The brittleness index(BI)is crucial for predicting engineering sweet spots and designing fracturing operations in shale oil reservoir exploration and development.Seismic amplitude variation with offset(AVO)inversion i...The brittleness index(BI)is crucial for predicting engineering sweet spots and designing fracturing operations in shale oil reservoir exploration and development.Seismic amplitude variation with offset(AVO)inversion is commonly used to obtain the BI.Traditionally,velocity,density,and other parameters are firstly inverted,and the BI is then calculated,which often leads to accumulated errors.Moreover,due to the limited of well-log data in field work areas,AVO inversion typically faces the challenge of limited information,resulting in not high accuracy of BI derived by existing AVO inversion methods.To address these issues,we first derive an AVO forward approximation equation that directly characterizes the BI in P-wave reflection coefficients.Based on this,an intelligent AVO inversion method,which combines the advantages of traditional and intelligent approaches,for directly obtaining the BI is proposed.A TransUnet model is constructed to establish the strong nonlinear mapping relationship between seismic data and the BI.By incorporating a combined objective function that is constrained by both low-frequency parameters and training samples,the challenge of limited samples is effectively addressed,and the direct inversion of the BI is stably achieved.Tests on model data and applications on field data demonstrate the feasibility,advancement,and practicality of the proposed method.展开更多
Pre-stack seismic inversion is an effective way to investigate the characteristics of hydrocarbon-bearing reservoirs.Multi-parameter application is the key to identifying reservoir lithology and fluid in pre-stack inv...Pre-stack seismic inversion is an effective way to investigate the characteristics of hydrocarbon-bearing reservoirs.Multi-parameter application is the key to identifying reservoir lithology and fluid in pre-stack inversion.However,multi-parameter inversion may bring coupling effects on the parameters and destabilize the inversion.In addition,the lateral recognition accuracy of geological structures receives great attention.To address these challenges,a multi-task learning network considering the angle-gather difference is proposed in this work.The deep learning network is usually assumed as a black box and it is unclear what it can learn.However,the introduction of angle-gather difference can force the deep learning network to focus on the lateral differences,thus improving the lateral accuracy of the prediction profile.The proposed deep learning network includes input and output blocks.First,angle gathers and the angle-gather difference are fed into two separate input blocks with Res Net architecture and Unet architecture,respectively.Then,three elastic parameters,including P-and S-wave velocities and density,are simultaneously predicted based on the idea of multi-task learning by using three separate output blocks with the same convolutional network layers.Experimental and field data tests demonstrate the effectiveness of the proposed method in improving the prediction accuracy of seismic elastic parameters.展开更多
Although the ambiguity of seismic inversion is widely recognized in both theory and practice, so far as a concrete inversion example is concerned, there is not any objective, controllable method or any standard for ho...Although the ambiguity of seismic inversion is widely recognized in both theory and practice, so far as a concrete inversion example is concerned, there is not any objective, controllable method or any standard for how to evaluate and determine its ambiguity and reliability, especially for the high frequency components beyond the effective seismic frequency band. Taking log-constrained impedance inversion as an example, a new appraisal method is proposed on the basis of analyzing a simple geological model. Firstly, the inverted impedance model is transformed to a reflection coefficient series. Secondly, the maximum effective frequency of the real seismic data is chosen as a cutoff point and the reflection coefficient series is decomposed into two components by low-pass and high-pass filters. Thirdly, the geometrical reflection characteristics of the high-frequency components and that of the real seismic data are compared and analyzed. Then, the reliability of the inverted impedance model is appraised according to the similarity of geometrical characteristics between the high-frequency components and the real seismic data. The new method avoids some subjectivity in appraising the inverted result, and helps to enhance the reliability of reservoir prediction by impedance inversion technology.展开更多
Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep l...Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based methods.In order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets.Three kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training process.Benefit from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real data.Moreover,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model.The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases.And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.展开更多
Seismic amplitude variation with offset(AVO) inversion is an important approach for quantitative prediction of rock elasticity,lithology and fluid properties.With Biot-Gassmann's poroelasticity,an improved statist...Seismic amplitude variation with offset(AVO) inversion is an important approach for quantitative prediction of rock elasticity,lithology and fluid properties.With Biot-Gassmann's poroelasticity,an improved statistical AVO inversion approach is proposed.To distinguish the influence of rock porosity and pore fluid modulus on AVO reflection coefficients,the AVO equation of reflection coefficients parameterized by porosity,rock-matrix moduli,density and fluid modulus is initially derived from Gassmann equation and critical porosity model.From the analysis of the influences of model parameters on the proposed AVO equation,rock porosity has the greatest influences,followed by rock-matrix moduli and density,and fluid modulus has the least influences among these model parameters.Furthermore,a statistical AVO stepwise inversion method is implemented to the simultaneous estimation of rock porosity,rock-matrix modulus,density and fluid modulus.Besides,the Laplace probability model and differential evolution,Markov chain Monte Carlo algorithm is utilized for the stochastic simulation within Bayesian framework.Models and field data examples demonstrate that the simultaneous optimizations of multiple Markov chains can achieve the efficient simulation of the posterior probability density distribution of model parameters,which is helpful for the uncertainty analysis of the inversion and sets a theoretical fundament for reservoir characterization and fluid discrimination.展开更多
Non-liner wave equation inversion,wavelet analysis and artificial neural networks were used to obtain stratum parameters and the distribution of thin coal seams.The lithology of the water-bearing/resisting layer in th...Non-liner wave equation inversion,wavelet analysis and artificial neural networks were used to obtain stratum parameters and the distribution of thin coal seams.The lithology of the water-bearing/resisting layer in the Quaternary system was also predicted.The implementation process included calculating the well log parameters,stratum contrasting the seismic data and the well logs,and extracting,studying and predicting seismic attributes.Seismic inversion parameters,including the layer velocity and wave impedance,were calculated and effectively used for prediction and analysis.Prior knowledge and seismic interpretation were used to remedy a dearth of seismic data during the inversion procedure.This enhanced the stability of the inversion method.Non-linear seismic inversion and artificial neural networks were used to interpret coal seismic lithology and to study the water-bearing/resisting layer in the Quaternary system.Interpretation of the 1～2 m thin coal seams,and also of the water-bearing/resisting layer in the Quaternary system,is provided.The upper mining limit can be lifted from 60 m to 45 m.The predictions show that this method can provide reliable data useful for thin coal seam exploitation and for lifting the upper mining limit,which is one of the principles of green mining.展开更多
Deep learning is widely used for seismic impedance inversion,but few work provides in-depth research and analysis on designing the architectures of deep neural networks and choosing the network hyperparameters.This pa...Deep learning is widely used for seismic impedance inversion,but few work provides in-depth research and analysis on designing the architectures of deep neural networks and choosing the network hyperparameters.This paper is dedicated to comprehensively studying on the significant aspects of deep neural networks that affect the inversion results.We experimentally reveal how network hyperparameters and architectures affect the inversion performance,and develop a series of methods which are proven to be effective in reconstructing high-frequency information in the estimated impedance model.Experiments demonstrate that the proposed multi-scale architecture is helpful to reconstruct more high-frequency details than a conventional network.Besides,the reconstruction of high-frequency information can be further promoted by introducing a perceptual loss and a generative adversarial network from the computer vision perspective.More importantly,the experimental results provide valuable references for designing proper network architectures in the seismic inversion problem.展开更多
Under the condition of thin interbeds with great lateral changes in terrestrial basins,a seismic meme inversion method is established based on the analysis of seismic sedimentology technology.The relationship between ...Under the condition of thin interbeds with great lateral changes in terrestrial basins,a seismic meme inversion method is established based on the analysis of seismic sedimentology technology.The relationship between seismic waveform and high-frequency well logs is established through dynamic clustering of seismic waveform to improve the vertical and horizontal resolution of inversion results;meanwhile,by constructing the Bayesian inversion framework of different seismic facies,the real facies controlled inversion is realized.The forward model verification results show that the seismic meme inversion can realize precise prediction of 3 m thick thin interbeds,proving the rationality and high precision of the method.The application in the Daqing placanticline shows that the seismic meme inversion could identify 2 m thin interbeds,and the coincidence rates of inversion results and drilling data were more than 80%.The seismic meme inversion method can improve the accuracy of reservoir prediction and provides a useful mean for thin interbeds prediction in terrestrial basins.展开更多
Seismic inversion and basic theory are briefly presented and the main idea of this method is introduced. Both non-linear wave equation inversion technique and Complete Utilization of Samples Information (CUSI) neural ...Seismic inversion and basic theory are briefly presented and the main idea of this method is introduced. Both non-linear wave equation inversion technique and Complete Utilization of Samples Information (CUSI) neural network analysis are used in lithological interpretation in Jibei coal field. The prediction results indicate that this method can provide reliable data for thin coal exploitation and promising area evaluation.展开更多
Total organic carbon (TOC) prediction with elastic parameter inversions has been widely used in the identification and evaluation of source rocks. However, the elastic parameters used to predict TOC are not only deter...Total organic carbon (TOC) prediction with elastic parameter inversions has been widely used in the identification and evaluation of source rocks. However, the elastic parameters used to predict TOC are not only determined by TOC but also depend on the other physical properties of source rocks. Besides, the TOC prediction with the elastic parameters inversion is an indirect method based on the statistical relationship obtained from well logs and experiment data. Therefore, we propose a rock physics model and define a TOC indicator mainly affected by TOC to predict TOC directly. The proposed rock physics model makes the equivalent elastic moduli of source rocks parameterized by the TOC indicator. Combining the equivalent elastic moduli of source rocks and Gray’s approximation leads to a novel linearized approximation of the P-wave reflection coefficient incorporating the TOC indicator. Model examples illustrate that the novel reflectivity approximation well agrees with the exact Zoeppritz equation until incident angles reach 40°. Convoluting the novel P-wave reflection approximation with seismic wavelets as the forward solver, an AVO inversion method based on the Bayesian theory is proposed to invert the TOC indicator with seismic data. The synthetic examples and field tests validate the feasibility and stability of the proposed AVO inversion approach. Using the inversion results of the TOC indicator, TOC is directly and accurately estimated in the target area.展开更多
In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inve...In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inversion. Through numerical simulation, we tested the effects of different algorithm parameters and different model parameterization methods on PSO wave impedance inversion, and analyzed the characteristics of PSO method. Under the conclusions drawn from numerical simulation, we propose the scheme of combining a cross-moving strategy based on a divided block model and high-frequency filtering technology for PSO inversion. By analyzing the inversion results of a wedge model of a pitchout coal seam and a coal coking model with igneous rock intrusion, we discuss the vertical and horizontal resolution, stability and reliability of PSO inversion. Based on the actual seismic and logging data from an igneous area, by taking a seismic profile through wells as an example, we discuss the characteristics of three inversion methods, including model-based wave impedance inversion, multi-attribute seismic inversion based on probabilistic neural network(PNN) and wave impedance inversion based on PSO.And we draw the conclusion that the inversion based on PSO method has a better result for this igneous area.展开更多
In this paper seismic inversion was used as a key technique and the seismic wavelet most suitable to the actual underground situation was extracted with the higher-order statistics algorithm. The wavelets extracted in...In this paper seismic inversion was used as a key technique and the seismic wavelet most suitable to the actual underground situation was extracted with the higher-order statistics algorithm. The wavelets extracted in this way and the wavelets extracted with the seismic statistics techniques were used separately for inverting the seismic data of the southern part of Tahe oilfield, Tarim basin. The results showed that the resolution of the wavelet inversion with the higher-order statistics method was greatly improved, and the wavelet-inverted section could better distinguish the thin sandstone reservoirs of the upper and lower Carboniferous and their lateral distribution, providing a reliable basis of analysis for the study of thin sandstone reservoirs.展开更多
Geoscientific evidence shows that various parameters such as compaction,buoyancy effect,hydrocarbon maturation,gas effect and tectonic activities control the pore pressure of sub-surface geology.Spatially controlled g...Geoscientific evidence shows that various parameters such as compaction,buoyancy effect,hydrocarbon maturation,gas effect and tectonic activities control the pore pressure of sub-surface geology.Spatially controlled geoscientific data in the tectonically active areas is significantly useful for robust estimation of pre-drill pore pressure.The reservoir which is tectonically complex and pore pressure is changing frequently that circumference motivated us to conduct this study.The changes in pore pressure have been captured from the fine-scale to the broad scale in the Jaisalmer sub-basin.Pore pressure variation has been distinctly observed in pre-and post-Jurassic age based on the current study.Post-stack seismic inversion study was conducted to capturing the variation of pore pressure.Analysis of low-frequency spectrum and integrated interval velocity model provided a detailed feature of pore pressure in each compartment of the study area.Pore pressure estimated from well log data was correlated with seismic inversion based result.Based on the current study one well has been proposed where pore pressure was estimated and two distinguished trends are identified in the study zone.The approaches of the current study were analysed thoroughly and it will be highly useful in complex reservoir condition where pore pressure varies frequently.展开更多
The objective of this work is to implement a pseudo-forward equation which is called PFE to transform data （similarity attribute） to model parameters （porosity） in a gas reservoir in the F3 block of North Sea. Thi...The objective of this work is to implement a pseudo-forward equation which is called PFE to transform data （similarity attribute） to model parameters （porosity） in a gas reservoir in the F3 block of North Sea. This equation which is an experimental model has unknown constants in its structure; hence, a least square solution is applied to find the best constants. The results derived from solved equa- tions show that the errors on measured data are mapped into the errors of estimated constants; hence, Tikhonov regularization is used to improve the estimated parameters. The results are compared with a conventional method such as cross plotting between acoustic impedance and porosity values to validate the PFE model. When the testing dataset in sand units was used, the correlation coefficient between two variables （actual and predicted values） was obtained as 0.720 and 0.476 for PFE model and cross-plotting analysis, respectively. Therefore, the testing dataset validates rela- tively well the PFE optimized by Tikhonov regularization in sand units of a gas reservoir. The obtained results indi- cate that PFE could provide initial information about sandstone reservoirs. It could estimate reservoir porosity distribution approximately and it highlights bright spots and fault structures such as gas chimneys and salt edges.展开更多
Seismic inversion is one of the most important methods for lithological prospecting . Seismic data with lowresolution is converted into impedance data of high resolution which can reflect the geological structure by i...Seismic inversion is one of the most important methods for lithological prospecting . Seismic data with lowresolution is converted into impedance data of high resolution which can reflect the geological structure by inversionThe inversion technique of 3D seismic data is discussed from both methodological and theoretical aspects, and the in-version test is also carried out using actual logging data. The result is identical with the measured data obtained fromroadway of coal mine. The field tests and research results indicate that this method can provide more accurate data foridentifying thin coal seam and minor faults.展开更多
As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this prob...As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this problem,a model-data-driven seismic AVO(amplitude variation with offset)inversion method based on a space-variant objective function has been worked out.In this method,zero delay cross-correlation function and F norm are used to establish objective function.Based on inverse distance weighting theory,change of the objective function is controlled according to the location of the target CDP(common depth point),to change the constraint weights of training samples,initial low-frequency models,and seismic data on the inversion.Hence,the proposed method can get high resolution and high-accuracy velocity and density from inversion of small sample data,and is suitable for identifying thin interbedded sand bodies.Tests with thin interbedded geological models show that the proposed method has high inversion accuracy and resolution for small sample data,and can identify sandstone and mudstone layers of about one-30th of the dominant wavelength thick.Tests on the field data of Lishui sag show that the inversion results of the proposed method have small relative error with well-log data,and can identify thin interbedded sandstone layers of about one-15th of the dominant wavelength thick with small sample data.展开更多
Instability is an inherent problem with the attenuation compensation methods and has been partially relieved by using the inverse scheme.However,the conventional inversion-based attenuation compensation approaches ign...Instability is an inherent problem with the attenuation compensation methods and has been partially relieved by using the inverse scheme.However,the conventional inversion-based attenuation compensation approaches ignore the important prior information of the seismic dip.Thus,the compensated result appears to be distorted spatial continuity and has a low signal-to-noise ratio(S/N).To alleviate this issue,we have incorporated the seismic dip information into the inversion framework and have developed a dip-constrained attenuation compensation(DCAC)algorithm.The seismic dip information,calculated from the poststack seismic data,is the key to construct a dip constraint term.Benefiting from the introduction of the seismic dip constraint,the DCAC approach maintains the numerical stability and preserves the spatial continuity of the compensated result.Synthetic and field data examples demonstrate that the proposed method can not only improve seismic resolution,but also protect the continuity of seismic data.展开更多
基金supposed by the National Nature Science Foundation of China(Grant No.42304131)the Natural Science Foundation of Heilongjiang Province(Grant No.LH2023D012)+1 种基金the Heilongjiang Postdoctoral Fund(Grant No.LBH-Z22092)the Basic Research Fund for Universities in Xinjiang Uygur Autonomous Region(Grant No.XJEDU2023P166)。
文摘The brittleness index(BI)is crucial for predicting engineering sweet spots and designing fracturing operations in shale oil reservoir exploration and development.Seismic amplitude variation with offset(AVO)inversion is commonly used to obtain the BI.Traditionally,velocity,density,and other parameters are firstly inverted,and the BI is then calculated,which often leads to accumulated errors.Moreover,due to the limited of well-log data in field work areas,AVO inversion typically faces the challenge of limited information,resulting in not high accuracy of BI derived by existing AVO inversion methods.To address these issues,we first derive an AVO forward approximation equation that directly characterizes the BI in P-wave reflection coefficients.Based on this,an intelligent AVO inversion method,which combines the advantages of traditional and intelligent approaches,for directly obtaining the BI is proposed.A TransUnet model is constructed to establish the strong nonlinear mapping relationship between seismic data and the BI.By incorporating a combined objective function that is constrained by both low-frequency parameters and training samples,the challenge of limited samples is effectively addressed,and the direct inversion of the BI is stably achieved.Tests on model data and applications on field data demonstrate the feasibility,advancement,and practicality of the proposed method.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.42130810,42204135,42174170,and 42074165)the Natural Science Foundation of Hunan Province(Grant No.2023JJ40716)。
文摘Pre-stack seismic inversion is an effective way to investigate the characteristics of hydrocarbon-bearing reservoirs.Multi-parameter application is the key to identifying reservoir lithology and fluid in pre-stack inversion.However,multi-parameter inversion may bring coupling effects on the parameters and destabilize the inversion.In addition,the lateral recognition accuracy of geological structures receives great attention.To address these challenges,a multi-task learning network considering the angle-gather difference is proposed in this work.The deep learning network is usually assumed as a black box and it is unclear what it can learn.However,the introduction of angle-gather difference can force the deep learning network to focus on the lateral differences,thus improving the lateral accuracy of the prediction profile.The proposed deep learning network includes input and output blocks.First,angle gathers and the angle-gather difference are fed into two separate input blocks with Res Net architecture and Unet architecture,respectively.Then,three elastic parameters,including P-and S-wave velocities and density,are simultaneously predicted based on the idea of multi-task learning by using three separate output blocks with the same convolutional network layers.Experimental and field data tests demonstrate the effectiveness of the proposed method in improving the prediction accuracy of seismic elastic parameters.
基金supported by the Major Basic Research Development Program of China’s 973 Project(grant No.2007CB209608)the Science and Technology Innovation Foundation of CNPC(grant No.2010D-5006-0301)
文摘Although the ambiguity of seismic inversion is widely recognized in both theory and practice, so far as a concrete inversion example is concerned, there is not any objective, controllable method or any standard for how to evaluate and determine its ambiguity and reliability, especially for the high frequency components beyond the effective seismic frequency band. Taking log-constrained impedance inversion as an example, a new appraisal method is proposed on the basis of analyzing a simple geological model. Firstly, the inverted impedance model is transformed to a reflection coefficient series. Secondly, the maximum effective frequency of the real seismic data is chosen as a cutoff point and the reflection coefficient series is decomposed into two components by low-pass and high-pass filters. Thirdly, the geometrical reflection characteristics of the high-frequency components and that of the real seismic data are compared and analyzed. Then, the reliability of the inverted impedance model is appraised according to the similarity of geometrical characteristics between the high-frequency components and the real seismic data. The new method avoids some subjectivity in appraising the inverted result, and helps to enhance the reliability of reservoir prediction by impedance inversion technology.
基金financially supported by the NSFC(Grant No.41974126 and 41674116)the National Key Research and Development Program of China(Grant No.2018YFA0702501)the 13th 5-Year Basic Research Program of China National Petroleum Corporation(CNPC)(2018A-3306)。
文摘Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based methods.In order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets.Three kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training process.Benefit from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real data.Moreover,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model.The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases.And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.
基金the sponsorship of National Grand Project for Science and Technology(2016ZX05024004,2017ZX05009001,2017ZX05032003)the Fundamental Research Funds for the Central Universities(20CX06036A)+1 种基金the Postdoctoral Applied Research Project of Qingdao(QDYY20190040)the Science Foundation from SINOPEC Key Laboratory of Geophysics(wtyjy-wx2019-01-04)。
文摘Seismic amplitude variation with offset(AVO) inversion is an important approach for quantitative prediction of rock elasticity,lithology and fluid properties.With Biot-Gassmann's poroelasticity,an improved statistical AVO inversion approach is proposed.To distinguish the influence of rock porosity and pore fluid modulus on AVO reflection coefficients,the AVO equation of reflection coefficients parameterized by porosity,rock-matrix moduli,density and fluid modulus is initially derived from Gassmann equation and critical porosity model.From the analysis of the influences of model parameters on the proposed AVO equation,rock porosity has the greatest influences,followed by rock-matrix moduli and density,and fluid modulus has the least influences among these model parameters.Furthermore,a statistical AVO stepwise inversion method is implemented to the simultaneous estimation of rock porosity,rock-matrix modulus,density and fluid modulus.Besides,the Laplace probability model and differential evolution,Markov chain Monte Carlo algorithm is utilized for the stochastic simulation within Bayesian framework.Models and field data examples demonstrate that the simultaneous optimizations of multiple Markov chains can achieve the efficient simulation of the posterior probability density distribution of model parameters,which is helpful for the uncertainty analysis of the inversion and sets a theoretical fundament for reservoir characterization and fluid discrimination.
基金Projects 40574057 and 40874054 supported by the National Natural Science Foundation of ChinaProjects 2007CB209400 by the National Basic Research Program of ChinaFoundation of China University of Mining and Technology (OF4471)
文摘Non-liner wave equation inversion,wavelet analysis and artificial neural networks were used to obtain stratum parameters and the distribution of thin coal seams.The lithology of the water-bearing/resisting layer in the Quaternary system was also predicted.The implementation process included calculating the well log parameters,stratum contrasting the seismic data and the well logs,and extracting,studying and predicting seismic attributes.Seismic inversion parameters,including the layer velocity and wave impedance,were calculated and effectively used for prediction and analysis.Prior knowledge and seismic interpretation were used to remedy a dearth of seismic data during the inversion procedure.This enhanced the stability of the inversion method.Non-linear seismic inversion and artificial neural networks were used to interpret coal seismic lithology and to study the water-bearing/resisting layer in the Quaternary system.Interpretation of the 1～2 m thin coal seams,and also of the water-bearing/resisting layer in the Quaternary system,is provided.The upper mining limit can be lifted from 60 m to 45 m.The predictions show that this method can provide reliable data useful for thin coal seam exploitation and for lifting the upper mining limit,which is one of the principles of green mining.
基金supported by the National Natural Science Foundation of China under Grant No.42050104
文摘Deep learning is widely used for seismic impedance inversion,but few work provides in-depth research and analysis on designing the architectures of deep neural networks and choosing the network hyperparameters.This paper is dedicated to comprehensively studying on the significant aspects of deep neural networks that affect the inversion results.We experimentally reveal how network hyperparameters and architectures affect the inversion performance,and develop a series of methods which are proven to be effective in reconstructing high-frequency information in the estimated impedance model.Experiments demonstrate that the proposed multi-scale architecture is helpful to reconstruct more high-frequency details than a conventional network.Besides,the reconstruction of high-frequency information can be further promoted by introducing a perceptual loss and a generative adversarial network from the computer vision perspective.More importantly,the experimental results provide valuable references for designing proper network architectures in the seismic inversion problem.
文摘Under the condition of thin interbeds with great lateral changes in terrestrial basins,a seismic meme inversion method is established based on the analysis of seismic sedimentology technology.The relationship between seismic waveform and high-frequency well logs is established through dynamic clustering of seismic waveform to improve the vertical and horizontal resolution of inversion results;meanwhile,by constructing the Bayesian inversion framework of different seismic facies,the real facies controlled inversion is realized.The forward model verification results show that the seismic meme inversion can realize precise prediction of 3 m thick thin interbeds,proving the rationality and high precision of the method.The application in the Daqing placanticline shows that the seismic meme inversion could identify 2 m thin interbeds,and the coincidence rates of inversion results and drilling data were more than 80%.The seismic meme inversion method can improve the accuracy of reservoir prediction and provides a useful mean for thin interbeds prediction in terrestrial basins.
文摘Seismic inversion and basic theory are briefly presented and the main idea of this method is introduced. Both non-linear wave equation inversion technique and Complete Utilization of Samples Information (CUSI) neural network analysis are used in lithological interpretation in Jibei coal field. The prediction results indicate that this method can provide reliable data for thin coal exploitation and promising area evaluation.
基金The authors acknowledge the sponsorship of National Natural Science Foundation of China(42174139,41974119,42030103)Laoshan Laboratory Science and Technology Innovation Program(LSKj202203406)Science Foundation from Innovation and Technology Support Program for Young Scientists in Colleges of Shandong Province and Ministry of Science and Technology of China(2019RA2136).
文摘Total organic carbon (TOC) prediction with elastic parameter inversions has been widely used in the identification and evaluation of source rocks. However, the elastic parameters used to predict TOC are not only determined by TOC but also depend on the other physical properties of source rocks. Besides, the TOC prediction with the elastic parameters inversion is an indirect method based on the statistical relationship obtained from well logs and experiment data. Therefore, we propose a rock physics model and define a TOC indicator mainly affected by TOC to predict TOC directly. The proposed rock physics model makes the equivalent elastic moduli of source rocks parameterized by the TOC indicator. Combining the equivalent elastic moduli of source rocks and Gray’s approximation leads to a novel linearized approximation of the P-wave reflection coefficient incorporating the TOC indicator. Model examples illustrate that the novel reflectivity approximation well agrees with the exact Zoeppritz equation until incident angles reach 40°. Convoluting the novel P-wave reflection approximation with seismic wavelets as the forward solver, an AVO inversion method based on the Bayesian theory is proposed to invert the TOC indicator with seismic data. The synthetic examples and field tests validate the feasibility and stability of the proposed AVO inversion approach. Using the inversion results of the TOC indicator, TOC is directly and accurately estimated in the target area.
基金provided by the National Science and Technology Major Project(No.2011ZX05004-004)China National Petroleum Corporation Key Projects(No.2014E2105)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inversion. Through numerical simulation, we tested the effects of different algorithm parameters and different model parameterization methods on PSO wave impedance inversion, and analyzed the characteristics of PSO method. Under the conclusions drawn from numerical simulation, we propose the scheme of combining a cross-moving strategy based on a divided block model and high-frequency filtering technology for PSO inversion. By analyzing the inversion results of a wedge model of a pitchout coal seam and a coal coking model with igneous rock intrusion, we discuss the vertical and horizontal resolution, stability and reliability of PSO inversion. Based on the actual seismic and logging data from an igneous area, by taking a seismic profile through wells as an example, we discuss the characteristics of three inversion methods, including model-based wave impedance inversion, multi-attribute seismic inversion based on probabilistic neural network(PNN) and wave impedance inversion based on PSO.And we draw the conclusion that the inversion based on PSO method has a better result for this igneous area.
文摘In this paper seismic inversion was used as a key technique and the seismic wavelet most suitable to the actual underground situation was extracted with the higher-order statistics algorithm. The wavelets extracted in this way and the wavelets extracted with the seismic statistics techniques were used separately for inverting the seismic data of the southern part of Tahe oilfield, Tarim basin. The results showed that the resolution of the wavelet inversion with the higher-order statistics method was greatly improved, and the wavelet-inverted section could better distinguish the thin sandstone reservoirs of the upper and lower Carboniferous and their lateral distribution, providing a reliable basis of analysis for the study of thin sandstone reservoirs.
文摘Geoscientific evidence shows that various parameters such as compaction,buoyancy effect,hydrocarbon maturation,gas effect and tectonic activities control the pore pressure of sub-surface geology.Spatially controlled geoscientific data in the tectonically active areas is significantly useful for robust estimation of pre-drill pore pressure.The reservoir which is tectonically complex and pore pressure is changing frequently that circumference motivated us to conduct this study.The changes in pore pressure have been captured from the fine-scale to the broad scale in the Jaisalmer sub-basin.Pore pressure variation has been distinctly observed in pre-and post-Jurassic age based on the current study.Post-stack seismic inversion study was conducted to capturing the variation of pore pressure.Analysis of low-frequency spectrum and integrated interval velocity model provided a detailed feature of pore pressure in each compartment of the study area.Pore pressure estimated from well log data was correlated with seismic inversion based result.Based on the current study one well has been proposed where pore pressure was estimated and two distinguished trends are identified in the study zone.The approaches of the current study were analysed thoroughly and it will be highly useful in complex reservoir condition where pore pressure varies frequently.
文摘The objective of this work is to implement a pseudo-forward equation which is called PFE to transform data （similarity attribute） to model parameters （porosity） in a gas reservoir in the F3 block of North Sea. This equation which is an experimental model has unknown constants in its structure; hence, a least square solution is applied to find the best constants. The results derived from solved equa- tions show that the errors on measured data are mapped into the errors of estimated constants; hence, Tikhonov regularization is used to improve the estimated parameters. The results are compared with a conventional method such as cross plotting between acoustic impedance and porosity values to validate the PFE model. When the testing dataset in sand units was used, the correlation coefficient between two variables （actual and predicted values） was obtained as 0.720 and 0.476 for PFE model and cross-plotting analysis, respectively. Therefore, the testing dataset validates rela- tively well the PFE optimized by Tikhonov regularization in sand units of a gas reservoir. The obtained results indi- cate that PFE could provide initial information about sandstone reservoirs. It could estimate reservoir porosity distribution approximately and it highlights bright spots and fault structures such as gas chimneys and salt edges.
文摘Seismic inversion is one of the most important methods for lithological prospecting . Seismic data with lowresolution is converted into impedance data of high resolution which can reflect the geological structure by inversionThe inversion technique of 3D seismic data is discussed from both methodological and theoretical aspects, and the in-version test is also carried out using actual logging data. The result is identical with the measured data obtained fromroadway of coal mine. The field tests and research results indicate that this method can provide more accurate data foridentifying thin coal seam and minor faults.
文摘As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this problem,a model-data-driven seismic AVO(amplitude variation with offset)inversion method based on a space-variant objective function has been worked out.In this method,zero delay cross-correlation function and F norm are used to establish objective function.Based on inverse distance weighting theory,change of the objective function is controlled according to the location of the target CDP(common depth point),to change the constraint weights of training samples,initial low-frequency models,and seismic data on the inversion.Hence,the proposed method can get high resolution and high-accuracy velocity and density from inversion of small sample data,and is suitable for identifying thin interbedded sand bodies.Tests with thin interbedded geological models show that the proposed method has high inversion accuracy and resolution for small sample data,and can identify sandstone and mudstone layers of about one-30th of the dominant wavelength thick.Tests on the field data of Lishui sag show that the inversion results of the proposed method have small relative error with well-log data,and can identify thin interbedded sandstone layers of about one-15th of the dominant wavelength thick with small sample data.
基金financial support provided by National Natural Science Foundation of China(42074141)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-03)National Key R&D Program of China(2018YFA0702504)
文摘Instability is an inherent problem with the attenuation compensation methods and has been partially relieved by using the inverse scheme.However,the conventional inversion-based attenuation compensation approaches ignore the important prior information of the seismic dip.Thus,the compensated result appears to be distorted spatial continuity and has a low signal-to-noise ratio(S/N).To alleviate this issue,we have incorporated the seismic dip information into the inversion framework and have developed a dip-constrained attenuation compensation(DCAC)algorithm.The seismic dip information,calculated from the poststack seismic data,is the key to construct a dip constraint term.Benefiting from the introduction of the seismic dip constraint,the DCAC approach maintains the numerical stability and preserves the spatial continuity of the compensated result.Synthetic and field data examples demonstrate that the proposed method can not only improve seismic resolution,but also protect the continuity of seismic data.
