ABSTRACTS

Fieldqualityevaluationmethodofmassiveseismicacquisitiondata.XULeiliang1andXUWeixiu1.OilGeophysicalProspecting,2021,56(6):1205-1213.

With the development of high-efficiency acquisition and wide frequency,wide azimath,high density seismic exploration technology,seismic acquisition data shows exponential growth,which brings challenges to the field quality evaluation of such data.The present theoretical field evaluation method of massive seismic data and its adaptability in quality control of massive seismic acquisition data are studied in this paper.It is difficult for single attribute models to fully characterize the quality of seismic data.A multi-attribute discriminant analysis model for single shot records and its production process are designed.Given the severe subjectivity of the multi-attribute discriminant analysis model that completely relies on the standard records and the threshold value,an intelligent quality classification model for massive seismic data is put forward.With feature analysis of massive seismic data,an intelligent evaluation process of single shot records based on the random forest is proposed.Three sample enhancement techniques are used to solve the problem of small and unbalanced samples of single shot records.The random forest classification algorithm of single shot records and its key technologies are studied,including the branch node construction based on continuous seismic attri-butes,the selection of modeling parameters,and the evaluation of the classification results.The application of experimental data shows that the results of the new method are correct and ready to be highly parallelized.Finally,according to the ana-lysis of the correlations of these models as well as their adaptability and timeliness,the combined application of multiple models can meet the requirements of field quality control of massive seismic acquisition data.

Keywords：random forest,intelligent single shot record classification,multi-attribute evaluation mo-del,evaluation method of single shot record,field quality evaluation,massive seismic data

1.Shengli Branch,Geophysical Company of Sino-pec,Dongying,Shandong 257000,China

Near-surfacestructureinversionusingvibrationsignalswhiledrillingshotholesinseismicexploration.LIBiao1,LIZixuan2,PENGWen1,ZHANGJingyao2,andWUXiaohua1.OilGeophysicalProspecting,2021,56(6):1214-1219.

The deep-hole dynamite source is usually adopted for seismic exploration in mountain areas due to the undulating terrain.In the process of drilling shot holes,the connector comprised of the diesel (gas)engine,drilling bit,and support equipment forms a noise source that continuously vibrates on the surface.The near-surface vibration generated radiates to the surroundings with the derrick at its center,and the law of this vibration closely correlates with the near-surface velocity structure.Therefore,this paper proposes a me-thod of utilizing this type of vibration signal while drilling to acquire the near-surface structure through inversion.Specifically,geophones are arranged on a radiation line centering around the connector to continuously record the vibration signals,and then each signal is processed through mean removal,detrending,time-domain normalization,and spectral whitening.Finally,the one closest to the connector of the drilling rig is cross-correlated with the rest of the signals.The time derivative of the correlation result is the empirical Green's function of the seismic wave field generated by the drilling rig vibration.The multichannel analysis is employed to extract the surface wave dispersion curve to obtain the near-surface velocity structure through inversion.The analysis of the pilot field experiment shows that the extracted pseudo surface wave by the proposed method is highly close to the surface wave acquired by the multichannel transient method,and the near-surface velocity structure obtained through inversion is also consistent with the uphole survey measurement.

Keywords:drilling noise,near-surface structure,cross correlation,Green's function,surface wave,dispersion curve

1.Southwest Geophysical Company,BGP Inc.,CNPC,Chengdu,Sichuan 610213,China

2.PetroChina Southwest Oil &Gasfield Company,Chengdu,Sichuan 610041,China

SeismicdatareconstructionbySR-ADMMalgorithmbasedoncompressedsensing.DUANZhongyu1，LITingting1，XIAOYong1,WANGYunlei2，andZHENGGuijuan3.OilGeophysicalProspecting,2021,56(6):1220-1228.

The lack of seismic data due to the field environment and operation cost will affect subsequent processing and interpretation of seismic data.Thus,it is of great significance to reconstruct the missing seismic data.In light of the compressed sensing theory,complete data can be recovered by an optimization algorithm at a frequency lower than the Nyquist sampling frequency on the pre-mise of sparse data.In this paper,a square-regular alternating direction method of multipliers (SR-ADMM)based on compressed sensing is proposed for seismic data reconstruction.The square regularization term is added to the SR-ADMM algorithm in the iterative process of the alternating direction method of multipliers,and this algorithm realizes the adaptive selection of the parameter ba-lance factor.First,sparse representation of missing seismic data is made by dictionary learning,and then the SR-ADMM algorithm is used to solve the optimization problem and reconstruct the missing seismic data.The reconstruction of simulated seismic data and actual data of Daqing Oilfield shows that the SR-ADMM algorithm proposed in this paper has high reconstruction accuracy and certain practicability.

Keywords：compressed sensing，seismic data reconstruction，optimization algorithm，alternating direction method of multipliers，dictionary learning

1.School of Information &Communication Engineering，Beijing Information Science &Technology University，Beijing，100101，China

2.BGP Intl.，CNPC，Zhuozhou，Hebei 072751，China

3.Geophysical Research Institute,BGP,CNPC，Zhuozhou，Hebei 072751，China

Analysisofcomplexnear-surfacemultiplewavesanddiscussiononsuppressionmethods:acaseofseismicdatainthehinterlandoftheJunggarBasin.LINJuan1,JIANGLi1,PANLong1,LENGXuemei1,ZHANGXinji1,andYOUWei1.OilGeophysicalProspecting,2021,56(6):1229-1235.

The surface in the hinterland of the Junggar Basin is mainly desert and ecological forest.From the analysis of the east-west section,it is known that the wave group characteristics and the frequency and phase characteristics corresponding to the two types of surface are greatly different,which results in the obvious “Yin-yang face”phenomenon on the seismic section.Therefore,considering the single shot records on different types of surface,we analyzed the reasons for the “trapping”characteristics in the first-break frequency spectrum.Combined with the demonstration and analysis of the surface structure and forward mo-del,we believe that complex near-surface multiple waves are the main factor causing the “Yin-yang face”phenomenon.Moreover,the strength and specific location of trapping are clarified,and thus this paper proposes a predictive variant-step deconvolution technology based on the difference of tra-vel time and of the time of the first trough in autocorrelation,which has an obvious suppression effect on near-surface multiple waves in this area and improves and eliminates the phenomenon of “Yin-yang face”.This method has been widely used and applied in other blocks in the hinterland of the Junggar Basin,and good results have been achieved.

Keywords：complex near-surface,multiple waves,“trapping”,variant-step deconvolution,Junggar Basin

1.Institute of Geophysics,Research Institute of Exploration and Development,Xinjiang Oilfield Company,PetroChina,Urumqi，Xinjiang 830013,China

Adaptivestrongreflectionseparationmethodbasedonseismicphasedecomposition.ZHANGSheng-qiang1，ZHANGZhijun1，LIYao1，andGUOJun1.OilGeophysicalProspecting,2021,56(6):1236-1243.

The main channel with relatively large scale of Neogene in the Bohai Sea is the key research object of rolling evaluation potential search.However,this type of channel usually extends far and the silty content of mudstone changes rapidly,which leads to the large difference in the wave impedance of the surrounding rock of the target reservoir and strengthens the false seismic response of the reservoir caused by the difference of surrounding rock.Therefore,the seismic response amplitude of the reservoir cannot effectively reflect the real changes of reservoir development,which brings great challenges to the fine prediction and characterization of the reservoir in the study area.We analyzed the characteristics and mechanism of seismic response under such complex geological conditions through forward modeling to establish the relationship of the waveform information of seismic response cha-racteristics with the corresponding phase information and geological information.We innovatively proposed an adaptive strong reflection separation method based on seismic phase decomposition.This method firstly obtains accurate and effective phase spectrum with high resolution by sparse inversion complex spectral decomposition via sparse inversion.Then the inverse transform is applied to all frequency components of the phase spectrum to form phase gather and realizes the stable phase decomposition and reconstruction of seismic data,which constructs a new seismic geological interpretation.It can effectively remove the phase component of strong reflection interference caused by the special surrounding rock and other factors to highlight the target reservoir,solving the seismic response distortion affected by the difference among surrounding rocks.Finally,the application results of theoretically synthetic data and actual data show that the new method proposed in this paper significantly improves the accuracy of reservoir prediction.It provides an important basis for the scientific deployment of wells and the calculation of proven oil reserves in the rolling evaluation of BZ Oilfield in Bohai Bay.

Keywords：seismic phase decomposition,strong reflection,reservoir prediction,high resolution,complex spectral decomposition,seismic response characteristics

1.CNOOC China Limited,Tianjin Branch,Tianjin 300459,China

Constrainedcomplex-domainleast-squaresspectrumblueing.YANGPeijie1.OilGeophysicalProspecting,2021,56(6):1244-1253.

The purpose of frequency extension is to improve the dominant frequency of seismic data,broaden the frequency band,and thereby better describe smaller and thinner geological bodies.A forward mathematical model is built for spectrum extension,and the forward problem of spectrum extension is transformed into an inverse problem through the broadband constrained target spectrum (CTS).The spectrum extension operator (SEO)in the frequency domain is acquired through solving the inverse problem by the complex-domain least-squares method.Spectrum extension of seismic data is achieved by applying SEO to the frequency spectrum of the original seismic data and the inverse Fourier transform.This method can effectively improve the dominant frequency,broaden the frequency band,and improve the resolution of seismic data without changing the phase spectrum of the data.The distance between the frequency spectrum after frequency extension and the CTS can be adjusted by setting different spectrum control factors to obtain spectrum extension results with different resolutions.This method has been applied to the fine identification of thin layers and overlap pinch-outs in the Jiyang Depression，and has a broad application prospect.

Keywords：complex-domain least-squares method,constrained target spectrum,spectrum extension operator,spectrum control factor

1.Research Institute of Exploration and Development,Sinopec Shengli Oilfield Company,Dong-ying,Shandong 257015,China

Thecompletedecoupledwaveequationsforellipsoidalanisotropicmedia.LIANGKai1,CAODanping1,SUNShangrao1,andYINXingyao1.OilGeophysicalProspecting,2021,56(6):1254-1261.

The decoupling of the wave equation refers to the decoupling of an elastic wave equation into wave equations that can describe the independent propagation of various wave patterns,which plays an important role in numerical simulation of seismic waves,seismic migration,and multi-component seismology.In most anisotropic media,the qP wave and qS wave are generally coupled for propagation without exact decoupling nature,but it is found that the ellipsoidal anisotropic (EA)media are an exception.First,on the basis of the exact dispersion relation equation of the elastic wave in homogeneous EA media,three decoupled dispersion equations are decomposed from this equation by the factorization method and then transformed into the completely and exactly decoupled wave equations of the qP wave,qSV wave,and SH wave in homogeneous EA media by inverse Fourier transform.The theoretical formulas and numerical examples indicate that the qP wave,qSV wave,and SH wave can be completely and exactly decoupled and propagate independently in homogeneous EA media by decoupled wave equations.The wavefront of the qP wave and SH wave is ellipsoidal,and the wavefront of the qSV wave is spherical,independent of anisotropic parameters.The three complete decoupled wave equations are suitable not only for weak anisotropic EA media but also for strong anisotropic EA media.

Keywords：ellipsoidal anisotropic media,wave equation,decoupling,dispersion relation,elastic wave

1.School of Geosciences,China University of Petroleum (East China),Qingdao,Shandong 266580,China

Curvilinear-gridfinite-differencenumericalsimulationmethodforgeneralizedfirst-order2.5Dtime-domainwaveequation.YANGShangBei1,BAIChaoYing2,andZHOUBing3.OilGeophysicalPro-specting，2021，56(6)：1262-1278.

The 2.5D seismic wavefield numerical stimulation employs the point source in 2D geological models to calculate 3D seismic wavefields.In this paper,we present a generalized 2.5D first-order time-domain wave equation that can be applied to different media (acoustic isotropic,elastic isotropic,and elastic anisotropic)and various boundary conditions (acoustic free-surface,solid free-surface,and solid-liquid boundary).The wave equation is solved by a curvilinear-grid finite-difference method.A comparison of 2.5D numerical solutions,3D analytic solutions,and 3D numerical solutions in different homogeneous medium models (acoustic isotropic,elastic isotropic,and elastic anisotropic)verifies the correctness of the derived equation and the numerical solution method.It also demonstrates that compared with the 3D nume-rical method,the 2.5D numerical method has great advantages in calculation efficiency and memory footprint.The 2D numerical solutions cannot be applied directly in that they suffer significant amplitude distortion and phase shifts due to an artificial line source applied in this method.The results of numerical experiments show that the proposed 2.5D numerical simulation method can be applied to geological models with different boundary conditions (acoustic free-surface,solid free-surface,and solid-liquid boundary).In addition,unlike the 2D wavefield numerical simulation method,the 2.5D method can be directly employed to process actual point source observation data such as 2.5D reverse-time migration.

Keywords：wavefield simulation,generalized first-order wave equation,2.5D,finite-difference me-thod,curvilinear-grid

1.Xi’an Centre of Geological Survey,China Geological Survey,Xi’an,Shaanxi 710054,China

2.Institute of Geophysics,School of Geological Engineering and Geomatics,Chang’an University,Xi’an,Shaanxi 710054,China

3.Department of Earth Sciences,Khalifa University of Science and Technology,Abu Dhabi 2533,UAE

Correctionmethodforwell-drivenseismicvelocitymodelanditsapplicationinLWDprocessing.SUNJiaqing1,XUXingrong1，KOULongjiang1,WANGJing1,LIUJintao1,andLIHuizhen1.OilGeophysicalProspecting，2021,56(6)：1279-1285.

As an effective method to apply surface seismic data to development seismology,logging-while-drilling (LMD)processing plays an important role in avoiding drilling risk and improving the drilling success rate.The core problem is how to use the measured data while drilling to improve the accuracy of the surface seismic velocity model and further promote the accuracy of seismic data imaging and guide the adjustment of drilling trajectories.Therefore,this paper proposed the correction method for the well-driven seismic velocity model.Specifically,given the traditional pre-stack depth migration velocity model,we presume the velocity and depth information of the drilled formations while drilling as constraints and rapidly correct the existing seismic velocity model combined with the logging and VSP data of drilled wells in the surrounding area.In this way,more accurate seismic underground imaging can be obtained,and the geological description and prediction of the undrilled formations can be realized to improve the drilling success rate.Oilfield examples show that this method shortens the update and iteration time of the velocity model and can meet the accuracy and timeliness requirements of LWD processing.The proposed method plays an important role in improving the accuracy of real-time pre-drilling prediction while drilling.

Keywords：logging-while-drilling,velocity mode-ling,velocity model correction,pre-stack depth migration,well-driven

1.Northwest Branch,Research Institute of Petroleum Exploration &Development,PetroChina,Lanzhou,Gansu 730020,China

MicroseismiccharacteristicsofshalegaswellswithcasingdeformationinChangning,Sichuan.CHENZhaowei1，ZHANGHaozhe2，ZHOUXiaojin3，andCAOHu2.OilGeophysicalProspecting,2021,56(6):1286-1292.

To address the casing deformation of shale gas wells in Sichuan Basin,this paper calculates the casing deformation of the H19 platform with a small bridge plug size and multi-finger image tool(MIT)data and analyzes the characteristics of the spatial and temporal distribution and moment magnitude of microseismic events at casing deformation points.The fracture surface model is established depending on the spatial distribution characteristics of identified natural fractures.The quantitative relationship among casing deformation,natural fracture scale and microseismic moment is analyzed by the model.There were 9 deformation values,with the minimum of 6.10mm and the maximum of 50.43mm,averaged to be 28.03mm.The spatial and temporal distribution characteristics of microseismic events are as follows:①The microseismic events are not symmetric about the wellbore;②most of the microseismic events in different fracturing stages are overlapped spatially,showing linear distribution;③there are many large-moment-magnitude events;④the frequency of large-moment-magnitude events is relatively high in the middle or late fracturing period.According to the calculation with the fracture surface model,the area of the fractures causing casing deformation in shale gas wells in the Changning area is 40000～70000m2,the seismic moment 2.57×109～7.57×1010N·m,and the moment magnitude range is -1.16～0.79.These results have guiding significance for real-time monitoring and early warning of casing deformation according to microseismic events and prevention of casing deformation via the judgment of natural fracture scale.

Keywords：Sichuan Basin，shale gas，casing deformation，hydraulic fracturing，natural fracture，microseismic，casing deformation value

1.CNPC Engineering Technology R &D Company Limited，Beijing 102206，China

2.China University of Petroleum (Beijing)，Beijing 102249，China

3.Shale Gas Research Institute，PetroChina Southwest Oil &Gasfield Company，Chengdu，Sichuan 610051，China

AVFinversionbasedonanalyticalsolutionofviscousacousticequation.YANGWuyang1,LIYuanqiang2,HUANGYan3,LIJingye2,WANGEnli1,andZHOUChunlei1.OilGeophysicalProspecting,2021,56(6):1293-1300.

The P-wave attenuation and dispersion are the main reasons for the attenuation of PP wave seismic records.Therefore,in theory,only the post-stack seismic data of the PP wave is required for AVF inversion and the subsequent acquisition of P-wave dispersion factor that can indicate the fluid area.However,the AVF inversion method based on the traditional single-interface assumption is not satisfactory and is controversial in many aspects.So,an AVF inversion method based on the analytical solution of the zero-offset viscous acoustic equation is proposed.The process is as follows:①The time-frequency spectra of seismic records are calculated by a time-frequency spectrum method.②With seismic records,wavelets are extracted to eliminate the wavelet overprints in seismic data and thereby obtain the time-frequency spectra of the reflection coefficients.③According to the viscous acoustic equation,wave impedance inversion is carried out to obtain more accurate impedance parameters and thus to calculate the Fréchet derivative.④An AVF inversion equation is formulated in view of the derivative matrix.Then,appropriate reference frequency points and frequency points involved in the calculation are selected to acquire the high-precision dispersion attributes through inversion.Numerical simulation and actual data tests show that interface dispersion has little effect on seismic records and the AVF effect of the propagation process is much greater than that caused by interface dispersion.The accuracy and resolution of the proposed method are significantly higher than those of the traditional single-interface AVF inversion.

Keywords：AVF inversion,dispersion,attenuation,analytical solution,viscous acoustic equation

1.Northwest Branch,Research Institute of Petroleum Exploration &Development,PetroChina,Lanzhou,Gansu 730020,China

2.College of Geophysics,China University of Petroleum (Beijing),Beijing 102249,China

3.Research Institute of Exploration and Development,Changqing Oilfield Company,PetroChina,Xi’an,Shaanxi 710018,China

Studyonseismicstochasticinversionmethodbasedoncharacteristicparametersofinhomogeneousmedia.WANGBaoli1,2,LINYing1,ZHANGGuang-zhi1,2,andYINXingyao1,2.OilGeophysicalPro-specting,2021,56(6):1301-1310.

The underground media generally have heterogeneous characteristics,which means the elastic,petrophysical,and fluid parameters that characterize the oil and gas reservoirs are spatially inhomogeneous.The conventional seismic stochastic inversion mainly uses the variogram function obtained from well log data to characterize the spatial structure of underground formations,and it is difficult to effectively describe the spatial variation characteristics of underground complex heterogeneous reservoirs.Therefore,this paper first describes the characteristics of the spatial disturbance caused by different inhomogeneous media characteristic parameters to the media.Then,guided by the Bayesian theory,this paper makes use of the underground formation information contained in the known well log data and seismic data and proposes the stochastic inversion method based on the characteristic parameters of inhomogeneous media.This method integrates the given well log data and seismic data and estimates the characteristic parameters of inhomogeneous media which can better describe the spatial structure characteristics of underground reservoirs based on random medium theory.Next,these parameters are utilized to build a prior information model required for the subsequent inversion process.Finally,a very fast quantum annealing algorithm is adopted to optimize the objective function,and the stochastic inversion results are obtained.The model test shows that the prior model of heterogeneous characteristic parameters can describe the heterogeneous characteristics of the reservoirs and provide reliable geostatistical prior information for subsequent inversion.The case analysis further shows that this method can better achieve high-resolution inversion of complex underground reservoirs and obtain more reliable inversion results.

Keywords：inhomogeneous medium,random medium theory,characteristic parameter,Bayesian theory,seismic stochastic inversion

1.School of Geosciences,China University of Petroleum (East China),Qingdao,Shandong 266580,China

2.Laboratory for Marine Mineral Resources,Qingdao National Laboratory for Marine Science and Technology,Qingdao,Shandong 266071,China

Multi-parameterconstrainedhigh-resolutioninversionmethoddrivenbywaveform:AcasestudyofLongmaxiFormationshalegasinwesternChongqing,SichuanBasin.GUWen1,2,YINXingyao1,WUFurong2,LIKun1,andZHAIHaojie3.OilGeophysicalProspecting，2021,56(1)：1311-1321.

The post-stack seismic waveform indication inversion shows advantages in thin reservoir prediction,and pre-stack elastic parameters have more information and are more sensitive to reservoirs than post-stack elastic parameters.Taking advantage of them,this paper applies the high-precision pre-stack stochastic inversion method under seismic drive and reservoir configuration constraint to quantitatively characterize high-quality thin shale reservoirs with petrophysical data in hope of providing technical support for the prediction of geological sweet spots of deep shale gas.Firstly,the three-variable optimization method based on gather waveform similarity,AVO feature and spatial distance is employed to extract wells with similar structures as spatial estimation samples,and then the initial model of the to-be-discriminated gather is established.Secondly,with the statistical elastic impedance as the prior information,the pre-stack seismic waveform indication inversion is performed with “Markov chain Monte Carlo stochastic simulation algorithm based on the pre-stack gather feature indication.”Finally,a high-precision inversion result is obtained with pre-stack elastic parameters.The practical application demonstrates that this method effectively simulates the thickness of high-quality shale in 1-2 layer in the first sub-member of the first member of Longmaxi Formation,and accurately simulated the geological sweet spot parameter therein on the basis of the high-frequency well-seismic simulation of the characteristic parameters.This research provides technical support for shale gas exploration.

Keywords：shale gas,geological sweet spot,multi-parameter similarity,Longmaxi Formation,pre-stack geostatistical inversion,stochastic simulation

1.School of Geosciences,China University of Petroleum (East China),Qingdao,Shandong 266555,China

2.BGP Inc.,CNPC,Zhuozhou,Hebei 072750,China

3.ZHLH Petroleum Technology Research Institute,Beijing 100102,China

Edgedetectionmethodforlow-ranksparsereconstructionanalysis.LIUJun1,2,SONGWeiqi2,CHENJun’an1,TANMing1,HUJianlin2,andDONGLin2.OilGeophysicalProspecting,2021,56(6):1322-1329.

Many edge detection methods can achieve good application results.However,these methods have their shortcomings and limited edge detection ability,especially the unsatisfying effects of edge detection for noise interference,multi-edge interference,and weak small targets.Therefore,the spatial distribution characteristics of fault,fracture,and cave edges are analyzed.According to the seismic response characteristics of those edges,the low-rank sparse analysis theory is introduced into edge detection to study the low-rank sparse decomposition and reconstruction of edge information,background information,and noise information.For the improvement of the edge detection ability and resolution,the in-depth sparse representation of seismic data is carried out on the basis of the compressed sensing sparse representation.Given the vector sparse representation and matrix sparse representation,a new edge detection method,i.e.,an edge detection method for low-rank sparse reconstruction analysis,is formed through the low-rank sparse analysis theory.The specific steps are as follows:First,the seismic data is decomposed into stationary wavelets.Second,multi-scale wavelet coefficients are optimized.Then,the tensor matrix is established and modeled according to the multi-scale optimized wavelet coefficients.Fourth,the singular values of the tensor matrix are decomposed.Fifth,low-rank optimization of those singular values is conducted.Finally,the multi-scale double sparse and double optimization results are fused and reconstructed.The model analysis and the analysis of practical data application effect show that the proposed method has strong noise resistance and applicability and is capable of effectively depicting the edges of faults,fractures,and caves.

Keywords：multi-scale decomposition,low-rank sparse analysis,vector sparse representation,matrix sparse representation,edge detection

1.SINOPEC Northwest Oil Field Company,Urumqi,Xinjiang 830011,China

2.China University of Petroleum (East China),Qingdao,Shandong 266580,China

Researchonhigh-andlow-frequencyexpansionofseismicamplitudepreservingandmulti-scaleBaye-sianfusioninversion.LIAOYi1,LIUWei1,HULin1,ZHANGGuodong1,andZHANGKunkun1.OilGeophysicalProspecting，2021,56(6)：1330-1339.

Both thin and very thick reservoirs in the complex water channels are difficult to distinguish and describe efficiently in Gasfield L.To solve this problem,the frequency-division sections of forward signals with and without attenuation and actual seismic data are used to analyze the multi-scale seismic response features of geological bodies,explore the potential of post-stack seismic signals with weak effectiveness.First,the frequency expansion of relative amplitude preserving was utilized to obtain high-and low-frequency seismic data volumes.Then,the Bayesian iterative inversion was conducted on those data step by step to gain the impedance inversion data that could effectively identify large-,medium-,and small-scale geologi-cal bodies.The results are as follows:Low-frequency and high-frequency effective signals are inherently hidden in seismic data,and the low-,medium-,and high-frequency signals can reflect the overall envelope (large scale),the internal structure (medium scale),and the structure details (small scale)of geological bodies respectively.Moreover,frequency-reduction trace integration can effectively increase the identification degree of very thick reservoirs within the scope of relative amplitude preserving.Given the seismic data volu-me constraint of frequency reduction and in-crease,the multi-scale Bayesian inversion could deliever reliable P-wave impedance inversion results representing multi-scale geological bodies within effective frequency bands.The practical application has proved the validity of this method.

Keywords：multi-scale inversion,very thick reservoir,frequency reduction,trace integration,relative amplitude preserving.

1.Hainan Branch of CNOOC Ltd.,Haikou,Hainan 527100,China

Singlepointbarinterpretationinmeanderingbeltwithextremelearningmachinedrivenmultipleseismicattributesfusion.ZHANGXianguo1,WUXiao-xiao2,HUANGDerong1,andLINChengyan1.OilGeophysicalProspecting,2021,56(6):1340-1350.

The identification of single point bars in high-curvature meandering rivers is of great significance for understanding the evolution and characteristics of meandering rivers and guiding the oilfield deve-lopment.With the meandering river of Guantao Formation in Gudong oilfield as an example,a fusion technology of multiple seismic attributes with the extreme learning machine algorithm is constructed to recognize single point bars in complex meandering belts.The technology integrates the clustering analysis of seismic attributes,seismic attributes fusion by the extreme learning machine algorithm,and seismic forward modeling.Through the drilling and dynamic verification,the following results can be obtained.1)The method can improve the prediction accuracy of sandstone thickness with the coincidence rate of a single well reaching 93.3% which is higher than that of SVM and BP neural network.2)Three combination models for the point bar in the meandering belt are studied,including the migration pattern of point bars in the opposite direction,the migration patterns with and without abandoned channels in the same direction.The three differ in reflection continuity and amplitude.3)There are five single point bars in the study area and the abandoned channels between adjacent point bars form seepage barriers influencing the remaining oil development.The methods and results provide direct geological support for oil development.

Keywords：multiple seismic attributes,extreme learning machine,point bar,meandering belt

1.School of Geosciences,China University of Petroleum (East China),Qingdao,Shandong 266580,China

2.Shanghai Branch of CNOOC Ltd.,Shanghai 200335,China

Seismicdiscontinuityenhancementmethodbasedontensorvoting.CUIXiaoqing1,HUANGXuri1,YANGJian1,ZHANGDong1,CHENXiaochun1,andLIKai1.OilGeophysicalProspecting，2021,56(6):1351-1358.

Fractures,faults and geological body boundaries feature discontinuity in seismic data.Current boundary detection methods often lead to fuzzy boundary characteristics,poor integrity and low coherence due to noise,which increases the difficulty of geological interpretation.To tackle these problems,this paper proposes a seismic discontinuity enhancement method based on tensor voting.Firstly,the seismic geometric attributes are analyzed with eigenvalues and eigenvectors of gradient structure tensor to determine the seed points with geological structure characteristics.Then,the geo-logical structure characteristics are enhanced by the superposition of voting fields of seed points,and the discontinuous structure characteristics hidden in seismic attributes are excavated to improve the integrity of geological structure characteristics.Finally,the continuous and smooth feature skeleton is extracted by tensor decomposition.Model calculation results and field data prove that the tensor voting method can describe the fault feature information and retain the visually low-continuity fault information to the maximum extent,which makes the fault feature more complete,more continuous and clearer.The proposed method has good robustness and applicability,thus capable of being an effective tool for identifying geological body boundaries.

Keywords：discontinuity,tensor voting,fracture identification,skeleton extraction

1.School of Geoscience and Technology,Southwest Petroleum University，Chengdu，Sichuan 610500，China

Reservoirparametercharacterizationmethodbasedonjointprobabilityinversionwithstructuralconstraints.ZHANGJian1,2,3,LIJingye1,2,WANGJianhua4,CHENXiaohong1,2,LIYuanqiang1,2,andZHOUChunlei5.OilGeophysicalProspecting,2021,56(6):1359-1369.

Current methods for the reservoir parameter prediction and the uncertainty evaluation all use multi-step inversion,which makes it difficult to consider the uncertainty in each step.To this end,a reservoir parameter characterization method based on the joint probability inversion with structural constraints is proposed.The mixed Gaussian joint prior distribution associated with reservoir elastic parameters and physical parameters is first obtained based on well logs,followed by the single Gaussian one according to the sensitivity analysis of petrophysical parameters.Then,the geological structural information and well information are integrated into the inversion process through the least-squares well log interpolation with structural constraints.Finally,the analytical expressions of elastic parameters,physical parameters,and facies are defined by the Bayesian posterior distribution.Compared with traditional methods,the proposed method reduces cumulative errors and improves the accuracy of the prediction of reservoir parameters and uncertainty.The introduction of structural information and well information improves the lateral continuity and resolution of the inversion results.The conditional and blind well tests are carried out according to the actual data in Area M to verify the method.Also,we compare and analyze the difference between the inversion results of the new method and the multi-step one without constraints.The results show that under the assumption of linearized and Gaussian distribution,the new method achieves better inversion results with more accurate posterior probabilities.It objectively characterizes the uncertainties and provides a favorable basis for reservoir characterization and evaluation.

Keywords：Bayesian theory,reservoir parameters,mixed Gaussian joint prior distribution,petrophysics,geological constraints,well interpolation

1.National Engineering Laboratory for Offshore Oil Exploration,China University of Petroleum (Beijing),Beijing 102249,China

2.State Key Laboratory of Petroleum Resources and Prospecting,China University of Petroleum (Beijing),Beijing 102249,China

3.Faculty of Geoscience and Environmental Engineering,Southwest Jiaotong University,Chengdu,Sichuan 611756,China

4.CNOOC Research Institute Co.Ltd.,Beijing 100028,China

5.Northwest Branch,Research Institute of Petroleum Exploration &Development,Petrochina,Lanzhou,Gansu 730020,China

KeytechnologyandapplicationofprocessingandinterpretationoftightgasinJurassicShaximiaoFormationinCentralSichuanBasin.ZHAOBangliu1,ZHANGYusheng2,ZENGZhong1,XICheng2,ZHANGXiong3,andZHANGGuangrong2.OilGeophysicalProspecting,2021,56(6):1370-1380.

Sichuan Basin is rich in tight sandstone gas resources,and the recent exploration of channel sandbodies by high-precision 3D data in Jurassic Shaximiao Formation has achieved certain results.Drilling shows that Shaximiao channel sandbody has the characteristics of fast lateral change and strong reservoir heterogeneity.Therefore,improving the boundary recognition and gas-bearing prediction accuracy of channel sandbody is the key to tight gas seismic exploration.By conducting “six division”(divided by type,frequency,time,domain,step,and zone)high-fidelity prestack denoising technology,near-surfaceQcompensation technology,and OVT domain prestack time migration (PreSTM)technology for AVO feature low frequency protection,sets of processing technology with high fidelity and high resolution was deve-loped for tight gas reservoir of Shaximiao Formation in Sichuan Basin.Meanwhile,the “double bright spot”attribute and multi-wave and multi-component seismic prediction technology are innovated and applied to improve the prediction accuracy of gas-bearing sandbody.The application of this technology series in the tight gas prediction in Central Sichuan has achieved remarkable results,such as expanded frequency band of seismic data,more abundant low-frequency information,significantly improved signal-to-noise ratio,and greatly improved accuracy of channel boundary and gas bearing prediction.In addition,the success rate of new wells has been more than 83%.The achievement strongly supports the increase in storage and production of Shaximiao tight gas in Central Sichuan.

Keywords：Sichuan Basin,Shaximiao Formation,tight gas,seismic prediction

1.PetroChina Exploration &Production Company,Beijing 100011,China

2.PetroChina Southwest Oil &Gasfield Company,Chengdu,Sichuan 610041,China

3.Southwest Branch,GRI,BGP,CNPC,Chengdu,Sichuan 610000,China

Calculationmethodofvariationfunctionforpredictingsandstone-typeuraniumorebadybygeostatisticalinversion.WEIDa1,SUNZhangqing2.OilGeophysicalProspecting,2021,56(6):1381-1390.

The reliability of geostatistical inversion is mainly controlled by the selection of key parameters such as the variation function.The traditional method of calculating the transverse range of the variation function has high randomness.To improve the accuracy of geostatistical inversion in predicting sandstone-type uranium ore body,this paper proposed a method of calculating the variation function under the control of logging data and geological significance.In this method,the vertical range is calculated by counting the data points in the well.The transverse range is calculated through statistics and analysis of the ore body scales and ore-bearing properties of ore occurrences in a developed zone.Compared with conventional deterministic inversion,geostatistical inversion,based on random modeling of logging data,not only retains the high transverse resolution of seismic data but also displays higher vertical resolution.It has a good prediction effect in the Qianjiadian (QJD)sandstone-type uranium ore body with a small thickness and rapid transverse changes.Here,the calculation of the variation function is an important factor affecting the inversion result.The prediction accuracy analysis of the QJD uranium ore body and practical application in the study area conclude as follows:① When the vertical range is 4.5 m,the vertical resolution of the inversion result is the highest,and the inversion efficiency is high;② Compared with post-stack sparse pulse inversion,the results of geostatistical inversion based on the proposed calculation method of the variation function are more consistent with the actual ore body distribution;③ The discovery rate of industrial ore in 12 exploration wells deployed according to the inversion results obtained by the proposed method is 75%,and the average exploration success rate is greatly improved.

Keywords：sandstone-type uranium ore body,reservoir prediction,variation function,vertical range,transverse range,geostatistical inversion

1.E &D Research Institute,Liaohe Oilfield of CNPC,Panjin,Liaoning 124010,China

2.College of Geoexploration Science and Techno-logy,Jilin University,Changchun,Jilin 130026,China

Time-frequencyelectromagnetic(TFEM)technology:Dataprocessing.HEZhanxiang1,2，3,DONGWeibin4,ZHAOGuo4,HOUYujian1,SHENYi-bin1,3,andLIUXuejun4.OilGeophysicalProspecting,2021,56(6):1391-1399.

Time-frequency electromagnetic (TFEM)technology has been used in oil and gas exploration for more than 20 years and plays an important role in the field.Systematic research on TFEM data processing is of great significance for the development of this method.Regarding TFEM data processing,this paper makes clear its flow and analyzes its characteristics and advantages.Moreover,the paper gives the concept of the electromagnetic attribute,improves the method to extract main electromagnetic attribute parameters,and determines the oil-water identification factor of the TFEM method.The oil-bearing and water-bearing examples of an actual exploration area are presented,and the constraint processing and interpretation method of reservoir target multiplicity is further clarified.An actual section showing the superposition of oil-gas factors and seismic anomalies can better indicate the favorable oil-bearing targets.This research put forwards the development direction of the TFEM data processing method and has important practical value.

Keywords：time-frequency electromagnetic (TFEM),data processing,electromagnetic properties,hydrocarbon identification factor

1.Shenzhen Key Laboratory of Deep Offshore Oil and Gas Exploration Technology,Southern University of Science and Technology,Shenzhen,Guangdong 518055,China

2.Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou),Guangzhou,Guangdong 511458，China

3.Department of Earth and Space Sciences SUSTech,Shenzhen,Guangdong 518055,China

4.GME &Geochemical Surveys,BGP,CNPC,Zhuozhou,Hebei 072751,China

Dual-populationartificialbeecolonyalgorithmanditsapplicationinjointinversionofmagnetotelluricandgravitydata.ZENGZhiwen1,2,CHENXiao1,2,GUODong3,DENGJuzhi1,2,ZHANGZhiyong1,2,andCHENHui1,2.OilGeophysicalProspecting,2021,56(1)：1400-1408,1447.

Joint inversion based on a non-linear optimization algorithm has the advantages of global optimization,no need to calculate partial derivatives,and convenience for the integration of prior information.As a novel non-linear optimization algorithm,the artificial bee colony (ABC)algorithm has a unique role transformation mechanism and strong performance in solving optimization problems.However,it also has some shortcomings such as low search efficiency and weak local search ability.Considering that a dual-population framework is capable of improving the global optimization performance of an optimization algorithm,we propose an ABC algorithm with a dual-population framework,in which crossover and mutation operations and neighborhood search for the optimal solution are integrated into different populations.Typical test functions are selected to verify the effectiveness of the improved ABC algorithm.Besides,the algorithm is applied into the joint inversion of magnetotelluric (MT)and gravity data.Model tests and real data feedback show that the dual-population ABC algorithm has high global optimization capability and certain practicability.

Keywords：artificial bee colony,joint inversion,dual population,magnetotelluric,gravity

1.State Key Laboratory of Nuclear Resources and Environment,East China University of Technology,Nanchang,Jiangxi 330013,China

2.School of Geophysics and Measurement-control Technology,East China University of Technology,Nanchang Jiangxi 330013,China

3.Geological Exploration Technology Institute of Anhui Province,Hefei,Anhui 230041,China

Gravityinversionmethodbasedonquasi-neuralnetworkfeaturingGaussianradialbasisfunction.XIANGPeng1,TANShaoquan1,CHENXueguo1,andLIUJia2.OilGeophysicalProspecting,2021,56(1)：1409-1418.

An inversion method based on a quasi-neural network featuring Gaussian radial basis function (RBF)is presented in this paper to improve the resolution of gravity inversion.The model space is compressed through the Gaussian RBF,and the dimension of the inversion parameters is reduced without influencing the representation ability of complex models.A quasi-neural network structure is proposed which takes the Gaussian RBF as the activation function and saves the difficulty of establishing a training set in that it does not require training.The proposed method solves the pro-blems of skinning,low vertical resolution,strong multi-solution,and severe dependence on priori constraints caused by the ill-posedness of gravity inversion.In addition,it can extract effective information from gravitational data to enhance the resolution and reliability of the inversion results.Model experiments show that the method,with high accuracy and resolution,can accurately obtain the position,boundary,and density of the model through inversion.A residual density model with a high vertical resolution is obtained by inverting the gravitational data of the Chezhen Depression.Density interfaces and profiles are extracted from the density model for structural interpretation,which in turn reveals the structural pattern of Lower Paleozoic and the development law of buried hills and proves the practical value and application potential of this method.

Keywords：gravity inversion,Gaussian RBF,quasi-neural network,Chezhen Depression

1.Research Institute of Exploration &Development of Shengli Oilfield,SINOPEC,Dongying,Shandong 257000,China

2.Petroleum Development Center of Shengli Oilfield,SINOPEC,Dongying,Shandong 257000,China

Researchprogressofup-goinganddown-goingwavefieldseparationfordual-sensordata.GAOShao-wu1，SUNPengyuan1，FANGYunfeng1，MAGuangkai1，ZHANGXudong1，andYUWanhui1.OilGeophysicalProspecting,2021,56(6):1419-1429.

With offshore oil and gas exploration and development,the application of dual-sensor ocean bottom receivers is becoming increasingly extensive.As the key technology of dual-sensor data processing,the up-going and down-going wavefield separation determines the data processing quality and application effect.In the development of the up-going and down-going wavefield separation techniques for the dual-sensor data,many feasible schemes have been proposed by scholars in China and other countries.In this paper,the methods and techniques of separating up-going and down-going wavefields are summarized by the systematic investigation of relevant literature.Firstly,in light of the wave equation theory,the dual-sensor data is linked with the up-going and down-going wavefields,which lays the theoretical foundation for the dual-sensor data calibration and the up-going and down-going wavefield separation.Specifically,the wavefield data received by hydrophone and geophone are pressure wavefields and particle vertical velocity wavefields respectively.The pressure wavefields can be decomposed into the up-going and down-going pressure wavefields,and the particle vertical velocity wavefields can be decomposed into the up-going and down-going particle vertical velocity wavefields.The amplitude of down-going pressure wavefields is proportional to that of the down-going particle velocity wavefields,and they have the same polarity,while the amplitude of the up-going pressure wavefields is proportional to that of the up-going particle velocity wavefields,and they have opposite polarity.Then,the OBC dual-sensor data processing technology based on eliminating the sea-water reverberations is established for OBC/OBN hydrophone and geophone data.Finally,this paper summarizes seven kinds of the up-going and down-going wavefield separation methods for dual-sensor data,including constant calibration factor separation,frequency-wavenumber domain separation,mirror separa-tion,deghosting separation,optimal deghosting separation,geophone pulse response separation andτ-pdomain separation.Before the up-going and down-going wavefield separation for the dual-sensor data,the geophone impulse response correction should be carried out first.Then,various preprocesses are performed for the dual-sensor data,such as matching and calibration processes,so that the up-going and down-going pressure wavefields in the hydrophone data converge with the up-going and down-going particle vertical velocity wavefields in the geophone data in terms of amplitudes and frequencies.In addition,because the polarity of the down-going pressure wavefields is the same with that of the down-going particle vertical velocity wavefields,and the polarity of the up-going pressure wavefields is opposite to that of the up-going particle vertical velocity wavefields,the band notches are compensated,and the ghosts at the receiver point are reasonably suppressed.Finally,the best up-going and down-going wavefield separation is achieved.

Keywords：ocean bottom cable (OBC)，ocean bottom node (OBN)，hydrophone and geophone data，wavefield separation，up-going wavefield，down-going wavefield，pressure wavefield，vertical velocity wavefield

1.Geophysical Research &Development Center，BGP Inc.，CNPC，Zhuozhou，Hebei 072751，China

Keyproblemsinthedevelopmentofpetroleumelectromagneticlogging.ZHANGYi1,2,FENGHong2,HANXue3,CHENGang2,andJIANGBici1,2.OilGeophysicalProspecting,2021,56(6):1430-1447.

Induction logging is commonly used in the wireline logging.It has a large detection depth and can be applied to oil-base mud,which plays an irreplaceable role in the petroleum logging.Electromagnetic wave logging is more effective in logging-while-drilling (LWD),and the azimuth electromagnetic wave logging technology can identify interface and distinguish azimuth,which has become one of the key technologies and main research directions of geosteering while drilling.This paper reviews the development of induction logging and electromagnetic logging instruments and points out the characteristics and design differences in design as well as difficulties in hardware design and manufacture of the instruments in each development stage.This paper summarizes the factors that influence the response of petroleum electromagnetic logging and analyzes the similarities and differences between the effects of environmental factors on the induction logging and electromagnetic logging.It considers the relative dip angle and borehole environmental correction as the main factors that influence the application of coplanar or cross components.According to the advantages and disadvantages of the common forward and inversion technology,it is concluded that the lack of fast and reliable forward and inversion technology in the complex strata restricts the application of electromagnetic logging in real-time interpretation and geo-steering.The difficulties in the electromagnetic logging data interpretation and technical upgrading are summarized.Finally,the development vision of the electromagnetic logging is prospected.

Keywords：induction logging,electromagnetic wave logging,logging while drilling,geosteering,forward and inverse

1.China Coal Research Institute,Beijing 100013,China

2.Xi'an Research Institute of China Coal Techno-logy &Engineering Group Corp,Xi’an,Shaanxi 710054，China

3.China Petroleum Logging Co.LTD,Xi'an,Shaanxi 710077,China

Influencingfactorsofseismicwavevelocity.YUNMeihou1,LIXiaobin1,andFENGLei1.OilGeophysicalProspecting,2021,56(6):1448-1458.

Given the classification confusion of influencing factors of seismic wave velocity,the influencing factors are systematically sorted and classified according to the dialectical theory of internal and external causes on the premise of a detailed analysis of the factors.It is pointed out that the mineral composition,structure,pore structure,porosity,and pore fluid type and saturation are the internal geological factors affecting the velocity.The temperature field,the pressure field,and the various physical and chemical fields related to diagenesis and metamorphism are the external environmental factors affecting the velocity.The relations of velocity with density,lithology,buried depth,geological age,and anisotropy are discussed,and it is concluded that it is inappropriate to take them as the influencing factors of seismic wave velocity in that there they have no essential connection with velocity change.

Keywords：seismic wave velocity,rock velocity,influencing factor,density,lithology,geological age

1.Institute of Resources and Environment,Henan Polytechnic University,Jiaozuo,Henan,454000,China