Submarine seep plumes are a natural phenomenon in which different types of gases migrate through deep or shallow subsurface sediments and leak into seawater in pressure gradient.When detected using acoustic data,the l...Submarine seep plumes are a natural phenomenon in which different types of gases migrate through deep or shallow subsurface sediments and leak into seawater in pressure gradient.When detected using acoustic data,the leaked gases frequently exhibit a flame-like structure.We numerically modelled the relationship between the seismic response characteristic and bubble volume fraction to establish the bubble volume fraction in the submarine seep plume.Results show that our models are able to invert and predict the bubble volume fraction from field seismic oceanography data,by which synthetic seismic sections in different dominant frequencies could be numerically simulated,seismic attribute sections(e.g.,instantaneous amplitude,instantaneous frequency,and instantaneous phase)extracted,and the correlation between the seismic attributes and bubble volume fraction be quantitatively determined with functional equations.The instantaneous amplitude is positively correlated with bubble volume fraction,while the instantaneous frequency and bubble volume fraction are negatively correlated.In addition,information entropy is introduced as a proxy to quantify the relationship between the instantaneous phase and bubble volume fraction.As the bubble volume fraction increases,the information entropy of the instantaneous phase increases rapidly at the beginning,followed by a slight upward trend,and finally stabilizes.Therefore,under optimal noise conditions,the bubble volume fraction of submarine seep plumes can be inverted and predicted based on seismic response characteristics in terms of seismic attributes.展开更多
Small structures in coal mine working face is one of the main hidden dangers of safe and effi cient production in coal mine.Currently,seismic exploration is often used as the main method for detecting such structures....Small structures in coal mine working face is one of the main hidden dangers of safe and effi cient production in coal mine.Currently,seismic exploration is often used as the main method for detecting such structures.However,limited by the accuracy of seismic data processing and interpretation,the interpreted location of small structures is often deviated.Ground-penetrating radar(GPR)can detect small structures accurately,but the exploration depth is shallow.The combination of the two methods can improve the exploration accuracy of small structures in coal mine.Aiming at the 1226#working face of Shuguang coal mine,we propose a method of seismic-attributes based small-structure prediction error correction using GPR data.First,we extract the coherence,curvature,and dip attributes from seismic data,that are sensitive to small structures,then by considering factors such as the eff ective detection range of GPR and detection environment,we select two structures from the prediction results of seismic attributes for GPR detection.Finally,based on the relationship between the positions of small structures predicted by the two methods,we use statistical methods to determine the overall off set distance and azimuth of the small structures in the entire study area and use the results as a standard for correcting each structure position.The results show that the GPR data can be used to correct the horizontal position errors of small structures predicted by seismic attribute analysis.The accuracy of the prediction results is greatly improved,with the error controlled within 5 m and reduced by more than 80%.Therefore,the feasibility of the method proposed in this study is verified.展开更多
By examining field outcrops, drilling cores and seismic data, it is concluded that the Middle and Late Permian “Emeishan basalts” in Western Sichuan Basin were developed in two large eruption cycles, and the two set...By examining field outcrops, drilling cores and seismic data, it is concluded that the Middle and Late Permian “Emeishan basalts” in Western Sichuan Basin were developed in two large eruption cycles, and the two sets of igneous rocks are in unconformable contact. The lower cycle is dominated by overflow volcanic rocks;while the upper cycle made up of pyroclastic flow volcanic breccia and pyroclastic lava is typical explosive facies accumulation. With high-quality micro-dissolution pores and ultra-fine dissolution pores, the upper cycle is a set of high-quality porous reservoir. Based on strong heterogeneity and great differences of pyroclastic flow subfacies from surrounding rocks in lithology and physical properties, the volcanic facies and volcanic edifices in Western Sichuan were effectively predicted and characterized by using seismic attribute analysis method and instantaneous amplitude and instantaneous frequency coherence analysis. The pyroclastic flow volcanic rocks are widely distributed in the Jianyang area. Centering around wells YT1, TF2 and TF8, the volcanic rocks in Jianyang area had 3edifice groups and an area of about 500 km^(2), which is the most favorable area for oil and gas exploration in volcanic rocks.展开更多
Electrofacies are used to determine reservoir rock properties,especially permeability,to simulate fluid flow in porous media.These are determined based on classification of similar logs among different groups of loggi...Electrofacies are used to determine reservoir rock properties,especially permeability,to simulate fluid flow in porous media.These are determined based on classification of similar logs among different groups of logging data.Data classification is accomplished by different statistical analysis such as principal component analysis,cluster analysis and differential analysis.The aim of this study is to predict 3D FZI(flow zone index)and Electrofacies(EFACT)volumes from a large volume of 3D seismic data.This study is divided into two parts.In the first part of the study,in order to make the EFACT model,nuclear magnetic resonance(NMR)log parameters were employed for developing an Electrofacies diagram based on pore size distribution and porosity variations.Then,a graph-based clustering method,known as multi resolution graph-based clustering(MRGC),was employed to classify and obtain the optimum number of Electrofacies.Seismic attribute analysis was then applied to model each relaxation group in order to build the initial 3D model which was used to reach the final model by applying Probabilistic Neural Network(PNN).In the second part of the study,the FZI 3D model was created by multi attributes technique.Then,this model was improved by three different artificial intelligence systems including PNN,multilayer feed-forward network(MLFN)and radial basis function network(RBFN).Finally,models of FZI and EFACT were compared.Results obtained from this study revealed that the two models are in good agreement and PNN method is successful in modeling FZI and EFACT from 3D seismic data for which no Stoneley data or NMR log data are available.Moreover,they may be used to detect hydrocarbon-bearing zones and locate the exact place for producing wells for the future development plans.In addition,the result provides a geologically realistic spatial FZI and reservoir facies distribution which helps to understand the subsurface reservoirs heterogeneities in the study area.展开更多
基金Supported by the Natural Science Foundation of Shandong Province(No.ZR2022MD074)the Laboratory for Marine Mineral Resources+3 种基金Qingdao National Laboratory for Marine Science and Technology(No.MMRKF201810)the National Natural Science Foundation of China(No.41606077)the National Key R&D Program of China:HighPrecision Characterization Technology of Gas Hydrate Reservoir(No.2017YFC0307406-03)supported by the Shandong Province Taishan Scholar Construction Project。
文摘Submarine seep plumes are a natural phenomenon in which different types of gases migrate through deep or shallow subsurface sediments and leak into seawater in pressure gradient.When detected using acoustic data,the leaked gases frequently exhibit a flame-like structure.We numerically modelled the relationship between the seismic response characteristic and bubble volume fraction to establish the bubble volume fraction in the submarine seep plume.Results show that our models are able to invert and predict the bubble volume fraction from field seismic oceanography data,by which synthetic seismic sections in different dominant frequencies could be numerically simulated,seismic attribute sections(e.g.,instantaneous amplitude,instantaneous frequency,and instantaneous phase)extracted,and the correlation between the seismic attributes and bubble volume fraction be quantitatively determined with functional equations.The instantaneous amplitude is positively correlated with bubble volume fraction,while the instantaneous frequency and bubble volume fraction are negatively correlated.In addition,information entropy is introduced as a proxy to quantify the relationship between the instantaneous phase and bubble volume fraction.As the bubble volume fraction increases,the information entropy of the instantaneous phase increases rapidly at the beginning,followed by a slight upward trend,and finally stabilizes.Therefore,under optimal noise conditions,the bubble volume fraction of submarine seep plumes can be inverted and predicted based on seismic response characteristics in terms of seismic attributes.
基金This study work is supported by the Directly Managed Scientifi c Research Project of Huainan Mining(Group)Co.Ltd.(No.HNKYJTJS(2018)181),the Major Project of Shaanxi Coal and Chemical Industry Group Co.Ltd.(No.2018SMHKJ-A-J-03),China Energy Investment Corporation 2030 Pilot Project(No.GJNY2030XDXM-19-03.2),State Key Laboratory of Coal Resources and Safe Mining,China University of Mining and Technology(Beijing).I also would like to thank the editorial department and the review experts for their valuable comments and suggestions,and thank the Compagnie Générale de Géophysique(CGG)for the Jason software support.
文摘Small structures in coal mine working face is one of the main hidden dangers of safe and effi cient production in coal mine.Currently,seismic exploration is often used as the main method for detecting such structures.However,limited by the accuracy of seismic data processing and interpretation,the interpreted location of small structures is often deviated.Ground-penetrating radar(GPR)can detect small structures accurately,but the exploration depth is shallow.The combination of the two methods can improve the exploration accuracy of small structures in coal mine.Aiming at the 1226#working face of Shuguang coal mine,we propose a method of seismic-attributes based small-structure prediction error correction using GPR data.First,we extract the coherence,curvature,and dip attributes from seismic data,that are sensitive to small structures,then by considering factors such as the eff ective detection range of GPR and detection environment,we select two structures from the prediction results of seismic attributes for GPR detection.Finally,based on the relationship between the positions of small structures predicted by the two methods,we use statistical methods to determine the overall off set distance and azimuth of the small structures in the entire study area and use the results as a standard for correcting each structure position.The results show that the GPR data can be used to correct the horizontal position errors of small structures predicted by seismic attribute analysis.The accuracy of the prediction results is greatly improved,with the error controlled within 5 m and reduced by more than 80%.Therefore,the feasibility of the method proposed in this study is verified.
基金Supported by the Scientific and Technological Major Project of the Southwest Oil and Gas Field Company (2019ZD01-03)。
文摘By examining field outcrops, drilling cores and seismic data, it is concluded that the Middle and Late Permian “Emeishan basalts” in Western Sichuan Basin were developed in two large eruption cycles, and the two sets of igneous rocks are in unconformable contact. The lower cycle is dominated by overflow volcanic rocks;while the upper cycle made up of pyroclastic flow volcanic breccia and pyroclastic lava is typical explosive facies accumulation. With high-quality micro-dissolution pores and ultra-fine dissolution pores, the upper cycle is a set of high-quality porous reservoir. Based on strong heterogeneity and great differences of pyroclastic flow subfacies from surrounding rocks in lithology and physical properties, the volcanic facies and volcanic edifices in Western Sichuan were effectively predicted and characterized by using seismic attribute analysis method and instantaneous amplitude and instantaneous frequency coherence analysis. The pyroclastic flow volcanic rocks are widely distributed in the Jianyang area. Centering around wells YT1, TF2 and TF8, the volcanic rocks in Jianyang area had 3edifice groups and an area of about 500 km^(2), which is the most favorable area for oil and gas exploration in volcanic rocks.
文摘Electrofacies are used to determine reservoir rock properties,especially permeability,to simulate fluid flow in porous media.These are determined based on classification of similar logs among different groups of logging data.Data classification is accomplished by different statistical analysis such as principal component analysis,cluster analysis and differential analysis.The aim of this study is to predict 3D FZI(flow zone index)and Electrofacies(EFACT)volumes from a large volume of 3D seismic data.This study is divided into two parts.In the first part of the study,in order to make the EFACT model,nuclear magnetic resonance(NMR)log parameters were employed for developing an Electrofacies diagram based on pore size distribution and porosity variations.Then,a graph-based clustering method,known as multi resolution graph-based clustering(MRGC),was employed to classify and obtain the optimum number of Electrofacies.Seismic attribute analysis was then applied to model each relaxation group in order to build the initial 3D model which was used to reach the final model by applying Probabilistic Neural Network(PNN).In the second part of the study,the FZI 3D model was created by multi attributes technique.Then,this model was improved by three different artificial intelligence systems including PNN,multilayer feed-forward network(MLFN)and radial basis function network(RBFN).Finally,models of FZI and EFACT were compared.Results obtained from this study revealed that the two models are in good agreement and PNN method is successful in modeling FZI and EFACT from 3D seismic data for which no Stoneley data or NMR log data are available.Moreover,they may be used to detect hydrocarbon-bearing zones and locate the exact place for producing wells for the future development plans.In addition,the result provides a geologically realistic spatial FZI and reservoir facies distribution which helps to understand the subsurface reservoirs heterogeneities in the study area.
