The seismic data of the Laoshan Uplift in the South Yellow Sea Basin reveal a low signal-tonoise ratio and low refl ection signal energy in the deep Mesozoic–Paleozoic strata.The main reason is that the Mesozoic-Pale...The seismic data of the Laoshan Uplift in the South Yellow Sea Basin reveal a low signal-tonoise ratio and low refl ection signal energy in the deep Mesozoic–Paleozoic strata.The main reason is that the Mesozoic-Paleozoic marine carbonate rock strata are directly covered by the Cenozoic terrestrial clastic rock strata,which form a strong shielding layer.To obtain the reflection signals of the strata below the strong shielding layer,a one-way wave equation bidirectional illumination analysis of the main observation system parameters was conducted by analyzing the mechanism of the strong shielding layer.Low-frequency seismic sources are assumed to have a high illumination intensity on the reflection layer below the strong shielding layer.Accordingly,optimized acquisition parameter suggestions were proposed,and reacquisition was performed at the existing survey line locations in the Laoshan Uplift area.The imaging of the newly acquired data in the middle and deep layers was drastically improved.It revealed the unconformity between the Sinian and Cambrian under the strong shielding layer.The study yielded new insights into the tectonic and sedimentary evolution of the Lower Paleozoic in the South Yellow Sea.展开更多
Advanced geological prediction is a crucial means to ensure safety and efficiency in tunnel construction.However,diff erent advanced geological forecasting methods have their own limitations,resulting in poor detectio...Advanced geological prediction is a crucial means to ensure safety and efficiency in tunnel construction.However,diff erent advanced geological forecasting methods have their own limitations,resulting in poor detection accuracy.Using multiple methods to carry out a comprehensive evaluation can eff ectively improve the accuracy of advanced geological prediction results.In this study,geological information is combined with the detection results of geophysical methods,including transient electromagnetic,induced polarization,and tunnel seismic prediction,to establish a comprehensive analysis method of adverse geology.First,the possible main adverse geological problems are determined according to the geological information.Subsequently,various physical parameters of the rock mass in front of the tunnel face can then be derived on the basis of multisource geophysical data.Finally,based on the analysis results of geological information,the multisource data fusion algorithm is used to determine the type,location,and scale of adverse geology.The advanced geological prediction results that can provide eff ective guidance for tunnel construction can then be obtained.展开更多
Since April 2002,the Gravity Recovery and Climate Experiment Satellite(GRACE)has provided monthly total water storage anomalies(TWSAs)on a global scale.However,these TWSAs are discontinuous because some GRACE observat...Since April 2002,the Gravity Recovery and Climate Experiment Satellite(GRACE)has provided monthly total water storage anomalies(TWSAs)on a global scale.However,these TWSAs are discontinuous because some GRACE observation data are missing.This study presents a combined machine learning-based modeling algorithm without hydrological model data.The TWSA time-series data for 11 large regions worldwide were divided into training and test sets.Autoregressive integrated moving average(ARIMA),long short-term memory(LSTM),and an ARIMA-LSTM combined model were used.The model predictions were compared with GRACE observations,and the model accuracy was evaluated using fi ve metrics:the Nash-Sutcliff e effi ciency coeffi cient(NSE),Pearson correlation coeffi cient(CC),root mean square error(RMSE),normalized RMSE(NRMSE),and mean absolute percentage error.The results show that at the basin scale,the mean CC,NSE,and NRMSE for the ARIMA-LSTM model were 0.93,0.83,and 0.12,respectively.At the grid scale,this study compared the spatial distribution and cumulative distribution function curves of the metrics in the Amazon and Volga River basins.The ARIMA-LSTM model had mean CC and NSE values of 0.89 and 0.61 and 0.92 and 0.61 in the Amazon and Volga River basins,respectively,which are superior to those of the ARIMA model(0.86 and 0.48 and 0.88 and 0.46,respectively)and the LSTM model(0.80 and 0.41 and 0.89 and 0.31,respectively).In the ARIMA-LSTM model,the proportions of grid cells with NSE>0.50 for the two basins were 63.3%and 80.8%,while they were 54.3%and 51.3%in the ARIMA model and 53.7%and 43.2%in the LSTM model.The ARIMA-LSTM model significantly improved the NSE values of the predictions while guaranteeing high CC values in the GRACE data reconstruction at both scales,which can aid in fi lling in discontinuous data in temporal gravity fi eld models..展开更多
Enhancing the mining speed of a working face has become the primary approach to achieve high production and efficiency in coal mines,thereby further improving the production capacity.However,the problem of rock bursts...Enhancing the mining speed of a working face has become the primary approach to achieve high production and efficiency in coal mines,thereby further improving the production capacity.However,the problem of rock bursts resulting from this approach has become increasingly serious.Therefore,to implement coal mine safety and efficient extraction,the impact of deformation pressure caused by different mining speeds should be considered,and a reasonable mining speed of the working face should be determined.The influence of mining speed on overlying rock breaking in the stope is analyzed by establishing a key layer block rotation and subsidence model.Results show that with the increasing mining speed,the compression amount of gangue in the goaf decreases,and the rotation and subsidence amount of rock block B above goaf decreases,forcing the rotation and subsidence amount of rock block A above roadway to increase.Consequently,the contact mode between rock block A and rock block B changes from line contact to point contact,and the horizontal thrust and shear force between blocks increase.The increase in rotation and subsidence of rock block A intensifies the compression degree of coal and rock mass below the key layer,thereby increasing the stress concentration degree of coal and rock mass as well as the total energy accumulation.In addition,due to the insufficient compression of gangue in the goaf,the bending and subsidence space of the far-field key layer are limited,the length of the suspended roof increases,and the influence range of mining stress and the energy accumulation range expand.Numerical test results and underground microseismic monitoring results verify the correlation between mining speed and stope energy,and high-energy events generally appear 1-2 d after the change in mining speed.On this basis,the statistical principle confirms that the maximum mining speed of the working face at 6 m/d is reasonable.展开更多
Ground source heat pump systems demonstrate significant potential for northern rural heating applications;however,the effectiveness of these systems is often limited by challenging geological conditions.For instance,i...Ground source heat pump systems demonstrate significant potential for northern rural heating applications;however,the effectiveness of these systems is often limited by challenging geological conditions.For instance,in certain regions,the installation of buried pipes for heat exchangers may be complicated,and these pipes may not always serve as efficient low-temperature heat sources for the heat pumps of the system.To address this issue,the current study explored the use of solar-energy-collecting equipment to supplement buried pipes.In this design,both solar energy and geothermal energy provide low-temperature heat to the heat pump.First,a simulation model of a solar‒ground source heat pump coupling system was established using TRNSYS.The accuracy of this model was validated through experiments and simulations on various system configurations,including varying numbers of buried pipes,different areas of solar collectors,and varying volumes of water tanks.The simulations examined the coupling characteristics of these components and their influence on system performance.The results revealed that the operating parameters of the system remained consistent across the following configurations:three buried pipes,burial depth of 20 m,collector area of 6 m^(2),and water tank volume of 0.5 m^(3);four buried pipes,burial depth of 20 m,collector area of 3 m^(2),and water tank volume of 0.5 m^(3);and five buried pipes with a burial depth of 20 m.Furthermore,the heat collection capacity of the solar collectors spanning an area of 3 m^(2)was found to be equivalent to that of one buried pipe.Moreover,the findings revealed that the solar‒ground source heat pump coupling system demonstrated a lower annual cumulative energy consumption compared to the ground source heat pump system,presenting a reduction of 5.31%compared to the energy consumption of the latter.展开更多
The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic techno...The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic technology,plays a pivotal role in ensuring mine safety by enabling real-time identifi cation and accurate classification of vibration signals such as microseismic signals,blasting signals,and noise.These classifications are critical for improving the efficacy of ground pressure monitoring systems,conducting stability analyses of deep rock masses,and implementing timely and precise roadway support measures.Such eff orts are essential for mitigating ground pressure disasters and ensuring safe mining operations.This study proposes an artificial intelligence-based automatic classification network model for mine vibration signals.Based on conventional convolutional neural networks,the proposed model further incorporates long short-term memory(LSTM)networks and attention mechanisms.The LSTM component eff ectively captures temporal correlations in time-series mining vibration data,while the attention mechanism enhances the models’ability to focus on critical features within the data.To validate the eff ectiveness of our proposed model,a dataset comprising 480,526 waveform records collected in 2022 by the microseismic monitoring system at Guangxi Shanhu Tungsten Mine was used for training,validation,and testing purposes.Results demonstrate that the proposed artifi cial intelligence-based classifi cation method achieves a higher recognition accuracy of 92.21%,significantly outperforming traditional manual classification methods.The proposed model represents a signifi cant advancement in ground pressure monitoring and disaster mitigation.展开更多
This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was...This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.展开更多
Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI present...Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.展开更多
Evaluation of water richness in sandstone is an important research topic in the prevention and control of mine water disasters,and the water richness in sandstone is closely related to its porosity.The refl ection sei...Evaluation of water richness in sandstone is an important research topic in the prevention and control of mine water disasters,and the water richness in sandstone is closely related to its porosity.The refl ection seismic exploration data have high-density spatial sampling information,which provides an important data basis for the prediction of sandstone porosity in coal seam roofs by using refl ection seismic data.First,the basic principles of the variational mode decomposition(VMD)method and the random forest method are introduced.Then,the geological model of coal seam roof sandstone is constructed,seismic forward modeling is conducted,and random noise is added.The decomposition eff ects of the empirical mode decomposition(EMD)method and VMD method on noisy signals are compared and analyzed.The test results show that the firstorder intrinsic mode functions(IMF1)and IMF2 decomposed by the VMD method contain the main eff ective components of seismic signals.A prediction process of sandstone porosity in coal seam roofs based on the combination of VMD and random forest method is proposed.The feasibility and eff ectiveness of the method are verified by trial calculation in the porosity prediction of model data.Taking the actual coalfield refl ection seismic data as an example,the sandstone porosity of the 8 coal seam roof is predicted.The application results show the potential application value of the new porosity prediction method proposed in this study.This method has important theoretical guiding significance for evaluating water richness in coal seam roof sandstone and the prevention and control of mine water disasters.展开更多
When the expressway crosses the goafs inevitably,the design is generally to build the road on coal pillars as much as possible.However,the existing coal pillars are often unable to meet relevant requirements of highwa...When the expressway crosses the goafs inevitably,the design is generally to build the road on coal pillars as much as possible.However,the existing coal pillars are often unable to meet relevant requirements of highway construction.Combining three-dimensional physical model tests,numerical simulations and field monitoring,with the Urumqi East Second Ring Road passing through acute inclined goafs as a background,the deformation and failure mechanism of the overlying rock and coal pillars in acute inclined goafs under expressway load were studied.And in accordance with construction requirements of subgrade,comprehensive consideration of the deformation and instability mechanism of acute inclined goafs,the treatment measures and suggestions for this type of geological disasters were put forward.The research results confirmed the rationality of coal pillars in acute inclined goafs under the expressway through grouting.According to the ratio of diff erent overlying rock thickness to coal pillar height,the change trend and value of the required grouting range were summarized,which can provide reference for similar projects.展开更多
With the continued expansion of oil and gas exploration,both in the eastern and western regions,the quality of seismic acquisition has become a key factor in oil and gas exploration in complex areas.However,convention...With the continued expansion of oil and gas exploration,both in the eastern and western regions,the quality of seismic acquisition has become a key factor in oil and gas exploration in complex areas.However,conventional seismic acquisition methods cannot efficiently avoid challenging acquisition locations and produce high-quality seismic data.In this regard,based on the curvelet transform,this paper proposes an irregular seismic acquisition method,which utilizes the high-precision characteristics of the curvelet transform and simulated annealing algorithm to establish a method for the evaluation of the coherence of irregular sampling matrices and design of observation systems.The method was verified using forward simulation and actual acquisition data.The results suggest the superior quality of seismic data gathered in complicated areas through this method over those acquired using traditional methods,which can provide technical guidance for the design of observation systems in complex areas.展开更多
This study discusses the challenges in logging the evaluation of low-resistivity oil reservoirs,especially the difficult problems involving their saturation calculation.A correction method for equivalent water conduct...This study discusses the challenges in logging the evaluation of low-resistivity oil reservoirs,especially the difficult problems involving their saturation calculation.A correction method for equivalent water conductivity is proposed,given the high conductivity caused by small amounts of water distributed in a network within the low-resistivity reservoir,which mimics the eff ects of high water saturation.This approach signifi cantly improves the accuracy of hydrocarbon saturation calculations in these low-resistivity reservoirs.The corrected hydrocarbon saturation values highly matched the porosity and are consistent with experimental results.This study also establishes a discrimination process to determine whether corrections are required,verifying the eff ectiveness and accuracy of the method through an application example.展开更多
Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of th...Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.展开更多
As geological exploration conditions become increasingly complex, meeting the requirements of precise geological exploration necessitates the development of a controlled-source audio magnetotelluric (CSAMT) inversion ...As geological exploration conditions become increasingly complex, meeting the requirements of precise geological exploration necessitates the development of a controlled-source audio magnetotelluric (CSAMT) inversion method that considers anisotropy to improve the effectiveness of inversion accuracy and interpretation accuracy of data. This study is based on the 3D fi nite-diff erence forward modeling of axis anisotropy using the reciprocity theorem to calculate the Jacobian matrix by applying the search method to automatically search for the Lagrange operator. The aim is to establish inversion iteration equations to achieve the axis anisotropic Occam's 3D inversion of tensor CSAMT in data space. Further, we obtain an underground axis anisotropic 3D geoelectric model by inverting the impedance data of tensor CSAMT. Two synthetic data examples show that using the isotropic tensor CSAMT algorithm to directly invert data in anisotropic media can generate false anomalies, leading to incorrect geological interpretations. Meanwhile, the proposed anisotropic inversion algorithm can eff ectively improve the accuracy of data inversion in anisotropic media. Further, the inversion examples verify the eff ectiveness and stability of the algorithm.展开更多
Low-frequency vibroseis acquisition has become a routine operation in land seismic surveys,given the advantages of low-frequency signals in characterizing geological structures and enhancing the imaging of deep explor...Low-frequency vibroseis acquisition has become a routine operation in land seismic surveys,given the advantages of low-frequency signals in characterizing geological structures and enhancing the imaging of deep exploration targets.The two key points of low-frequency sweep design techniques include controlling the distortion and improving the output energy during the low-frequency stage.However,the vibrators are limited by the maximum fl ow provided by the hydraulic systems at the low-frequency stage,causing difficulty in satisfying exploration energy requirements.Initially,a theoretical analysis of the low-frequency acquisition performance of vibrators is conducted.A theoretical maximum output force below 10 Hz is obtained by guiding through theoretical formulas and combining actual vibrator parameters.Then,the signal is optimized according to the surface characteristics of the operation area.Finally,detailed application quality control and operational procedures are established.The new low-frequency sweep design method has overcome the maximum flow limitations of the hydraulic system,increased the low-frequency energy,and achieved broadband acquisition.The designed signal has been tested and applied on various types of ground surfaces in the Middle East desert region,yielding good performance.The proposed low-frequency sweep design method holds considerable value for the application of conventional vibroseis in low-frequency acquisition.展开更多
Fractures are critical control factors in volcanic reservoirs.Thus,studies on the prediction and distribution patterns of volcanic fractures are crucial for oil and gas exploration and development.Although considerabl...Fractures are critical control factors in volcanic reservoirs.Thus,studies on the prediction and distribution patterns of volcanic fractures are crucial for oil and gas exploration and development.Although considerable research has been conducted on volcanic fractures,targeted research on the spatial distribution patterns of fracture development remains limited in the literature.Two eruption modes,subaqueous eruption and subaerial eruption,have been identified in the Chaganhua subsag of the Songliao Basin,where gas resources have been discovered.Studying the differences in volcanic fracture development is highly important for understanding the law of oil and gas enrichment in volcanic reservoirs.On the basis of thin sections,cores,electrical imaging logs,and other data obtained from boreholes of subaqueous volcanic eruptions in the Songnan fault depression,we describe the characteristics of drilled fractures in detail and use 3D seismic data to extract intrinsic coherence,ant bodies,and Fourier series to extend the azimuthal anisotropic P-wave reflection coefficient,thereby predicting the spatial distribution of fractures at different scales.Moreover,through statistical analysis of quantitative evaluation indicators,such as fracture density and fracture development rate in different drilling wells,combined with the spatial distribution patterns of the predicted fractures,we compared and analyzed the relationships between the distributions of fractures and volcanic institutions,lithology,and volcanic facies.Results indicate that fractures are more developed in volcanic edifices located closer to faults.Considerable differences in fracture development are observed among lithofacies of volcanic rocks.Near faults,effusion facies,explosive facies and external clastic pyroclastic sedimentary subfacies are favorable for fracture development.This research provides a reference for investigating volcanic rock fractures of the same origins worldwide.展开更多
In this paper,we investigate the method of compensating LTS SQUID Gradiometer Systems data.By matching the attitude changes of the pod in fl ight to the anomalies of the magnetic measurement data,we find that the yaw ...In this paper,we investigate the method of compensating LTS SQUID Gradiometer Systems data.By matching the attitude changes of the pod in fl ight to the anomalies of the magnetic measurement data,we find that the yaw attitude changes most dramatically and corresponds best to the magnetic data anomaly interval.Based on this finding,we solved the compensation model using least squares fitting and Huber's parametric fitting.By comparison,we found that the Huber parametric fit not only eliminates the interference introduced by attitude changes but also retains richer anomaly source information and therefore obtains a higher signal-to-noise ratio.The experimental results show that the quality of the magnetometry data obtained by using the compensation method proposed in this paper has been significantly improved,and the mean value of its improvement ratio can reach 118.93.展开更多
The deep learning algorithm,which has been increasingly applied in the field of petroleum geophysical prospecting,has achieved good results in improving efficiency and accuracy based on test applications.To play a gre...The deep learning algorithm,which has been increasingly applied in the field of petroleum geophysical prospecting,has achieved good results in improving efficiency and accuracy based on test applications.To play a greater role in actual production,these algorithm modules must be integrated into software systems and used more often in actual production projects.Deep learning frameworks,such as TensorFlow and PyTorch,basically take Python as the core architecture,while the application program mainly uses Java,C#,and other programming languages.During integration,the seismic data read by the Java and C#data interfaces must be transferred to the Python main program module.The data exchange methods between Java,C#,and Python include shared memory,shared directory,and so on.However,these methods have the disadvantages of low transmission efficiency and unsuitability for asynchronous networks.Considering the large volume of seismic data and the need for network support for deep learning,this paper proposes a method of transmitting seismic data based on Socket.By maximizing Socket’s cross-network and efficient longdistance transmission,this approach solves the problem of inefficient transmission of underlying data while integrating the deep learning algorithm module into a software system.Furthermore,the actual production application shows that this method effectively solves the shortage of data transmission in shared memory,shared directory,and other modes while simultaneously improving the transmission efficiency of massive seismic data across modules at the bottom of the software.展开更多
The Jurassic Lianggaoshan Formation in eastern Sichuan Basin is a key target for shale oil exploration.It faces challenges in three-pressure prediction due to complex structural and sedimentary interactions,as well as...The Jurassic Lianggaoshan Formation in eastern Sichuan Basin is a key target for shale oil exploration.It faces challenges in three-pressure prediction due to complex structural and sedimentary interactions,as well as strong reservoir anisotropy.These issues often lead to wellbore instability and gas logging anomalies during drilling.This study presents an integrated workflow that combines residual moveout correction using correlation-based dynamic time warping(CDTW),high-resolution seismic waveform indication inversion,and three-pressure prediction of jointing well-seismic data.Applied to the LT1 well block,the workflow effectively corrects anisotropic residual moveout in image gathers,leading to a signal strength increase of over 10%in frequency bands above 30 Hz and enhancing event continuity.High-resolution rock mechanical parameters are obtained through seismic waveform inversion and regional calibration,enabling the prediction of three-dimensional pore pressure,collapse pressure and fracture pressure.The results are consistent with actual drilling gas shows and core data,confirming the method's accuracy and supporting mud weight planning and wellbore stability efforts.This cost-effective and technically robust approach proves highly reliable in complex environments with significant heterogeneity and anisotropy,assisting drilling decisions and risk management in eastern Sichuan and similar challenging geological settings.展开更多
The geological disasters such as collapse,mud bursting and water gushing often occur during tunnel construction.Thus,it is of great significance to detect the hidden geological disasters ahead of the tunnel face.The a...The geological disasters such as collapse,mud bursting and water gushing often occur during tunnel construction.Thus,it is of great significance to detect the hidden geological disasters ahead of the tunnel face.The audio magnetotelluric(AMT)was applied for the advanced detectionstudy during the boring process of the Tianheshan tunnel in the Taihang Mountains.Three AMT profiles were deployed above the tunnel,and the data obtained in the field were analysed in terms of electrical principal axes.From shallow to deep,the direction of the geoelectric strike angle changes,generally between 30°and 60°NE,which is consistent with similar to the direction of the Taihangshan Uplift,and the data show some 3D characteristics.Two-dimensional(2D)and three-dimensional(3D)inversion methods were adopted to jointly study the subsurface structural information,and the resistivity model was geophysically and geologically interpreted.Two sets of low resistance anomalies were found,and it was hypothesised that the near-erect low-resistivity anomalies in the east might be a fragmentation zone,while the low resistance anomalies in the west,which are inclined to the westward,might be a tectonic structure or fragmentation zone related to the regional major fault,and the results of the tunnelling confirmed the reliability of the inversion interpretation.The 3D inversion can fully reflect the development scale and morphological changes of the fracture zone,and the inversion model is more reliable.Finally,it is concluded that the audio magnetotelluric method,which adopts advanced acquisition,processing and inversion interpretation techniques,is an effective means of over-detection of tunnels.展开更多
基金“High precision prestack reverse time depth migration imaging of long array seismic data in the East China Sea Shelf Basin”of the National Natural Science Foundation of China(No.42106207)“Seismic acquisition technology for deep strata under strong shielding layers in the sea and rugged seabed”of Laoshan Laboratory Science and Technology Innovation Project(No.LSKJ202203404)“Research on the compensation methods of the middledeep weak seismic reflections in the South Yellow Sea based on multi-resolution HHT time-frequency analysis”of the National Natural Science Foundation of China(No.42106208).
文摘The seismic data of the Laoshan Uplift in the South Yellow Sea Basin reveal a low signal-tonoise ratio and low refl ection signal energy in the deep Mesozoic–Paleozoic strata.The main reason is that the Mesozoic-Paleozoic marine carbonate rock strata are directly covered by the Cenozoic terrestrial clastic rock strata,which form a strong shielding layer.To obtain the reflection signals of the strata below the strong shielding layer,a one-way wave equation bidirectional illumination analysis of the main observation system parameters was conducted by analyzing the mechanism of the strong shielding layer.Low-frequency seismic sources are assumed to have a high illumination intensity on the reflection layer below the strong shielding layer.Accordingly,optimized acquisition parameter suggestions were proposed,and reacquisition was performed at the existing survey line locations in the Laoshan Uplift area.The imaging of the newly acquired data in the middle and deep layers was drastically improved.It revealed the unconformity between the Sinian and Cambrian under the strong shielding layer.The study yielded new insights into the tectonic and sedimentary evolution of the Lower Paleozoic in the South Yellow Sea.
基金National Natural Science Foundation of China(grant numbers 42293351,41877239,51422904 and 51379112).
文摘Advanced geological prediction is a crucial means to ensure safety and efficiency in tunnel construction.However,diff erent advanced geological forecasting methods have their own limitations,resulting in poor detection accuracy.Using multiple methods to carry out a comprehensive evaluation can eff ectively improve the accuracy of advanced geological prediction results.In this study,geological information is combined with the detection results of geophysical methods,including transient electromagnetic,induced polarization,and tunnel seismic prediction,to establish a comprehensive analysis method of adverse geology.First,the possible main adverse geological problems are determined according to the geological information.Subsequently,various physical parameters of the rock mass in front of the tunnel face can then be derived on the basis of multisource geophysical data.Finally,based on the analysis results of geological information,the multisource data fusion algorithm is used to determine the type,location,and scale of adverse geology.The advanced geological prediction results that can provide eff ective guidance for tunnel construction can then be obtained.
基金financially supported by The National Natural Science Foundation of China (42374004)the Open Fund of Hubei Luojia Laboratory (220100045)the Natural Science Foundation of Sichuan Province (2022NSFSC1047)。
文摘Since April 2002,the Gravity Recovery and Climate Experiment Satellite(GRACE)has provided monthly total water storage anomalies(TWSAs)on a global scale.However,these TWSAs are discontinuous because some GRACE observation data are missing.This study presents a combined machine learning-based modeling algorithm without hydrological model data.The TWSA time-series data for 11 large regions worldwide were divided into training and test sets.Autoregressive integrated moving average(ARIMA),long short-term memory(LSTM),and an ARIMA-LSTM combined model were used.The model predictions were compared with GRACE observations,and the model accuracy was evaluated using fi ve metrics:the Nash-Sutcliff e effi ciency coeffi cient(NSE),Pearson correlation coeffi cient(CC),root mean square error(RMSE),normalized RMSE(NRMSE),and mean absolute percentage error.The results show that at the basin scale,the mean CC,NSE,and NRMSE for the ARIMA-LSTM model were 0.93,0.83,and 0.12,respectively.At the grid scale,this study compared the spatial distribution and cumulative distribution function curves of the metrics in the Amazon and Volga River basins.The ARIMA-LSTM model had mean CC and NSE values of 0.89 and 0.61 and 0.92 and 0.61 in the Amazon and Volga River basins,respectively,which are superior to those of the ARIMA model(0.86 and 0.48 and 0.88 and 0.46,respectively)and the LSTM model(0.80 and 0.41 and 0.89 and 0.31,respectively).In the ARIMA-LSTM model,the proportions of grid cells with NSE>0.50 for the two basins were 63.3%and 80.8%,while they were 54.3%and 51.3%in the ARIMA model and 53.7%and 43.2%in the LSTM model.The ARIMA-LSTM model significantly improved the NSE values of the predictions while guaranteeing high CC values in the GRACE data reconstruction at both scales,which can aid in fi lling in discontinuous data in temporal gravity fi eld models..
基金supported by Technology Innovation Fund of China Coal Research Institute(2022CX-I-04)Science and Technology Innovation Venture Capital Project of China Coal Technology Engineering Group(2020-2-TD-CXY005)。
文摘Enhancing the mining speed of a working face has become the primary approach to achieve high production and efficiency in coal mines,thereby further improving the production capacity.However,the problem of rock bursts resulting from this approach has become increasingly serious.Therefore,to implement coal mine safety and efficient extraction,the impact of deformation pressure caused by different mining speeds should be considered,and a reasonable mining speed of the working face should be determined.The influence of mining speed on overlying rock breaking in the stope is analyzed by establishing a key layer block rotation and subsidence model.Results show that with the increasing mining speed,the compression amount of gangue in the goaf decreases,and the rotation and subsidence amount of rock block B above goaf decreases,forcing the rotation and subsidence amount of rock block A above roadway to increase.Consequently,the contact mode between rock block A and rock block B changes from line contact to point contact,and the horizontal thrust and shear force between blocks increase.The increase in rotation and subsidence of rock block A intensifies the compression degree of coal and rock mass below the key layer,thereby increasing the stress concentration degree of coal and rock mass as well as the total energy accumulation.In addition,due to the insufficient compression of gangue in the goaf,the bending and subsidence space of the far-field key layer are limited,the length of the suspended roof increases,and the influence range of mining stress and the energy accumulation range expand.Numerical test results and underground microseismic monitoring results verify the correlation between mining speed and stope energy,and high-energy events generally appear 1-2 d after the change in mining speed.On this basis,the statistical principle confirms that the maximum mining speed of the working face at 6 m/d is reasonable.
基金supported by 2024 Central Guidance Local Science and Technology Development Fund Project"Study on the mechanism and evaluation method of thermal pollution in water bodies,as well as research on thermal carrying capacity".(Grant 246Z4506G)Key Research and Development Project in Hebei Province:"Key Technologies and Equipment Research and Demonstration of Multiple Energy Complementary(Electricity,Heat,Cold System)for Solar Energy,Geothermal Energy,Phase Change Energy"(Grant 236Z4310G)the Hebei Academy of Sciences Key Research and Development Program"Research on Heat Transfer Mechanisms and Efficient Applications of Intermediate and Deep Geothermal Energy"(22702)。
文摘Ground source heat pump systems demonstrate significant potential for northern rural heating applications;however,the effectiveness of these systems is often limited by challenging geological conditions.For instance,in certain regions,the installation of buried pipes for heat exchangers may be complicated,and these pipes may not always serve as efficient low-temperature heat sources for the heat pumps of the system.To address this issue,the current study explored the use of solar-energy-collecting equipment to supplement buried pipes.In this design,both solar energy and geothermal energy provide low-temperature heat to the heat pump.First,a simulation model of a solar‒ground source heat pump coupling system was established using TRNSYS.The accuracy of this model was validated through experiments and simulations on various system configurations,including varying numbers of buried pipes,different areas of solar collectors,and varying volumes of water tanks.The simulations examined the coupling characteristics of these components and their influence on system performance.The results revealed that the operating parameters of the system remained consistent across the following configurations:three buried pipes,burial depth of 20 m,collector area of 6 m^(2),and water tank volume of 0.5 m^(3);four buried pipes,burial depth of 20 m,collector area of 3 m^(2),and water tank volume of 0.5 m^(3);and five buried pipes with a burial depth of 20 m.Furthermore,the heat collection capacity of the solar collectors spanning an area of 3 m^(2)was found to be equivalent to that of one buried pipe.Moreover,the findings revealed that the solar‒ground source heat pump coupling system demonstrated a lower annual cumulative energy consumption compared to the ground source heat pump system,presenting a reduction of 5.31%compared to the energy consumption of the latter.
基金supported in part by the National Science Fund for Distinguished Young Scholars under Grant (42025403)the National Key Research and Development Plan of China (2021YFA0716800)the National Key Research and Development Plan of China (2022YFC2903804)。
文摘The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic technology,plays a pivotal role in ensuring mine safety by enabling real-time identifi cation and accurate classification of vibration signals such as microseismic signals,blasting signals,and noise.These classifications are critical for improving the efficacy of ground pressure monitoring systems,conducting stability analyses of deep rock masses,and implementing timely and precise roadway support measures.Such eff orts are essential for mitigating ground pressure disasters and ensuring safe mining operations.This study proposes an artificial intelligence-based automatic classification network model for mine vibration signals.Based on conventional convolutional neural networks,the proposed model further incorporates long short-term memory(LSTM)networks and attention mechanisms.The LSTM component eff ectively captures temporal correlations in time-series mining vibration data,while the attention mechanism enhances the models’ability to focus on critical features within the data.To validate the eff ectiveness of our proposed model,a dataset comprising 480,526 waveform records collected in 2022 by the microseismic monitoring system at Guangxi Shanhu Tungsten Mine was used for training,validation,and testing purposes.Results demonstrate that the proposed artifi cial intelligence-based classifi cation method achieves a higher recognition accuracy of 92.21%,significantly outperforming traditional manual classification methods.The proposed model represents a signifi cant advancement in ground pressure monitoring and disaster mitigation.
基金supported by the National Natural Science Foundation of China (grant numbers 42293351, and U2468221)。
文摘This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.
基金supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(Grant No.2021QNLM020001)the National Key R&D Program of China(Grant No.2019YFC0605503C)+2 种基金the Major Scientific and Technological Projects of China National Petroleum Corporation(CNPC)(Grant No.ZD2019-183-003)the National Outstanding Youth Science Foundation(Grant No.41922028)the National Innovation Group Project(Grant No.41821002).
文摘Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.
基金National Natural Science Foundation of China(Grant No.42274180)National Key Research and Development Program of China(2021YFC2902003).
文摘Evaluation of water richness in sandstone is an important research topic in the prevention and control of mine water disasters,and the water richness in sandstone is closely related to its porosity.The refl ection seismic exploration data have high-density spatial sampling information,which provides an important data basis for the prediction of sandstone porosity in coal seam roofs by using refl ection seismic data.First,the basic principles of the variational mode decomposition(VMD)method and the random forest method are introduced.Then,the geological model of coal seam roof sandstone is constructed,seismic forward modeling is conducted,and random noise is added.The decomposition eff ects of the empirical mode decomposition(EMD)method and VMD method on noisy signals are compared and analyzed.The test results show that the firstorder intrinsic mode functions(IMF1)and IMF2 decomposed by the VMD method contain the main eff ective components of seismic signals.A prediction process of sandstone porosity in coal seam roofs based on the combination of VMD and random forest method is proposed.The feasibility and eff ectiveness of the method are verified by trial calculation in the porosity prediction of model data.Taking the actual coalfield refl ection seismic data as an example,the sandstone porosity of the 8 coal seam roof is predicted.The application results show the potential application value of the new porosity prediction method proposed in this study.This method has important theoretical guiding significance for evaluating water richness in coal seam roof sandstone and the prevention and control of mine water disasters.
基金Science and Technology Major Project of Xinjiang Uygur Autonomous Region(2020A03003-7)Fundamental Research on Natural Science Program of Shaanxi Province(2021JM-180)+2 种基金Fundamental Research Funds for the Central Universities,CHD(Project for Leading Talents)(300102211302)Tianshan Cedar Plan of Science and Technology Department of Xinjiang Uygur Autonomous Region(2017XS13)Shaanxi Province Young Talent Lifting Program(CLGC202219).
文摘When the expressway crosses the goafs inevitably,the design is generally to build the road on coal pillars as much as possible.However,the existing coal pillars are often unable to meet relevant requirements of highway construction.Combining three-dimensional physical model tests,numerical simulations and field monitoring,with the Urumqi East Second Ring Road passing through acute inclined goafs as a background,the deformation and failure mechanism of the overlying rock and coal pillars in acute inclined goafs under expressway load were studied.And in accordance with construction requirements of subgrade,comprehensive consideration of the deformation and instability mechanism of acute inclined goafs,the treatment measures and suggestions for this type of geological disasters were put forward.The research results confirmed the rationality of coal pillars in acute inclined goafs under the expressway through grouting.According to the ratio of diff erent overlying rock thickness to coal pillar height,the change trend and value of the required grouting range were summarized,which can provide reference for similar projects.
基金innovation consortium project of China Petroleum and Southwest Petroleum University(No.2020CX010201)Sichuan Science and Technology Program(No.2024NSFSC0081)。
文摘With the continued expansion of oil and gas exploration,both in the eastern and western regions,the quality of seismic acquisition has become a key factor in oil and gas exploration in complex areas.However,conventional seismic acquisition methods cannot efficiently avoid challenging acquisition locations and produce high-quality seismic data.In this regard,based on the curvelet transform,this paper proposes an irregular seismic acquisition method,which utilizes the high-precision characteristics of the curvelet transform and simulated annealing algorithm to establish a method for the evaluation of the coherence of irregular sampling matrices and design of observation systems.The method was verified using forward simulation and actual acquisition data.The results suggest the superior quality of seismic data gathered in complicated areas through this method over those acquired using traditional methods,which can provide technical guidance for the design of observation systems in complex areas.
文摘This study discusses the challenges in logging the evaluation of low-resistivity oil reservoirs,especially the difficult problems involving their saturation calculation.A correction method for equivalent water conductivity is proposed,given the high conductivity caused by small amounts of water distributed in a network within the low-resistivity reservoir,which mimics the eff ects of high water saturation.This approach signifi cantly improves the accuracy of hydrocarbon saturation calculations in these low-resistivity reservoirs.The corrected hydrocarbon saturation values highly matched the porosity and are consistent with experimental results.This study also establishes a discrimination process to determine whether corrections are required,verifying the eff ectiveness and accuracy of the method through an application example.
基金supported By Grant (PLN2022-14) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University)。
文摘Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.
基金supported by Heilongjiang Province Basic Research Business Expenses for Universities Heilongjiang University Special Fund Project (Grant No. 2023-KYYWF-1494)the Natural Science Foundation of Jiangxi Province (Grant No. 20212BAB213023)。
文摘As geological exploration conditions become increasingly complex, meeting the requirements of precise geological exploration necessitates the development of a controlled-source audio magnetotelluric (CSAMT) inversion method that considers anisotropy to improve the effectiveness of inversion accuracy and interpretation accuracy of data. This study is based on the 3D fi nite-diff erence forward modeling of axis anisotropy using the reciprocity theorem to calculate the Jacobian matrix by applying the search method to automatically search for the Lagrange operator. The aim is to establish inversion iteration equations to achieve the axis anisotropic Occam's 3D inversion of tensor CSAMT in data space. Further, we obtain an underground axis anisotropic 3D geoelectric model by inverting the impedance data of tensor CSAMT. Two synthetic data examples show that using the isotropic tensor CSAMT algorithm to directly invert data in anisotropic media can generate false anomalies, leading to incorrect geological interpretations. Meanwhile, the proposed anisotropic inversion algorithm can eff ectively improve the accuracy of data inversion in anisotropic media. Further, the inversion examples verify the eff ectiveness and stability of the algorithm.
基金The authors would like to express their sincere appreciation to the research project of CNPC Geophysical Key Lab(2022DQ0604-4)National Natural Science Foundation of China(Grant No.42074141).
文摘Low-frequency vibroseis acquisition has become a routine operation in land seismic surveys,given the advantages of low-frequency signals in characterizing geological structures and enhancing the imaging of deep exploration targets.The two key points of low-frequency sweep design techniques include controlling the distortion and improving the output energy during the low-frequency stage.However,the vibrators are limited by the maximum fl ow provided by the hydraulic systems at the low-frequency stage,causing difficulty in satisfying exploration energy requirements.Initially,a theoretical analysis of the low-frequency acquisition performance of vibrators is conducted.A theoretical maximum output force below 10 Hz is obtained by guiding through theoretical formulas and combining actual vibrator parameters.Then,the signal is optimized according to the surface characteristics of the operation area.Finally,detailed application quality control and operational procedures are established.The new low-frequency sweep design method has overcome the maximum flow limitations of the hydraulic system,increased the low-frequency energy,and achieved broadband acquisition.The designed signal has been tested and applied on various types of ground surfaces in the Middle East desert region,yielding good performance.The proposed low-frequency sweep design method holds considerable value for the application of conventional vibroseis in low-frequency acquisition.
基金supported by General Projects of the Natural Science Foundation of China(41972313)major projects of the Natural Science Foundation of China(41790453)。
文摘Fractures are critical control factors in volcanic reservoirs.Thus,studies on the prediction and distribution patterns of volcanic fractures are crucial for oil and gas exploration and development.Although considerable research has been conducted on volcanic fractures,targeted research on the spatial distribution patterns of fracture development remains limited in the literature.Two eruption modes,subaqueous eruption and subaerial eruption,have been identified in the Chaganhua subsag of the Songliao Basin,where gas resources have been discovered.Studying the differences in volcanic fracture development is highly important for understanding the law of oil and gas enrichment in volcanic reservoirs.On the basis of thin sections,cores,electrical imaging logs,and other data obtained from boreholes of subaqueous volcanic eruptions in the Songnan fault depression,we describe the characteristics of drilled fractures in detail and use 3D seismic data to extract intrinsic coherence,ant bodies,and Fourier series to extend the azimuthal anisotropic P-wave reflection coefficient,thereby predicting the spatial distribution of fractures at different scales.Moreover,through statistical analysis of quantitative evaluation indicators,such as fracture density and fracture development rate in different drilling wells,combined with the spatial distribution patterns of the predicted fractures,we compared and analyzed the relationships between the distributions of fractures and volcanic institutions,lithology,and volcanic facies.Results indicate that fractures are more developed in volcanic edifices located closer to faults.Considerable differences in fracture development are observed among lithofacies of volcanic rocks.Near faults,effusion facies,explosive facies and external clastic pyroclastic sedimentary subfacies are favorable for fracture development.This research provides a reference for investigating volcanic rock fractures of the same origins worldwide.
基金Earth Observation and Navigation Special,Research on Low Temperature Superconducting Aeromagnetic Vector Gradient Observation Technology(2021YFB3900201)projectState Key Laboratory of Remote Sensing Science project.
文摘In this paper,we investigate the method of compensating LTS SQUID Gradiometer Systems data.By matching the attitude changes of the pod in fl ight to the anomalies of the magnetic measurement data,we find that the yaw attitude changes most dramatically and corresponds best to the magnetic data anomaly interval.Based on this finding,we solved the compensation model using least squares fitting and Huber's parametric fitting.By comparison,we found that the Huber parametric fit not only eliminates the interference introduced by attitude changes but also retains richer anomaly source information and therefore obtains a higher signal-to-noise ratio.The experimental results show that the quality of the magnetometry data obtained by using the compensation method proposed in this paper has been significantly improved,and the mean value of its improvement ratio can reach 118.93.
基金supported by the PetroChina Prospective,Basic,and Strategic Technology Research Project(No.2021ZG03-02 and No.2023DJ8402)。
文摘The deep learning algorithm,which has been increasingly applied in the field of petroleum geophysical prospecting,has achieved good results in improving efficiency and accuracy based on test applications.To play a greater role in actual production,these algorithm modules must be integrated into software systems and used more often in actual production projects.Deep learning frameworks,such as TensorFlow and PyTorch,basically take Python as the core architecture,while the application program mainly uses Java,C#,and other programming languages.During integration,the seismic data read by the Java and C#data interfaces must be transferred to the Python main program module.The data exchange methods between Java,C#,and Python include shared memory,shared directory,and so on.However,these methods have the disadvantages of low transmission efficiency and unsuitability for asynchronous networks.Considering the large volume of seismic data and the need for network support for deep learning,this paper proposes a method of transmitting seismic data based on Socket.By maximizing Socket’s cross-network and efficient longdistance transmission,this approach solves the problem of inefficient transmission of underlying data while integrating the deep learning algorithm module into a software system.Furthermore,the actual production application shows that this method effectively solves the shortage of data transmission in shared memory,shared directory,and other modes while simultaneously improving the transmission efficiency of massive seismic data across modules at the bottom of the software.
基金supported by Science and Technology Cooperation Project of the CNPC-SWPU Innovation Alliance(No.2020CX010202).
文摘The Jurassic Lianggaoshan Formation in eastern Sichuan Basin is a key target for shale oil exploration.It faces challenges in three-pressure prediction due to complex structural and sedimentary interactions,as well as strong reservoir anisotropy.These issues often lead to wellbore instability and gas logging anomalies during drilling.This study presents an integrated workflow that combines residual moveout correction using correlation-based dynamic time warping(CDTW),high-resolution seismic waveform indication inversion,and three-pressure prediction of jointing well-seismic data.Applied to the LT1 well block,the workflow effectively corrects anisotropic residual moveout in image gathers,leading to a signal strength increase of over 10%in frequency bands above 30 Hz and enhancing event continuity.High-resolution rock mechanical parameters are obtained through seismic waveform inversion and regional calibration,enabling the prediction of three-dimensional pore pressure,collapse pressure and fracture pressure.The results are consistent with actual drilling gas shows and core data,confirming the method's accuracy and supporting mud weight planning and wellbore stability efforts.This cost-effective and technically robust approach proves highly reliable in complex environments with significant heterogeneity and anisotropy,assisting drilling decisions and risk management in eastern Sichuan and similar challenging geological settings.
基金projects of China Railway Beijing Group Company Limited.(No.2016CG23)for funding this research。
文摘The geological disasters such as collapse,mud bursting and water gushing often occur during tunnel construction.Thus,it is of great significance to detect the hidden geological disasters ahead of the tunnel face.The audio magnetotelluric(AMT)was applied for the advanced detectionstudy during the boring process of the Tianheshan tunnel in the Taihang Mountains.Three AMT profiles were deployed above the tunnel,and the data obtained in the field were analysed in terms of electrical principal axes.From shallow to deep,the direction of the geoelectric strike angle changes,generally between 30°and 60°NE,which is consistent with similar to the direction of the Taihangshan Uplift,and the data show some 3D characteristics.Two-dimensional(2D)and three-dimensional(3D)inversion methods were adopted to jointly study the subsurface structural information,and the resistivity model was geophysically and geologically interpreted.Two sets of low resistance anomalies were found,and it was hypothesised that the near-erect low-resistivity anomalies in the east might be a fragmentation zone,while the low resistance anomalies in the west,which are inclined to the westward,might be a tectonic structure or fragmentation zone related to the regional major fault,and the results of the tunnelling confirmed the reliability of the inversion interpretation.The 3D inversion can fully reflect the development scale and morphological changes of the fracture zone,and the inversion model is more reliable.Finally,it is concluded that the audio magnetotelluric method,which adopts advanced acquisition,processing and inversion interpretation techniques,is an effective means of over-detection of tunnels.
