The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata ...The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata in tight sandstone. First, multi-component composite seismic attributes are obtained.The strong nonlinear relationships between multi-component composite attributes and gas-bearing reservoirs can be constrained through a DNN. Therefore, we identify and predict the gas-bearing strata using a DNN. Then, sample data are fed into the DNN for training and testing. After optimized network parameters are determined by the performance curves and empirical formulas, the best deep learning gas-bearing prediction model is determined. The composite seismic attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics from known drilling areas to the entire region for predicting the gas reservoir distribution. Finally, we assess the proposed method in terms of the structure and fracture characteristics and predict favorable exploration areas for identifying gas reservoirs.展开更多
This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data.Traditional seismic data often lack both high and low frequencies,which are essential for detail...This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data.Traditional seismic data often lack both high and low frequencies,which are essential for detailed geological interpretation and various geophysical applications.Low frequency data is particularly valuable for reducing wavelet sidelobes and improving full waveform inversion(FWI).Conventional methods for bandwidth extension include seismic deconvolution and sparse inversion,which have limitations in recovering low frequencies.The study explores the potential of the U-net,which has been successful in other geophysical applications such as noise attenuation and seismic resolution enhancement.The novelty in our approach is that we do not rely on computationally expensive finite difference modelling to create training data.Instead,our synthetic training data is created from individual randomly perturbed events with variations in bandwidth,making it more adaptable to different data sets compared to previous deep learning methods.The method was tested on both synthetic and real seismic data,demonstrating effective low frequency reconstruction and sidelobe reduction.With a synthetic full waveform inversion to recover a velocity model and a seismic amplitude inversion to estimate acoustic impedance we demonstrate the validity and benefit of the proposed method.Overall,the study presents a robust approach to seismic bandwidth extension using deep learning,emphasizing the importance of diverse and well-designed but computationally inexpensive synthetic training data.展开更多
Seismic data denoising is a critical process usually applied at various stages of the seismic processing workflow,as our ability to mitigate noise in seismic data affects the quality of our subsequent analyses.However...Seismic data denoising is a critical process usually applied at various stages of the seismic processing workflow,as our ability to mitigate noise in seismic data affects the quality of our subsequent analyses.However,finding an optimal balance between preserving seismic signals and effectively reducing seismic noise presents a substantial challenge.In this study,we introduce a multi-stage deep learning model,trained in a self-supervised manner,designed specifically to suppress seismic noise while minimizing signal leakage.This model operates as a patch-based approach,extracting overlapping patches from the noisy data and converting them into 1D vectors for input.It consists of two identical sub-networks,each configured differently.Inspired by the transformer architecture,each sub-network features an embedded block that comprises two fully connected layers,which are utilized for feature extraction from the input patches.After reshaping,a multi-head attention module enhances the model’s focus on significant features by assigning higher attention weights to them.The key difference between the two sub-networks lies in the number of neurons within their fully connected layers.The first sub-network serves as a strong denoiser with a small number of neurons,effectively attenuating seismic noise;in contrast,the second sub-network functions as a signal-add-back model,using a larger number of neurons to retrieve some of the signal that was not preserved in the output of the first sub-network.The proposed model produces two outputs,each corresponding to one of the sub-networks,and both sub-networks are optimized simultaneously using the noisy data as the label for both outputs.Evaluations conducted on both synthetic and field data demonstrate the model’s effectiveness in suppressing seismic noise with minimal signal leakage,outperforming some benchmark methods.展开更多
Seismic data plays a pivotal role in fault detection,offering critical insights into subsurface structures and seismic hazards.Understanding fault detection from seismic data is essential for mitigating seismic risks ...Seismic data plays a pivotal role in fault detection,offering critical insights into subsurface structures and seismic hazards.Understanding fault detection from seismic data is essential for mitigating seismic risks and guiding land-use plans.This paper presents a comprehensive review of existing methodologies for fault detection,focusing on the application of Machine Learning(ML)and Deep Learning(DL)techniques to enhance accuracy and efficiency.Various ML and DL approaches are analyzed with respect to fault segmentation,adaptive learning,and fault detection models.These techniques,benchmarked against established seismic datasets,reveal significant improvements over classical methods in terms of accuracy and computational efficiency.Additionally,this review highlights emerging trends,including hybrid model applications and the integration of real-time data processing for seismic fault detection.By providing a detailed comparative analysis of current methodologies,this review aims to guide future research and foster advancements in the effectiveness and reliability of seismic studies.Ultimately,the study seeks to bridge the gap between theoretical investigations and practical implementations in fault detection.展开更多
Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approache...Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approaches sometimes fail to comprehend complex and persistent relationships between pore pressure and formation properties in the heterogeneous reservoirs.This study presents a novel machine learning optimized pore pressure prediction method with a limited dataset,particularly in complex formations.The method addresses the conventional approach's limitations by leveraging its capability to learn complex data relationships.It integrates the best Gradient Boosting Regressor(GBR)algorithm to model pore pressure at wells and later utilizes ContinuousWavelet Transformation(CWT)of the seismic dataset for spatial analysis,and finally employs Deep Neural Network for robust and precise pore pressure modeling for the whole volume.In the second stage,for the spatial variations of pore pressure in the thin Khadro Formation sand reservoir across the entire subsurface area,a three-dimensional pore pressure prediction is conducted using CWT.The relationship between the CWT and geomechanical properties is then established through supervised machine learning models on well locations to predict the uncertainties in pore pressure.Among all intelligent regression techniques developed using petrophysical and elastic properties for pore pressure prediction,the GBR has provided exceptional results that have been validated by evaluation metrics based on the R^(2) score i.e.,0.91 between the calibrated and predicted pore pressure.Via the deep neural network,the relationship between CWT resultant traces and predicted pore pressure is established to analyze the spatial variation.展开更多
The Belt and Road global navigation satellite system(B&R GNSS)network is the first large-scale deployment of Chinese GNSS equipment in a seismic system.Prior to this,there have been few systematic assessments of t...The Belt and Road global navigation satellite system(B&R GNSS)network is the first large-scale deployment of Chinese GNSS equipment in a seismic system.Prior to this,there have been few systematic assessments of the data quality of Chinese GNSS equipment.In this study,data from four representative GNSS sites in different regions of China were analyzed using the G-Nut/Anubis software package.Four main indicators(data integrity rate,data validity ratio,multi-path error,and cycle slip ratio)used to systematically analyze data quality,while evaluating the seismic monitoring capabilities of the network based on earthquake magnitudes estimated from high-frequency GNSS data are evaluated by estimating magnitude based on highfrequency GNSS data.The results indicate that the quality of the data produced by the three types of Chinese receivers used in the network meets the needs of earthquake monitoring and the new seismic industry standards,which provide a reference for the selection of equipment for future new projects.After the B&R GNSS network was established,the seismic monitoring capability for earthquakes with magnitudes greater than M_(W)6.5 in most parts of the Sichuan-Yunnan region improved by approximately 20%.In key areas such as the Sichuan-Yunnan Rhomboid Block,the monitoring capability increased by more than 25%,which has greatly improved the effectiveness of regional comprehensive earthquake management.展开更多
During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resol...During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resolution seismic data processing technologies and methods tailored for drilling scenarios.The high-resolution processing of seismic data is divided into three stages:pre-drilling processing,post-drilling correction,and while-drilling updating.By integrating seismic data from different stages,spatial ranges,and frequencies,together with information from drilled wells and while-drilling data,and applying artificial intelligence modeling techniques,a progressive high-resolution processing technology of seismic data based on multi-source information fusion is developed,which performs simple and efficient seismic information updates during drilling.Case studies show that,with the gradual integration of multi-source information,the resolution and accuracy of seismic data are significantly improved,and thin-bed weak reflections are more clearly imaged.The updated seismic information while-drilling demonstrates high value in predicting geological bodies ahead of the drill bit.Validation using logging,mud logging,and drilling engineering data ensures the fidelity of the processing results of high-resolution seismic data.This provides clearer and more accurate stratigraphic information for drilling operations,enhancing both drilling safety and efficiency.展开更多
Formation pore pressure is the foundation of well plan,and it is related to the safety and efficiency of drilling operations in oil and gas development.However,the traditional method for predicting formation pore pres...Formation pore pressure is the foundation of well plan,and it is related to the safety and efficiency of drilling operations in oil and gas development.However,the traditional method for predicting formation pore pressure involves applying post-drilling measurement data from nearby wells to the target well,which may not accurately reflect the formation pore pressure of the target well.In this paper,a novel method for predicting formation pore pressure ahead of the drill bit by embedding petrophysical theory into machine learning based on seismic and logging-while-drilling(LWD)data was proposed.Gated recurrent unit(GRU)and long short-term memory(LSTM)models were developed and validated using data from three wells in the Bohai Oilfield,and the Shapley additive explanations(SHAP)were utilized to visualize and interpret the models proposed in this study,thereby providing valuable insights into the relative importance and impact of input features.The results show that among the eight models trained in this study,almost all model prediction errors converge to 0.05 g/cm^(3),with the largest root mean square error(RMSE)being 0.03072 and the smallest RMSE being 0.008964.Moreover,continuously updating the model with the increasing training data during drilling operations can further improve accuracy.Compared to other approaches,this study accurately and precisely depicts formation pore pressure,while SHAP analysis guides effective model refinement and feature engineering strategies.This work underscores the potential of integrating advanced machine learning techniques with domain-specific knowledge to enhance predictive accuracy for petroleum engineering applications.展开更多
In multi-component seismic exploration, the horizontal and vertical components both contain P- and SV-waves. The P- and SV-wavefields in a seismic record can be separated by their horizontal and vertical displacements...In multi-component seismic exploration, the horizontal and vertical components both contain P- and SV-waves. The P- and SV-wavefields in a seismic record can be separated by their horizontal and vertical displacements when upgoing P- and SV-waves arrive at the sea floor. If the sea floor P wave velocity, S wave velocity, and density are known, the separation can be achieved in ther-p domain. The separated wavefields are then transformed to the time domain. A method of separating P- and SV-wavefields is presented in this paper and used to effectively separate P- and SV-wavefields in synthetic and real data. The application to real data shows that this method is feasible and effective. It also can be used for free surface data.展开更多
Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the g...Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.展开更多
To solve the problems in restoring sedimentary facies and predicting reservoirs in loose gas-bearing sediment,based on seismic sedimentologic analysis of the first 9-component S-wave 3D seismic dataset of China,a four...To solve the problems in restoring sedimentary facies and predicting reservoirs in loose gas-bearing sediment,based on seismic sedimentologic analysis of the first 9-component S-wave 3D seismic dataset of China,a fourth-order isochronous stratigraphic framework was set up and then sedimentary facies and reservoirs in the Pleistocene Qigequan Formation in Taidong area of Qaidam Basin were studied by seismic geomorphology and seismic lithology.The study method and thought are as following.Firstly,techniques of phase rotation,frequency decomposition and fusion,and stratal slicing were applied to the 9-component S-wave seismic data to restore sedimentary facies of major marker beds based on sedimentary models reflected by satellite images.Then,techniques of seismic attribute extraction,principal component analysis,and random fitting were applied to calculate the reservoir thickness and physical parameters of a key sandbody,and the results are satisfactory and confirmed by blind testing wells.Study results reveal that the dominant sedimentary facies in the Qigequan Formation within the study area are delta front and shallow lake.The RGB fused slices indicate that there are two cycles with three sets of underwater distributary channel systems in one period.Among them,sandstones in the distributary channels of middle-low Qigequan Formation are thick and broad with superior physical properties,which are favorable reservoirs.The reservoir permeability is also affected by diagenesis.Distributary channel sandstone reservoirs extend further to the west of Sebei-1 gas field,which provides a basis to expand exploration to the western peripheral area.展开更多
Complex repairable system is composed of thousands of components.Some maintenance management and decision problems in maintenance management and decision need to classify a set of components into several classes based...Complex repairable system is composed of thousands of components.Some maintenance management and decision problems in maintenance management and decision need to classify a set of components into several classes based on data mining.Furthermore,with the complexity of industrial equipment increasing,the managers should pay more attention to the key components and carry out the lean management is very important.Therefore,the idea"customer segmentation"of"precise marketing"can be used in the maintenance management of the multi-component system.Following the idea of segmentation,the components of multicomponent systems should be subdivied into groups based on specific attributes relevant to maintenance,such as maintenance cost,mean time between failures,and failure frequency.For the target specific groups of parts,the optimal maintenance policy,health assessment and maintenance scheduling can be determined.The proposed analysis framework will be given out.In order to illustrate the effectiveness of this method,a numerical example is given out.展开更多
Geophysicists interpreting seismic reflection data aim for the highest resolution possible as this facilitates the interpretation and discrimination of subtle geological features.Various deterministic methods based on...Geophysicists interpreting seismic reflection data aim for the highest resolution possible as this facilitates the interpretation and discrimination of subtle geological features.Various deterministic methods based on Wiener filtering exist to increase the temporal frequency bandwidth and compress the seismic wavelet in a process called spectral shaping.Auto-encoder neural networks with convolutional layers have been applied to this problem,with encouraging results,but the problem of generalization to unseen data remains.Most published works have used supervised learning with training data constructed from field seismic data or synthetic seismic data generated based on measured well logs or based on seismic wavefield modelling.This leads to satisfactory results on datasets similar to the training data but requires re-training of the networks for unseen data with different characteristics.In this work seek to improve the generalization,not by experimenting with network architecture(we use a conventional U-net with some small modifications),but by adopting a different approach to creating the training data for the supervised learning process.Although the network is important,at this stage of development we see more improvement in prediction results by altering the design of the training data than by architectural changes.The approach we take is to create synthetic training data consisting of simple geometric shapes convolved with a seismic wavelet.We created a very diverse training dataset consisting of 9000 seismic images with between 5 and 300 seismic events resembling seismic reflections that have geophysically motived perturbations in terms of shape and character.The 2D U-net we have trained can boost robustly and recursively the dominant frequency by 50%.We demonstrate this on unseen field data with different bandwidths and signal-to-noise ratios.Additionally,this 2D U-net can handle non-stationary wavelets and overlapping events of different bandwidth without creating excessive ringing.It is also robust in the presence of noise.The significance of this result is that it simplifies the effort of bandwidth extension and demonstrates the usefulness of auto-encoder neural network for geophysical data processing.展开更多
The use of blended acquisition technology in marine seismic exploration has the advantages of high acquisition efficiency and low exploration costs.However,during acquisition,the primary source may be disturbed by adj...The use of blended acquisition technology in marine seismic exploration has the advantages of high acquisition efficiency and low exploration costs.However,during acquisition,the primary source may be disturbed by adjacent sources,resulting in blended noise that can adversely affect data processing and interpretation.Therefore,the de-blending method is needed to suppress blended noise and improve the quality of subsequent processing.Conventional de-blending methods,such as denoising and inversion methods,encounter challenges in parameter selection and entail high computational costs.In contrast,deep learning-based de-blending methods demonstrate reduced reliance on manual intervention and provide rapid calculation speeds post-training.In this study,we propose a Uformer network using a nonoverlapping window multihead attention mechanism designed for de-blending blended data in the common shot domain.We add the depthwise convolution to the feedforward network to improve Uformer’s ability to capture local context information.The loss function comprises SSIM and L1 loss.Our test results indicate that the Uformer outperforms convolutional neural networks and traditional denoising methods across various evaluation metrics,thus highlighting the effectiveness and advantages of Uformer in de-blending blended data.展开更多
Irregular seismic data causes problems with multi-trace processing algorithms and degrades processing quality. We introduce the Projection onto Convex Sets （POCS） based image restoration method into the seismic data...Irregular seismic data causes problems with multi-trace processing algorithms and degrades processing quality. We introduce the Projection onto Convex Sets （POCS） based image restoration method into the seismic data reconstruction field to interpolate irregularly missing traces. For entire dead traces, we transfer the POCS iteration reconstruction process from the time to frequency domain to save computational cost because forward and reverse Fourier time transforms are not needed. In each iteration, the selection threshold parameter is important for reconstruction efficiency. In this paper, we designed two types of threshold models to reconstruct irregularly missing seismic data. The experimental results show that an exponential threshold can greatly reduce iterations and improve reconstruction efficiency compared to a linear threshold for the same reconstruction result. We also analyze the anti- noise and anti-alias ability of the POCS reconstruction method. Finally, theoretical model tests and real data examples indicate that the proposed method is efficient and applicable.展开更多
The Pearl River Estuary(PRE) is located at the onshore-offshore transition zone between South China and South China Sea Basin, and it is of great significant value in discussing tectonic relationships between South Ch...The Pearl River Estuary(PRE) is located at the onshore-offshore transition zone between South China and South China Sea Basin, and it is of great significant value in discussing tectonic relationships between South China block and South China Sea block and seismic activities along the offshore active faults in PRE. However, the researches on geometric characteristics of offshore faults in this area are extremely lacking. To investigate the offshore fault distribution and their geometric features in the PRE in greater detail, we acquired thirteen seismic reflection profiles in 2015. Combining the analysis of the seismic reflection and free-air gravity anomaly data, this paper revealed the location, continuity, and geometry of the littoral fault zone and other offshore faults in PRE. The littoral fault zone is composed of the major Dangan Islands fault and several parallel, high-angle, normal faults, which mainly trend northeast to northeast-to-east and dip to the southeast with large displacements. The fault zone is divided into three different segments by the northwest-trending faults. Moreover, the basement depth around Dangan Islands is very shallow, while it suddenly increases along the islands westward and southward. These has resulted in the islands and neighboring areas becoming the places where the stress accumulates easily. The seismogenic pattern of this area is closely related to the comprehensive effect of intersecting faults together with the low velocity layer.展开更多
In this paper, a new concept called numerical structure of seismic data is introduced and the difference between numerical structure and numerical value of seismic data is explained. Our study shows that the numerical...In this paper, a new concept called numerical structure of seismic data is introduced and the difference between numerical structure and numerical value of seismic data is explained. Our study shows that the numerical seismic structure is closely related to oil and gas-bearing reservoir, so it is very useful for a geologist or a geophysicist to precisely interpret the oil-bearing layers from the seismic data. This technology can be applied to any exploration or production stage. The new method has been tested on a series of exploratory or development wells and proved to be reliable in China. Hydrocarbon-detection with this new method for 39 exploration wells on 25 structures indi- cates a success ratio of over 80 percent. The new method of hydrocarbon prediction can be applied for: (1) depositional environment of reservoirs with marine fades, delta, or non-marine fades (including fluvial facies, lacustrine fades); (2) sedimentary rocks of reservoirs that are non-marine clastic rocks and carbonate rock; and (3) burial depths range from 300 m to 7000 m, and the minimum thickness of these reservoirs is over 8 m (main frequency is about 50 Hz).展开更多
The field seismic data is disturbed by the interferential information, which has low signal to noise ratio (SNR). That is disadvantage for seismic data interpretation. So it is important to remove the noise of seismic...The field seismic data is disturbed by the interferential information, which has low signal to noise ratio (SNR). That is disadvantage for seismic data interpretation. So it is important to remove the noise of seismic data. Independent component analysis (ICA) can remove most of the noise interference. However, ICA has some defects in noise reduction, because it needs some conditions that seismic data is independent reciprocally for denoising. To solve these defects, this paper proposes an improved ICA algorithm to noise reduction. Through simulation experiments, it can be obtained that the best decomposition levels of the new algorithm is 3. At last, the proposed improved ICA is applied to deal with the actual seismic data. The results show that it can effectively eliminate most of seismic noise such as random noise, linear interference, surface waves, and so on. The improved ICA is not only easy to denoising, but also has excellent mathematical theoretical properties.展开更多
A new method is introduced to suppress the noise in seismic data processing. Based on the subtle difference in shape between the noise and the actual signal, we introduce morphologic filtering into seismic data proces...A new method is introduced to suppress the noise in seismic data processing. Based on the subtle difference in shape between the noise and the actual signal, we introduce morphologic filtering into seismic data processing. From the shape and the S/N we can see that the effect of morphologic filtering is superior to other methods like id-value filtering, neighbor average filtering, etc. The SNR of the signal after morphological filtering is comparatively great. In addition, the precision of the seismic data after morphological filtering is high. The characteristics of the actual signal, such as frequency and amplitude, are preserved. We give an example of the real seismic data processing using morphological filtering, in which the actual signal is retained, while the random high intensity noise was removed.展开更多
In this paper, multi-scaled morphology is introduced into the digital processing domain for land seismic data. First, we describe the basic theory of multi-scaled morphology image decomposition of exploration seismic ...In this paper, multi-scaled morphology is introduced into the digital processing domain for land seismic data. First, we describe the basic theory of multi-scaled morphology image decomposition of exploration seismic waves; second, we illustrate how to use multi-scaled morphology for seismic data processing using two real examples. The first example demonstrates suppressing the surface waves in pre-stack seismic records using multi-scaled morphology decomposition and reconstitution and the other example demonstrates filtering different interference waves on the seismic record. Multi-scaled morphology filtering separates signal from noise by the detailed differences of the wave shapes. The successful applications suggest that multi-scaled morphology has a promising application in seismic data processing.展开更多
基金funded by the Natural Science Foundation of Shandong Province (ZR202103050722)National Natural Science Foundation of China (41174098)。
文摘The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata in tight sandstone. First, multi-component composite seismic attributes are obtained.The strong nonlinear relationships between multi-component composite attributes and gas-bearing reservoirs can be constrained through a DNN. Therefore, we identify and predict the gas-bearing strata using a DNN. Then, sample data are fed into the DNN for training and testing. After optimized network parameters are determined by the performance curves and empirical formulas, the best deep learning gas-bearing prediction model is determined. The composite seismic attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics from known drilling areas to the entire region for predicting the gas reservoir distribution. Finally, we assess the proposed method in terms of the structure and fracture characteristics and predict favorable exploration areas for identifying gas reservoirs.
文摘This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data.Traditional seismic data often lack both high and low frequencies,which are essential for detailed geological interpretation and various geophysical applications.Low frequency data is particularly valuable for reducing wavelet sidelobes and improving full waveform inversion(FWI).Conventional methods for bandwidth extension include seismic deconvolution and sparse inversion,which have limitations in recovering low frequencies.The study explores the potential of the U-net,which has been successful in other geophysical applications such as noise attenuation and seismic resolution enhancement.The novelty in our approach is that we do not rely on computationally expensive finite difference modelling to create training data.Instead,our synthetic training data is created from individual randomly perturbed events with variations in bandwidth,making it more adaptable to different data sets compared to previous deep learning methods.The method was tested on both synthetic and real seismic data,demonstrating effective low frequency reconstruction and sidelobe reduction.With a synthetic full waveform inversion to recover a velocity model and a seismic amplitude inversion to estimate acoustic impedance we demonstrate the validity and benefit of the proposed method.Overall,the study presents a robust approach to seismic bandwidth extension using deep learning,emphasizing the importance of diverse and well-designed but computationally inexpensive synthetic training data.
基金supported by the King Abdullah University of Science and Technology(KAUST)。
文摘Seismic data denoising is a critical process usually applied at various stages of the seismic processing workflow,as our ability to mitigate noise in seismic data affects the quality of our subsequent analyses.However,finding an optimal balance between preserving seismic signals and effectively reducing seismic noise presents a substantial challenge.In this study,we introduce a multi-stage deep learning model,trained in a self-supervised manner,designed specifically to suppress seismic noise while minimizing signal leakage.This model operates as a patch-based approach,extracting overlapping patches from the noisy data and converting them into 1D vectors for input.It consists of two identical sub-networks,each configured differently.Inspired by the transformer architecture,each sub-network features an embedded block that comprises two fully connected layers,which are utilized for feature extraction from the input patches.After reshaping,a multi-head attention module enhances the model’s focus on significant features by assigning higher attention weights to them.The key difference between the two sub-networks lies in the number of neurons within their fully connected layers.The first sub-network serves as a strong denoiser with a small number of neurons,effectively attenuating seismic noise;in contrast,the second sub-network functions as a signal-add-back model,using a larger number of neurons to retrieve some of the signal that was not preserved in the output of the first sub-network.The proposed model produces two outputs,each corresponding to one of the sub-networks,and both sub-networks are optimized simultaneously using the noisy data as the label for both outputs.Evaluations conducted on both synthetic and field data demonstrate the model’s effectiveness in suppressing seismic noise with minimal signal leakage,outperforming some benchmark methods.
文摘Seismic data plays a pivotal role in fault detection,offering critical insights into subsurface structures and seismic hazards.Understanding fault detection from seismic data is essential for mitigating seismic risks and guiding land-use plans.This paper presents a comprehensive review of existing methodologies for fault detection,focusing on the application of Machine Learning(ML)and Deep Learning(DL)techniques to enhance accuracy and efficiency.Various ML and DL approaches are analyzed with respect to fault segmentation,adaptive learning,and fault detection models.These techniques,benchmarked against established seismic datasets,reveal significant improvements over classical methods in terms of accuracy and computational efficiency.Additionally,this review highlights emerging trends,including hybrid model applications and the integration of real-time data processing for seismic fault detection.By providing a detailed comparative analysis of current methodologies,this review aims to guide future research and foster advancements in the effectiveness and reliability of seismic studies.Ultimately,the study seeks to bridge the gap between theoretical investigations and practical implementations in fault detection.
基金funded by the Basic Science Centre Project of the National Natural Science Foundation of China(Grant No.72088101)supported by the Higher Education Commission,Pakistan(Grant No.20-14925/NRPU/R&D/HEC/2021-2021)+1 种基金the Researchers Supporting Project Number(Grant No.RSP2025R351)King Saud University,Riyadh,Saudi Arabia,for funding this research article.
文摘Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approaches sometimes fail to comprehend complex and persistent relationships between pore pressure and formation properties in the heterogeneous reservoirs.This study presents a novel machine learning optimized pore pressure prediction method with a limited dataset,particularly in complex formations.The method addresses the conventional approach's limitations by leveraging its capability to learn complex data relationships.It integrates the best Gradient Boosting Regressor(GBR)algorithm to model pore pressure at wells and later utilizes ContinuousWavelet Transformation(CWT)of the seismic dataset for spatial analysis,and finally employs Deep Neural Network for robust and precise pore pressure modeling for the whole volume.In the second stage,for the spatial variations of pore pressure in the thin Khadro Formation sand reservoir across the entire subsurface area,a three-dimensional pore pressure prediction is conducted using CWT.The relationship between the CWT and geomechanical properties is then established through supervised machine learning models on well locations to predict the uncertainties in pore pressure.Among all intelligent regression techniques developed using petrophysical and elastic properties for pore pressure prediction,the GBR has provided exceptional results that have been validated by evaluation metrics based on the R^(2) score i.e.,0.91 between the calibrated and predicted pore pressure.Via the deep neural network,the relationship between CWT resultant traces and predicted pore pressure is established to analyze the spatial variation.
基金supported by grants from the National Natural Science Foundation of China(No.42004010)the B&R Seismic Monitoring Network Project of the China Earthquake Networks Center(No.5007).
文摘The Belt and Road global navigation satellite system(B&R GNSS)network is the first large-scale deployment of Chinese GNSS equipment in a seismic system.Prior to this,there have been few systematic assessments of the data quality of Chinese GNSS equipment.In this study,data from four representative GNSS sites in different regions of China were analyzed using the G-Nut/Anubis software package.Four main indicators(data integrity rate,data validity ratio,multi-path error,and cycle slip ratio)used to systematically analyze data quality,while evaluating the seismic monitoring capabilities of the network based on earthquake magnitudes estimated from high-frequency GNSS data are evaluated by estimating magnitude based on highfrequency GNSS data.The results indicate that the quality of the data produced by the three types of Chinese receivers used in the network meets the needs of earthquake monitoring and the new seismic industry standards,which provide a reference for the selection of equipment for future new projects.After the B&R GNSS network was established,the seismic monitoring capability for earthquakes with magnitudes greater than M_(W)6.5 in most parts of the Sichuan-Yunnan region improved by approximately 20%.In key areas such as the Sichuan-Yunnan Rhomboid Block,the monitoring capability increased by more than 25%,which has greatly improved the effectiveness of regional comprehensive earthquake management.
基金Supported by the National Natural Science Foundation of China(U24B2031)National Key Research and Development Project(2018YFA0702504)"14th Five-Year Plan"Science and Technology Project of CNOOC(KJGG2022-0201)。
文摘During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resolution seismic data processing technologies and methods tailored for drilling scenarios.The high-resolution processing of seismic data is divided into three stages:pre-drilling processing,post-drilling correction,and while-drilling updating.By integrating seismic data from different stages,spatial ranges,and frequencies,together with information from drilled wells and while-drilling data,and applying artificial intelligence modeling techniques,a progressive high-resolution processing technology of seismic data based on multi-source information fusion is developed,which performs simple and efficient seismic information updates during drilling.Case studies show that,with the gradual integration of multi-source information,the resolution and accuracy of seismic data are significantly improved,and thin-bed weak reflections are more clearly imaged.The updated seismic information while-drilling demonstrates high value in predicting geological bodies ahead of the drill bit.Validation using logging,mud logging,and drilling engineering data ensures the fidelity of the processing results of high-resolution seismic data.This provides clearer and more accurate stratigraphic information for drilling operations,enhancing both drilling safety and efficiency.
基金supported by the National Natural Science Foundation of China(Grant numbers:52174012,52394250,52394255,52234002,U22B20126,51804322).
文摘Formation pore pressure is the foundation of well plan,and it is related to the safety and efficiency of drilling operations in oil and gas development.However,the traditional method for predicting formation pore pressure involves applying post-drilling measurement data from nearby wells to the target well,which may not accurately reflect the formation pore pressure of the target well.In this paper,a novel method for predicting formation pore pressure ahead of the drill bit by embedding petrophysical theory into machine learning based on seismic and logging-while-drilling(LWD)data was proposed.Gated recurrent unit(GRU)and long short-term memory(LSTM)models were developed and validated using data from three wells in the Bohai Oilfield,and the Shapley additive explanations(SHAP)were utilized to visualize and interpret the models proposed in this study,thereby providing valuable insights into the relative importance and impact of input features.The results show that among the eight models trained in this study,almost all model prediction errors converge to 0.05 g/cm^(3),with the largest root mean square error(RMSE)being 0.03072 and the smallest RMSE being 0.008964.Moreover,continuously updating the model with the increasing training data during drilling operations can further improve accuracy.Compared to other approaches,this study accurately and precisely depicts formation pore pressure,while SHAP analysis guides effective model refinement and feature engineering strategies.This work underscores the potential of integrating advanced machine learning techniques with domain-specific knowledge to enhance predictive accuracy for petroleum engineering applications.
基金This research is sponsored by National Natural Science Foundation of China （No. 40272041） and Innovative Foundation of CNPC （N0. 04E702）.
文摘In multi-component seismic exploration, the horizontal and vertical components both contain P- and SV-waves. The P- and SV-wavefields in a seismic record can be separated by their horizontal and vertical displacements when upgoing P- and SV-waves arrive at the sea floor. If the sea floor P wave velocity, S wave velocity, and density are known, the separation can be achieved in ther-p domain. The separated wavefields are then transformed to the time domain. A method of separating P- and SV-wavefields is presented in this paper and used to effectively separate P- and SV-wavefields in synthetic and real data. The application to real data shows that this method is feasible and effective. It also can be used for free surface data.
基金funded by the National Natural Science Foundation of China(General Program:No.52074314,No.U19B6003-05)National Key Research and Development Program of China(2019YFA0708303-05)。
文摘Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.
基金Supported by the CNPC Science and Technology Projects(2022-N/G-47808,2023-N/G-67014)RIPED International Cooperation Project(19HTY5000008).
文摘To solve the problems in restoring sedimentary facies and predicting reservoirs in loose gas-bearing sediment,based on seismic sedimentologic analysis of the first 9-component S-wave 3D seismic dataset of China,a fourth-order isochronous stratigraphic framework was set up and then sedimentary facies and reservoirs in the Pleistocene Qigequan Formation in Taidong area of Qaidam Basin were studied by seismic geomorphology and seismic lithology.The study method and thought are as following.Firstly,techniques of phase rotation,frequency decomposition and fusion,and stratal slicing were applied to the 9-component S-wave seismic data to restore sedimentary facies of major marker beds based on sedimentary models reflected by satellite images.Then,techniques of seismic attribute extraction,principal component analysis,and random fitting were applied to calculate the reservoir thickness and physical parameters of a key sandbody,and the results are satisfactory and confirmed by blind testing wells.Study results reveal that the dominant sedimentary facies in the Qigequan Formation within the study area are delta front and shallow lake.The RGB fused slices indicate that there are two cycles with three sets of underwater distributary channel systems in one period.Among them,sandstones in the distributary channels of middle-low Qigequan Formation are thick and broad with superior physical properties,which are favorable reservoirs.The reservoir permeability is also affected by diagenesis.Distributary channel sandstone reservoirs extend further to the west of Sebei-1 gas field,which provides a basis to expand exploration to the western peripheral area.
基金National Natural Science Foundations of China(No.71501103)Natural Science Foundation of Inner Mongolia,China(No.2015BS0705)the Program of Higher-Level Talents of Inner Mongolia University,China(No.20700-5145131)
文摘Complex repairable system is composed of thousands of components.Some maintenance management and decision problems in maintenance management and decision need to classify a set of components into several classes based on data mining.Furthermore,with the complexity of industrial equipment increasing,the managers should pay more attention to the key components and carry out the lean management is very important.Therefore,the idea"customer segmentation"of"precise marketing"can be used in the maintenance management of the multi-component system.Following the idea of segmentation,the components of multicomponent systems should be subdivied into groups based on specific attributes relevant to maintenance,such as maintenance cost,mean time between failures,and failure frequency.For the target specific groups of parts,the optimal maintenance policy,health assessment and maintenance scheduling can be determined.The proposed analysis framework will be given out.In order to illustrate the effectiveness of this method,a numerical example is given out.
文摘Geophysicists interpreting seismic reflection data aim for the highest resolution possible as this facilitates the interpretation and discrimination of subtle geological features.Various deterministic methods based on Wiener filtering exist to increase the temporal frequency bandwidth and compress the seismic wavelet in a process called spectral shaping.Auto-encoder neural networks with convolutional layers have been applied to this problem,with encouraging results,but the problem of generalization to unseen data remains.Most published works have used supervised learning with training data constructed from field seismic data or synthetic seismic data generated based on measured well logs or based on seismic wavefield modelling.This leads to satisfactory results on datasets similar to the training data but requires re-training of the networks for unseen data with different characteristics.In this work seek to improve the generalization,not by experimenting with network architecture(we use a conventional U-net with some small modifications),but by adopting a different approach to creating the training data for the supervised learning process.Although the network is important,at this stage of development we see more improvement in prediction results by altering the design of the training data than by architectural changes.The approach we take is to create synthetic training data consisting of simple geometric shapes convolved with a seismic wavelet.We created a very diverse training dataset consisting of 9000 seismic images with between 5 and 300 seismic events resembling seismic reflections that have geophysically motived perturbations in terms of shape and character.The 2D U-net we have trained can boost robustly and recursively the dominant frequency by 50%.We demonstrate this on unseen field data with different bandwidths and signal-to-noise ratios.Additionally,this 2D U-net can handle non-stationary wavelets and overlapping events of different bandwidth without creating excessive ringing.It is also robust in the presence of noise.The significance of this result is that it simplifies the effort of bandwidth extension and demonstrates the usefulness of auto-encoder neural network for geophysical data processing.
基金supported by the National Natural Science Foundation of China(Research on Dynamic Location of Receiving Points and Wave Field Separation Technology Based on Deep Learning in OBN Seismic Exploration,No.42074140)the Sinopec Geophysical Corporation,Project of OBC/OBN Seismic Data Wave Field Characteristics Analysis and Ghost Wave Suppression(No.SGC-202206)。
文摘The use of blended acquisition technology in marine seismic exploration has the advantages of high acquisition efficiency and low exploration costs.However,during acquisition,the primary source may be disturbed by adjacent sources,resulting in blended noise that can adversely affect data processing and interpretation.Therefore,the de-blending method is needed to suppress blended noise and improve the quality of subsequent processing.Conventional de-blending methods,such as denoising and inversion methods,encounter challenges in parameter selection and entail high computational costs.In contrast,deep learning-based de-blending methods demonstrate reduced reliance on manual intervention and provide rapid calculation speeds post-training.In this study,we propose a Uformer network using a nonoverlapping window multihead attention mechanism designed for de-blending blended data in the common shot domain.We add the depthwise convolution to the feedforward network to improve Uformer’s ability to capture local context information.The loss function comprises SSIM and L1 loss.Our test results indicate that the Uformer outperforms convolutional neural networks and traditional denoising methods across various evaluation metrics,thus highlighting the effectiveness and advantages of Uformer in de-blending blended data.
基金financially supported by National 863 Program (Grants No.2006AA 09A 102-09)National Science and Technology of Major Projects ( Grants No.2008ZX0 5025-001-001)
文摘Irregular seismic data causes problems with multi-trace processing algorithms and degrades processing quality. We introduce the Projection onto Convex Sets （POCS） based image restoration method into the seismic data reconstruction field to interpolate irregularly missing traces. For entire dead traces, we transfer the POCS iteration reconstruction process from the time to frequency domain to save computational cost because forward and reverse Fourier time transforms are not needed. In each iteration, the selection threshold parameter is important for reconstruction efficiency. In this paper, we designed two types of threshold models to reconstruct irregularly missing seismic data. The experimental results show that an exponential threshold can greatly reduce iterations and improve reconstruction efficiency compared to a linear threshold for the same reconstruction result. We also analyze the anti- noise and anti-alias ability of the POCS reconstruction method. Finally, theoretical model tests and real data examples indicate that the proposed method is efficient and applicable.
基金supported by the National Natural Science Foundation of China(Nos.41506046,41376060,41706054)the Opening Foundation of Key Laboratory of Ocean and Marginal Sea Geology,CAS(No.MSGL15-05)+1 种基金WPOS(No.XDA11030102-02)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA13010101)
文摘The Pearl River Estuary(PRE) is located at the onshore-offshore transition zone between South China and South China Sea Basin, and it is of great significant value in discussing tectonic relationships between South China block and South China Sea block and seismic activities along the offshore active faults in PRE. However, the researches on geometric characteristics of offshore faults in this area are extremely lacking. To investigate the offshore fault distribution and their geometric features in the PRE in greater detail, we acquired thirteen seismic reflection profiles in 2015. Combining the analysis of the seismic reflection and free-air gravity anomaly data, this paper revealed the location, continuity, and geometry of the littoral fault zone and other offshore faults in PRE. The littoral fault zone is composed of the major Dangan Islands fault and several parallel, high-angle, normal faults, which mainly trend northeast to northeast-to-east and dip to the southeast with large displacements. The fault zone is divided into three different segments by the northwest-trending faults. Moreover, the basement depth around Dangan Islands is very shallow, while it suddenly increases along the islands westward and southward. These has resulted in the islands and neighboring areas becoming the places where the stress accumulates easily. The seismogenic pattern of this area is closely related to the comprehensive effect of intersecting faults together with the low velocity layer.
基金Mainly presented at the 6-th international meeting of acoustics in Aug. 2003, and The 1999 SPE Asia Pacific Oil and GasConference and Exhibition held in Jakarta, Indonesia, 20-22 April 1999, SPE 54274.
文摘In this paper, a new concept called numerical structure of seismic data is introduced and the difference between numerical structure and numerical value of seismic data is explained. Our study shows that the numerical seismic structure is closely related to oil and gas-bearing reservoir, so it is very useful for a geologist or a geophysicist to precisely interpret the oil-bearing layers from the seismic data. This technology can be applied to any exploration or production stage. The new method has been tested on a series of exploratory or development wells and proved to be reliable in China. Hydrocarbon-detection with this new method for 39 exploration wells on 25 structures indi- cates a success ratio of over 80 percent. The new method of hydrocarbon prediction can be applied for: (1) depositional environment of reservoirs with marine fades, delta, or non-marine fades (including fluvial facies, lacustrine fades); (2) sedimentary rocks of reservoirs that are non-marine clastic rocks and carbonate rock; and (3) burial depths range from 300 m to 7000 m, and the minimum thickness of these reservoirs is over 8 m (main frequency is about 50 Hz).
基金Funded by the Project of China Geological Survey (No.1212010916040)the Sichuan Science and Technology Program (No.2017JY0051)the Sichuan Science and Technology Program (No.2018GZ0200)
文摘The field seismic data is disturbed by the interferential information, which has low signal to noise ratio (SNR). That is disadvantage for seismic data interpretation. So it is important to remove the noise of seismic data. Independent component analysis (ICA) can remove most of the noise interference. However, ICA has some defects in noise reduction, because it needs some conditions that seismic data is independent reciprocally for denoising. To solve these defects, this paper proposes an improved ICA algorithm to noise reduction. Through simulation experiments, it can be obtained that the best decomposition levels of the new algorithm is 3. At last, the proposed improved ICA is applied to deal with the actual seismic data. The results show that it can effectively eliminate most of seismic noise such as random noise, linear interference, surface waves, and so on. The improved ICA is not only easy to denoising, but also has excellent mathematical theoretical properties.
文摘A new method is introduced to suppress the noise in seismic data processing. Based on the subtle difference in shape between the noise and the actual signal, we introduce morphologic filtering into seismic data processing. From the shape and the S/N we can see that the effect of morphologic filtering is superior to other methods like id-value filtering, neighbor average filtering, etc. The SNR of the signal after morphological filtering is comparatively great. In addition, the precision of the seismic data after morphological filtering is high. The characteristics of the actual signal, such as frequency and amplitude, are preserved. We give an example of the real seismic data processing using morphological filtering, in which the actual signal is retained, while the random high intensity noise was removed.
文摘In this paper, multi-scaled morphology is introduced into the digital processing domain for land seismic data. First, we describe the basic theory of multi-scaled morphology image decomposition of exploration seismic waves; second, we illustrate how to use multi-scaled morphology for seismic data processing using two real examples. The first example demonstrates suppressing the surface waves in pre-stack seismic records using multi-scaled morphology decomposition and reconstitution and the other example demonstrates filtering different interference waves on the seismic record. Multi-scaled morphology filtering separates signal from noise by the detailed differences of the wave shapes. The successful applications suggest that multi-scaled morphology has a promising application in seismic data processing.
