seismic reflections with improved continuity and resolution and clear stratigraphic contacts.Dominant frequency increased from 25 to 43 Hz; effective bandwidth expanded from 8~51 to 6~74 Hz; correlation coefficient of well-tie calibration increased from 0.75 to 0.90.Sands of 43.8 km² were predicted for the deployment of 3 drilling sites and reserves promotion.The workflow and techniques of 5D high-resolution gather conditioning add value to 5D seismic data for subsequent prestack inversion, fracture detection, and reservoir characterization.

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 focus of seismic exploration has gradually shifted from structural reservoirs to concealed reservoirs, which raises high requirements for high-resolution processing technology.Conventional high-resolution processing techniques, such as deconvolution and inverse Q filtering, are usually based on one-dimensional assumptions and are effective for horizontal structures or small dip-angle structures.However, these techniques are invalid theoretically for complex structures such as high-steep structures or faults.As we know, the migration image is the convolution of point spread function (PSF) and true reflection coefficient.The high-resolution processing technology in high-dimensional space is actually an inverse problem of this forward modeling problem, which is to solve the reflectivity based on migration result.In this paper, the high-resolution processing technology of imagery based on PSF is proposed.Firstly, a fast algorithm for solving the PSF in high-frequency approximation is deduced according to the computing theory of PSF.Then, a purely data-driven PSF extraction technique is developed depending on the theory of deconvolution in high-dimensional space together with the imagery.Finally, the high-dimensional space deconvolution algorithm is used to obtain high-resolution imaging result, so that the frequency band is widened.The tests of model and field data show that the algorithm proposed in this paper can effectively expand high frequencies without losing low frequencies and has the advantage of improving the resolution of geological bodies with different distribution azimuths compared with the conventional high-resolution algorithms.

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.

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.

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.

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.

In the process of vibroseis dynamic sweep acquisition, the seismic waves generated by adjacent multiple groups of sources interfere with each other, which affects the quality of seismic data.A reasonable design of acquisition time-distance rule can minimize the interference between efficient sources while ensuring the quality of raw data and the efficiency of vibroseis acquisition.In the past, the design of dynamic sweep time-distance rule did not consider the suppression effect of efficient acquisition noise and lacked a quantitative unified standard.This paper introduces the suppression effect of efficient acquisition noise into the design of dynamic sweep time-distance rule for the first time.Based on the uncorrelated data acquired by different surface sources through field tests, the cross-correlation method is used to obtain the efficient acquisition noise data, and a quantitative unified standard for design of time-distance rule is established with the energy of environmental noise, spatiotemporal position of interference, and energy ratio of the interference area as the thresholds.Thus, a method for quantitative optimization design of vibrosesis dynamic sweep time-distance rule is formed.This method can optimize the field test scheme and reduce the testing workload.It is compatible with the signal-noise separation technology in seismic data processing to optimize the slip time parameter.This quantitative and qualitative combined method can avoid the artificial subjective ambiguity brought by the conventional qualitative analysis method based on frequency scanning.Moreover, this method is not constrained by the surface characteristics of the exploration area and can maximize the efficiency of acquisition while ensuring the quality of seismic data.The proposed method for quantitative optimization design of vibrosesis dynamic sweep time-distance rule is an important supplement to the supporting technology of efficient vibroseis acquisition, and facilitates the application of "wide azimuth, broadband, and high-density" seismic acquisition technology.

Rock porosity is one of the critical parameters to characterize the reservoir.High-precision prediction of porosity is conducive to more detailed description of the location of highly porous and permeable reservoirs.Due to the strong heterogeneity and complex pore structure of the reservoir, there are many factors affecting the reservoir porosity, which brings difficulties to the accurate prediction of reservoir porosity.In recent years, the development of deep learning has provided a new idea for high-precision seismic porosity prediction.Accordingly, this paper presents a prestack seismic porosity prediction method based on the bidirectional gated recurrent unit neural network and attention mechanism (BiGRU-Attention).In this method, BiGRU is used to realize the bidirectional propagation of information and the attention mechanism is added to amplify the key information.The P-wave velocity and density information obtained from the prestack simultaneous inversion is used as the input and the logging porosity value is used as the label to train and test the BiGRU-Attention network, so as to obtain an optimal model.This method establishes a complex mapping relationship between seismic elastic parameters (P-wave velocity and density) and porosity to achieve high-precision prediction of porosity.The actual data test results showed that compared with the conventional multiple linear regression (MLR), dense neural network (DNN) and gated recurrent unit neural network (GRU), the proposed method based on BiGRU-Attention had higher prediction accuracy in blind well testing.The root mean square error (RMSE) of prediction results and logging data was less than 0.002 2, and the average absolute error (MAE) was less than 0.001 4.Application of this method to the seismic data of an actual 3D work area demonstrated that the predicted porosity profile matched well with the logging porosity value, indicating that the method has good practical value.

Borehole transient response should be investigated with consideration to the propagation and diffusion of transient physical fields inside and outside the borehole and the comprehensive responses of probes.A transmitter can actually excite three transient physical fields (acoustic, electric and magnetic) at the same time, and multiple probes can work simultaneously to obtain multiple physical parameters of formation, allowing for the integration of logging techniques.The piezoelectric tube and plate excite the transient vibration and electromagnetic field with the same spectrum.The coil with magnetic core produces the transient electromagnetic field when its current is turned on and off.At the same time, the magnetostrictive deformation of the magnetic core also excites the vibration, which propagates in the borehole according to the law of acoustics and diffuses according to the electromagnetic law.Resistivity and acoustic logging can be achieved by simultaneous measurement of transient electromagnetic and acoustic fields in borehole.The natural frequency and resonance response formed by continuous reflection of radial acoustic wave from cylindrical sidewall can be used to transmit at a high power, and the energy can effectively pass through the sidewall into the formation, so that the formation far away from the borehole can be detected.When the excitation power is further increased, the nuclear magnetic resonance of pore liquid in sandstone is excited by the transient strong magnetic field, and the seismoelectric effect is excited by the strong vibration.The nuclear magnetic and seismoelectric measurement can be realized by using highly sensitive probes in the borehole.Neutron and density logging can be achieved as the high-flux neutrons are produced by steady-state high-power ultrasonic excitation, and acoustic and resistivity logging can be obtained using transient responses produced as it turns on and off, which is the ultimate goal of principle integration.The integrated tool can be used in open hole and cased hole.

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.

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.

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.

To reduce the cost of seismic acquisition, improve the acquisition efficiency and maintain the regularity and completeness of seismic data, the regular projection of geometry is proposed to fill missing shots and receivers for random seismic data acquisition.Under the framework of compressive sensing, dictionary learning and sparse representation are used to reconstruct seismic data.Compared with traditional dictionary learning method, the proposed method replaces orthogonal matching pursuit (OMP) with batch-OMP to avoid heavy computation in direct inversion of matrix, and also uses alternating least squares (ALS) to take place of singular value decomposition (SVD) to improve computation efficiency.Moreover, to avoid fitting noise, a damped constrain is applied to sparse coefficients for obtaining better dictionary atoms.Frequency domain dictionary and seismic data reconstruction are proposed to tackle the issues of low computational efficiency and poor capability of protecting weak signal in the time domain using traditional dictionary learning methods.Only seismic data in principal frequency band is used to reconstruct seismic data, which can effectively reduce computation workload, suppress noise and improve signal-to-noise ratio.Thus, the technical process of regular projection of geometry and seismic data reconstruction for random acquisition of seismic data is formed.The proposed method is applied to field data, demonstrating that the quality of prestack seismic data is effectively improved through regular projection of geometry and seismic data reconstruction, contributing to better imaging results.

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.

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.

Traditional geostatistical estimation and simulation use static spatial data indiscriminatingly when integrating well and seismic data without considering spatial sedimentary variation across sampling points and the points to be simulated and the influence of lithofacies change on sampling values, particularly when there are strong spatial heterogeneity of reservoirs and uneven distribution of wells.For tight clastic reservoirs, we propose a facies-controlled simulation method using space-variable data based on a Bayesian-sequential Gaussian algorithm.A 3D spherical model is defined to vary dynamically with the position of the point to be simulated, which changes spatial data from a 1D column to the 3D sphere.Based on the direction of sedimentary source, the data in the 3D spherical model are screened using a variogram function; meanwhile, sedimentary facies are used as the constraints to improve the accuracy of sampling values.The weights at the sampling points are determined using inverse distance weighting.Our method was applied to the east slope of the west Sichuan Depression and yields improved-resolution images of continuous channels which are consistent with seismic attributes.The coincidence rate between the simulation results of the blind well and the logging data was 92%, which verifies the accuracy of the method.In addition, the method is demonstrated to be practical because it applies to different hardware configurations.

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.

Traditional manual interpretation of seismic facies relies heavily on the experience of interpreters, resulting in low efficiency and strong subjectivity.In recent years, deep learning techniques, represented by deep neural networks based on unsupervised learning and supervised learning, have played an important role in the intelligent identification of seismic facies.However, the problem of low accuracy of identification has vexed unsupervised learning with no guidance of prior knowledge in practical application, while supervised learning relies on a large amount of labelled information, which is often unavailable in practice.We propose a method of contrastive semi-supervised learning to optimize the model for seismic facies identification, which uses unlabelled and labelled data to learn common characteristics of similar samples and the discrepancies among dissimilar samples so as to minimize intra-class distance for similar facies and maximize inter-class distance for different facies as much as possible.The facies and learned features are correlated using a small number of labels to achieve high-precision identification of seismic facies in the region of interest.The proposed method was successfully applied to the public SEAM AI dataset and a filed dataset from the South China Sea.Compared with conventional supervised methods for seismic facies recognition, the proposed method can identify different types of seismic facies more accurately with a small number of labels.

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.

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.

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.

The complex structural area in the piedmont zone has abundant oil and gas resources, but the low signal-to-noise ratio(SNR) of the data and the difficulty of velocity modelling bring challenges to accurate seismic imaging for the piedmont zone.Based on the data from the Haitangpu area in the piedmont zone of Longmen Mountain, this paper presents an applied research-based work on prestack depth-domain seismic imaging processing, especially with respect to prestack noise suppression and prestack depth migration velocity modeling.This work includes two procedures: ①protection of low-frequency weak signals, and ② optimization of prestack depth migration velocity modeling process.The secondary signal-to-noise separation technique is adopted to perform a secondary signal-to-noise separation on effective signals in noise recording, so as to protect the low-frequency weak signals to the maximum extent.To optimize the prestack depth migration velocity modeling process, the situation that input gather and offset input gather share a common gather in traditional depth-domain velocity field update is optimized by five-dimensional data regularization of input gather driving the velocity field update to improve its SNR.Then, based on geosteering, the interface with strong reflection but missing velocity information is finely characterized, and the depth-domain near-surface velocity model is combined with joint tomographic inversion to establish a depth-domain velocity field with geological model constraints.After an accurate velocity model is obtained, the migration input gather and migration method are modified, that is, the gather before five-dimensional data regularization is taken as the input gather, and the full-azimuth angle-domain prestack depth migration as the final migration imaging technique.Application of this method to the Haitangpu area demonstrated a significantly improved seismic imaging accuracy, a high SNR in the final prestack depth migration imaging results, and a rational migration positioning.This provides a valuable reference for depth-domain seismic imaging processing to further promote the exploration and development in the area.

Wave attenuation across a thin reservoir mainly includes intrinsic attenuation and scattering attenuation.The former is caused by the viscoelasticity of media within seismic frequency band, and the latter manifests as tuning effect in a thin layer.In order to clarify the influence of reservoir thickness on seismic waves, we use the generalized propagation matrix to perform forward modeling of amplitude variation with offset for an isotropic and an anisotropic wedge model, and then extract the dispersion attribute from modelled data using a modified inversion spectral decomposition and frequency-dependent AVO inversion to investigate the impact of reservoir thickness on seismic-wave dispersion.The results indicate that intrinsic attenuation significantly reduces the reflection amplitude from the reservoir, while scattering attenuation causes reflection amplitude to first increase and then decrease with thickness.Seismic-wave dispersion shows a M-shaped curve as reservoir thickness increases, on which two maxima appear at the thicknesses around 1/20 wavelength and 1/2 wavelength, respectively, and the minimum at 1/4 wavelength.When reservoir thickness is less than 1/4 wavelength, scattering attenuation shows a stronger influence on reflection amplitude and dispersion than intrinsic attenuation.

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.

Reservoirs with vertically oriented fractures are equivalent to transversely isotropic media with a horizontal symmetry axis (HTI), and fractures could be characterized using fracture weakness.Under the Bayesian framework, an AVAZ inversion method based on anisotropic gradient is developed to calculate fracture weakness using azimuthal amplitude difference.Dry fracture weakness correlation is derived from the linear sliding model.Based on the relationship between anisotropic gradient and fracture weakness, AVO anisotropic gradient extracted from prestack seismic data is used to establish the initial model of fracture weakness, which functions as prior information constraints for inversion.Prestack AVAZ inversion is implemented to estimate fracture weakness of HTI media based on the Bayesian theory.To improve fracture prediction, azimuthal amplitude difference is used to mitigate the interference of background media.Owing to the accurate initial model and increasing influence of fracture parameters in the inversion, model data and field data tests show good results consistent with log interpretation and geologic knowledge, which demonstrates the robustness and credibility of the method.

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.

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.

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.

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.

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.

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.

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.