Evaluating the physical mechanisms that link hydraulic fracturing(HF) operations to induced earthquakes and the anticipated form of the resulting events is significant in informing subsurface fluid injection operation...Evaluating the physical mechanisms that link hydraulic fracturing(HF) operations to induced earthquakes and the anticipated form of the resulting events is significant in informing subsurface fluid injection operations. Current understanding supports the overriding role of the effective stress magnitude in triggering earthquakes, while the impact of change rate of effective stress has not been systematically addressed. In this work, a modified critical stiffness was brought up to investigate the likelihood, impact,and mitigation of induced seismicity during and after hydraulic fracturing by developing a poroelastic model based on rate-and-state fraction law and linear stability analysis. In the new criterion, the change rate of effective stress was considered a key variable to explore the evolution of this criterion and hence the likelihood of instability slip of fault. A coupled fluid flow-deformation model was used to represent the entire hydraulic fracturing process in COMSOL Multiphysics. The possibility of triggering an earthquake throughout the entire hydraulic fracturing process, from fracturing to cessation, was investigated considering different fault locations, orientations, and positions along the fault. The competition between the effects of the magnitude and change rate of effective stress was notable at each fracturing stage. The effective stress magnitude is a significant controlling factor during fracturing events, with the change rate dominating when fracturing is suddenly started or stopped. Instability dominates when the magnitude of the effective stress increases(constant injection at each fracturing stage) and the change rate of effective stress decreases(the injection process is suddenly stopped). Fracturing with a high injection rate, a fault adjacent to the hydraulic fracturing location and the position of the junction between the reservoir and fault are important to reduce the Coulomb failure stress(CFS) and enhance the critical stiffness as the significant disturbance of stresses at these positions in the coupled process. Therefore,notable attention should be given to the injection rate during fracturing, fault position, and position along faults as important considerations to help reduce the potential for induced seismicity. Our model was verified and confirmed using the case of the Longmaxi Formation in the Sichuan Basin, China, in which the reported microseismic data were correlated with high critical stiffness values. This work supplies new thoughts of the seismic risk associated with HF engineering.展开更多
A comprehensive dataset from 594 fracturing wells throughout the Duvernay Formation near Fox Creek, Alberta, is collected to quantify the influences of geological, geomechanical, and operational features on the distri...A comprehensive dataset from 594 fracturing wells throughout the Duvernay Formation near Fox Creek, Alberta, is collected to quantify the influences of geological, geomechanical, and operational features on the distribution and magnitude of hydraulic fracturing-induced seismicity. An integrated machine learning-based investigation is conducted to systematically evaluate multiple factors that contribute to induced seismicity. Feature importance indicates that a distance to fault, a distance to basement, minimum principal stress, cumulative fluid injection, initial formation pressure, and the number of fracturing stages are among significant model predictors. Our seismicity prediction map matches the observed spatial seismicity, and the prediction model successfully guides the fracturing job size of a new well to reduce seismicity risks. This study can apply to mitigating potential seismicity risks in other seismicity-frequent regions.展开更多
This paper explores possible synergies between techniques used to minimise seismicity in deep South African gold mines and their applicability to control coal bumps. The paper gives a summary of the techniques used in...This paper explores possible synergies between techniques used to minimise seismicity in deep South African gold mines and their applicability to control coal bumps. The paper gives a summary of the techniques used in the deep gold mines and a critical appraisal if these are useful in coal mines. The techniques typically include control of mining rate, preconditioning, optimisation of extraction sequences and centralised blasting. Of particular interest to the coal bump problem is an experimental limit equilibrium fracture zone model implemented in a displacement discontinuity code. This was recently developed for the gold mines to enable the interactive analysis of complex tabular mine layout extraction sequences. The model specifically accommodates energy dissipation computations in the developing fracture zone near the edges of these excavations. This allows the released energy to be used as a surrogate measure of ongoing seismic activity and addresses a number of the weaknesses in the traditional usage of this quantity as a criterion for the design of seismically active layouts. This paper investigates the application of the model to a hypothetical coal longwall layout and the specific problem of coal bumps.展开更多
The challenge of estimating mine-wide subsidence and linkages to seismicity over tabular deposits is addressed by a special finite element technique(dual node–dual mesh). Subsidence and mine-induced seismicity begins...The challenge of estimating mine-wide subsidence and linkages to seismicity over tabular deposits is addressed by a special finite element technique(dual node–dual mesh). Subsidence and mine-induced seismicity begins near the face when caving occurs and propagates to the surface as extraction reaches a critical extent. Thus, the challenge is to obtain details at the face at the meter scale and also at the surface over the whole mine at the kilometer scale. Interactions between old and new sections of a mine are automatically taken into account with this technique. The finite element method is well established technology based on fundamentals of physical laws, kinematics and material laws. With this technique, no empirical ‘‘scaling" or fitting computer output by input data ‘‘adjustment" to mine measurements is necessary. Capability is demonstrated for doing practical whole-mine subsidence analysis from first principles. Mine-induced seismicity is shown to correlate well with face advance and element failure.展开更多
The frequency-size distribution of model earthquakes in a one-dimensional Burridge-Knopoff spring-block model[Phys.Rev.Lett,22(1989)2632]is studied.Analytical approach is taken to avoid the limitations on the size of ...The frequency-size distribution of model earthquakes in a one-dimensional Burridge-Knopoff spring-block model[Phys.Rev.Lett,22(1989)2632]is studied.Analytical approach is taken to avoid the limitations on the size of models and computation time in numerical calculations.By using the Hamiltonian of the spring-block system a concept of‘seismic phonon’is proposed which is similar to the concept of phonon in solid state physics.The result of the occurrence frequency of model earthquakes shows a distribution similar to the modified gamma distribution proposed by Main and Burton using information theory.展开更多
Multistage hydraulic fracturing of horizontal wells(MFHW)is a promising technology for controlling coal burst caused by thick and hard roofs in China.However,challenges remain regarding the MFHW control mechanism of c...Multistage hydraulic fracturing of horizontal wells(MFHW)is a promising technology for controlling coal burst caused by thick and hard roofs in China.However,challenges remain regarding the MFHW control mechanism of coal burst and assessment of the associated fracturing effects.In this study,these challenges were investigated through numerical modelling and field applications,based on the actual operating parameters of MFHW for hard roofs in a Chinese coal mine.A damage parameter(D)is proposed to assess the degree of hydraulic fracturing in the roof.The mechanisms and effects of MFHW for controlling coal burst are analyzed using microseismic(MS)data and front-abutment stress distribution.Results show that the degree of fracturing can be categorized into lightly-fractured(D≤0.3),moderately fractured(0.3<D≤0.6),well-fractured(0.6<D≤0.9),and over-fractured(0.9<D≤0.95).A response stage in the fracturing process,characterized by a slowdown in crack development,indicates the transition to a wellfractured condition.After MFHW,the zone range and peak value of the front-abutment stress decrease.Additionally,MS events shift from near the coal seam to the fractured roof layers,with the number of MS events increases while the average MS energy decreases.The MFHW control mechanisms of coal bursts involve mitigating mining-induced stress and reducing seismic activity during longwall retreat,ensuring stresses remain below the ultimate stress level.These findings provide a reference for evaluating MFHW fracturing effects and controlling coal burst disasters in engineering.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
The Bozhong Sag is the largest petroliferous sag in the Bohai Bay Basin,and the source rocks of Paleogene Dongying and Shahejie Formations were buried deeply.Most of the drillings were located at the structural high,a...The Bozhong Sag is the largest petroliferous sag in the Bohai Bay Basin,and the source rocks of Paleogene Dongying and Shahejie Formations were buried deeply.Most of the drillings were located at the structural high,and there were few wells that met good quality source rocks,so it is difficult to evaluate the source rocks in the study area precisely by geochemical analysis only.Based on the Rock-Eval pyrolysis,total organic carbon(TOC)testing,the organic matter(OM)abundance of Paleogene source rocks in the southwestern Bozhong Sag were evaluated,including the lower of second member of Dongying Formation(E_(3)d2L),the third member of Dongying Formation(E_(3)d_(3)),the first and second members of Shahejie Formation(E_(2)s_(1+2)),the third member of Shahejie Formation(E_(2)s_(3)).The results indicate that the E_(2)s_(1+2)and E_(2)s_(3)have better hydrocarbon generative potentials with the highest OM abundance,the E_(3)d_(3)are of the second good quality,and the E_(3)d2L have poor to fair hydrocarbon generative potential.Furthermore,the well logs were applied to predict TOC and residual hydrocarbon generation potential(S_(2))based on the sedimentary facies classification,usingΔlogR,generalizedΔlogR,logging multiple linear regression and BP neural network methods.The various methods were compared,and the BP neural network method have relatively better prediction accuracy.Based on the pre-stack simultaneous inversion(P-wave impedance,P-wave velocity and density inversion results)and the post-stack seismic attributes,the three-dimensional(3D)seismic prediction of TOC and S_(2)was carried out.The results show that the seismic near well prediction results of TOC and S_(2)based on seismic multi-attributes analysis correspond well with the results of well logging methods,and the plane prediction results are identical with the sedimentary facies map in the study area.The TOC and S_(2)values of E_(2)s_(1+2)and E_(2)s_(3)are higher than those in E_(3)d_(3)and E_(3)d_(2)L,basically consistent with the geochemical analysis results.This method makes up the deficiency of geochemical methods,establishing the connection between geophysical information and geochemical data,and it is helpful to the 3D quantitative prediction and the evaluation of high-quality source rocks in the areas where the drillings are limited.展开更多
Forward modeling of seismic wave propagation is crucial for the realization of reverse time migration(RTM) and full waveform inversion(FWI) in attenuating transversely isotropic media. To describe the attenuation and ...Forward modeling of seismic wave propagation is crucial for the realization of reverse time migration(RTM) and full waveform inversion(FWI) in attenuating transversely isotropic media. To describe the attenuation and anisotropy properties of subsurface media, the pure-viscoacoustic anisotropic wave equations are established for wavefield simulations, because they can provide clear and stable wavefields. However, due to the use of several approximations in deriving the wave equation and the introduction of a fractional Laplacian approximation in solving the derived equation, the wavefields simulated by the previous pure-viscoacoustic tilted transversely isotropic(TTI) wave equations has low accuracy. To accurately simulate wavefields in media with velocity anisotropy and attenuation anisotropy, we first derive a new pure-viscoacoustic TTI wave equation from the exact complex-valued dispersion formula in viscoelastic vertical transversely isotropic(VTI) media. Then, we present the hybrid finite-difference and low-rank decomposition(HFDLRD) method to accurately solve our proposed pure-viscoacoustic TTI wave equation. Theoretical analysis and numerical examples suggest that our pure-viscoacoustic TTI wave equation has higher accuracy than previous pure-viscoacoustic TTI wave equations in describing q P-wave kinematic and attenuation characteristics. Additionally, the numerical experiment in a simple two-layer model shows that the HFDLRD technique outperforms the hybrid finite-difference and pseudo-spectral(HFDPS) method in terms of accuracy of wavefield modeling.展开更多
With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural network...With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.展开更多
High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher reso...High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher resolution seismic images in petroleum exploration. Although there have been successful applications of conventional signal processing and machine learning for post-stack seismic resolution enhancement,there is limited reference to the seismic applications of the recent emergence and rapid development of generative artificial intelligence. Hence, we propose to apply diffusion models, among the most popular generative models, to enhance seismic resolution. Specifically, we apply the classic diffusion model—denoising diffusion probabilistic model(DDPM), conditioned on the seismic data in low resolution, to reconstruct corresponding high-resolution images. Herein the entire scheme is referred to as SeisResoDiff. To provide a comprehensive and clear understanding of SeisResoDiff, we introduce the basic theories of diffusion models and detail the optimization objective's derivation with the aid of diagrams and algorithms. For implementation, we first propose a practical workflow to acquire abundant training data based on the generated pseudo-wells. Subsequently, we apply the trained model to both synthetic and field datasets, evaluating the results in three aspects: the appearance of seismic sections and slices in the time domain, frequency spectra, and comparisons with the synthetic data using real well-logging data at the well locations. The results demonstrate not only effective seismic resolution enhancement,but also additional denoising by the diffusion model. Experimental comparisons indicate that training the model on noisy data, which are more realistic, outperforms training on clean data. The proposed scheme demonstrates superiority over some conventional methods in high-resolution reconstruction and denoising ability, yielding more competitive results compared to our previous research.展开更多
(Multichannel)Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression.It utilizes the low-rank feature of seismic signal and regards the noise suppression...(Multichannel)Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression.It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem.However,in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers.The presence of this kind of noise,called erratic noise,makes singular spectrum analysis(SSA)reconstruction unstable and has undesirable effects on the final results.We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA(RD-SSA)method.It incorporates the damped SSA,an improved version of SSA,into a reweighted framework.The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise.The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations.The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance.The feasibility of RD-SSA is validated via both synthetic and field data examples.展开更多
To ensure project safety and secure public support, an integrated and comprehensive monitoring program is needed within a carbon capture and storage(CCS) project. Monitoring can be done using many well-established tec...To ensure project safety and secure public support, an integrated and comprehensive monitoring program is needed within a carbon capture and storage(CCS) project. Monitoring can be done using many well-established techniques from various fields, and the seismic method proves to be the crucial one. This method is widely used to determine the CO_(2) distribution, image the plume development, and quantitatively estimate the concentration. Because both the CO_(2) distribution and the potential migration pathway can be spatially small scale, high resolution for seismic imaging is demanded. However, obtaining a high-resolution image of a subsurface structure in marine settings is difficult. Herein, we introduce the novel Hcable(Harrow-like cable system) technique, which may be applied to offshore CCS monitoring. This technique uses a highfrequency source(the dominant frequency>100 Hz) to generate seismic waves and a combination of a long cable and several short streamers to receive seismic waves. Ultrahigh-frequency seismic images are achieved through the processing of Hcable seismic data. Hcable is then applied in a case study to demonstrate its detailed characterization for small-scale structures. This work reveals that Hcable is a promising tool for timelapse seismic monitoring of oceanic CCS.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
The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-nois...The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio(SNR)situations.To address this issue,we propose a regression approach to pick FBs based on bidirectional long short-term memory(Bi LSTM)neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region.We employ a regressive model that represents the relationships among the elevation of shots,offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs,from common-shot gathers with sparsely distributed traces.Different from image segmentation methods which automatically extract image features and classify FBs from seismic data,the proposed method can learn the inner relationship between field geometry and FBs.In addition,the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution.The picking results of synthetic data shows that the proposed method has low dependence on label data,and can obtain reliable and similar predicted results using two types of label data with large differences.The picking results of9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data.The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method.The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.展开更多
Seismic finite-difference(FD) modeling suffers from numerical dispersion including both the temporal and spatial dispersion, which can decrease the accuracy of the numerical modeling. To improve the accuracy and effic...Seismic finite-difference(FD) modeling suffers from numerical dispersion including both the temporal and spatial dispersion, which can decrease the accuracy of the numerical modeling. To improve the accuracy and efficiency of the conventional numerical modeling, I develop a new seismic modeling method by combining the FD scheme with the numerical dispersion suppression neural network(NDSNN). This method involves the following steps. First, a training data set composed of a small number of wavefield snapshots is generated. The wavefield snapshots with the low-accuracy wavefield data and the high-accuracy wavefield data are paired, and the low-accuracy wavefield snapshots involve the obvious numerical dispersion including both the temporal and spatial dispersion. Second, the NDSNN is trained until the network converges to simultaneously suppress the temporal and spatial dispersion.Third, the entire set of low-accuracy wavefield data is computed quickly using FD modeling with the large time step and the coarse grid. Fourth, the NDSNN is applied to the entire set of low-accuracy wavefield data to suppress the numerical dispersion including the temporal and spatial dispersion.Numerical modeling examples verify the effectiveness of my proposed method in improving the computational accuracy and efficiency.展开更多
Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production exp...Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.展开更多
Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficul...Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.展开更多
With the theoretical and technological developments related to cratonic strike-slip faults,the Shuntuoguole Low Uplift in the Tarim Basin has attracted considerable attention recently.Affected by multi-stage tectonic ...With the theoretical and technological developments related to cratonic strike-slip faults,the Shuntuoguole Low Uplift in the Tarim Basin has attracted considerable attention recently.Affected by multi-stage tectonic movements,the strike-slip faults have controlled the distribution of hydrocarbon resources owing to the special fault characteristics and fault-related structures.In contrast,the kinematics and formation mechanism of strike-slip faults in buried sedimentary basins are difficult to investigate,limiting the discussion of these faults and hydrocarbon accumulation.In this study,we identified the characteristics of massive sigmoidal tension gashes(STGs)that formed in the Shunnan area of the Tarim Basin.High-resolution three-dimensional seismic data and attribute analyses were used to investigate their geometric and kinematic characteristics.Then,the stress state of each point of the STGs was calculated using seismic curvature attributes.Finally,the formation mechanism of the STGs and their roles in controlling hydrocarbon migration and accumulation were discussed.The results suggest that:(1)the STGs developed in the Shunnan area have a wide distribution,with a tensile fault arranged in an enéchelon pattern,showing an S-shaped bending.These STGs formed in multiple stages,and differential rotation occurred along the direction of strike-slip stress during formation.(2)Near the principal displacement zone of the strike-slip faults,the stress value of the STGs was higher,gradually decreasing at both ends.The shallow layer deformation was greater than the deep layer deformation.(3)STGs are critical for connecting source rocks,migrating oil and gas,sealing horizontally,and developing efficient reservoirs.This study not only provides seismic evidence for the formation and evolution of super large STGs,but also provides certain guidance for oil and gas exploration in this area.展开更多
The deep-ultra deep carbonate reservoir in China,commonly subjected to modification of multi-stage diagenesis,has extremely high heterogeneity.Conventional rock physics analysis cannot accurately identify the elastic ...The deep-ultra deep carbonate reservoir in China,commonly subjected to modification of multi-stage diagenesis,has extremely high heterogeneity.Conventional rock physics analysis cannot accurately identify the elastic responses of reservoir.Here,the rock physics properties of the dolomite from the 4th Member of the Sinian Dengying Formation are experimentally measured,and the change law of rock physics characteristics is investigated within the framework of the diagenetic processes by the analysis of the elastic and petrologic characteristics,pore structure,and sedimentary environments.The results show that the differentiated diagenesis results in different pore structure characteristics and microtexture characteristics of the rock.The microbial dolomite of the algal mound-grain beach facies is subjected to the contemporaneous microbial dolomitization and seepage-reflux dolomitization,penecontemporaneous selective dissolution,burial dolomitization,and hydrothermal dolomitization.The resultant crystalline dolomite is found with one main type of the dolomite crystal contact boundaries,and the dissolution pore is extensive development.The siliceous,muddy,and limy dolomite of the interbeach sea environment mainly experiences the weak capillary concentration dolomitization,intensive mechanical compaction-induced densification,and burial dolomitization.Such crystalline dolomite is observed with four types of contact boundaries,namely the dolomite contact,clay contact,quartz contact,and calcite contact boundaries,and porosity mostly attributed to residual primary inter-granular or crystalline pores.The samples with the same crystal boundary condition have consistent correlations between the compressional-and shear-wave velocities,and between the compressional-wave velocity and the velocity ratio.Additionally,the variation of the acoustic velocity with effective pressure and the intensity of pore-scale fluid-related dispersion are controlled by the differentiation of pore structure types of the samples.The varied effects of soft pores like micro-cracks on the compressional-and shearwave velocity causes considerable changes in the relationships between the compressional-and shearwave velocities,compressional-wave velocity and velocity ratio,and porosity and acoustic velocity.This research is an attempt to demonstrate a novel method for investigating the rock physics variation of rock during the geological process,and the obtained findings can provide the rock physics basis for seismic prediction of the characteristics of deep carbonate reservoirs.展开更多
基金funded by the joint fund of the National Key Research and Development Program of China(No.2021YFC2902101)National Natural Science Foundation of China(Grant No.52374084)+1 种基金Open Foundation of National Energy shale gas R&D(experiment) center(2022-KFKT-12)the 111 Project(B17009)。
文摘Evaluating the physical mechanisms that link hydraulic fracturing(HF) operations to induced earthquakes and the anticipated form of the resulting events is significant in informing subsurface fluid injection operations. Current understanding supports the overriding role of the effective stress magnitude in triggering earthquakes, while the impact of change rate of effective stress has not been systematically addressed. In this work, a modified critical stiffness was brought up to investigate the likelihood, impact,and mitigation of induced seismicity during and after hydraulic fracturing by developing a poroelastic model based on rate-and-state fraction law and linear stability analysis. In the new criterion, the change rate of effective stress was considered a key variable to explore the evolution of this criterion and hence the likelihood of instability slip of fault. A coupled fluid flow-deformation model was used to represent the entire hydraulic fracturing process in COMSOL Multiphysics. The possibility of triggering an earthquake throughout the entire hydraulic fracturing process, from fracturing to cessation, was investigated considering different fault locations, orientations, and positions along the fault. The competition between the effects of the magnitude and change rate of effective stress was notable at each fracturing stage. The effective stress magnitude is a significant controlling factor during fracturing events, with the change rate dominating when fracturing is suddenly started or stopped. Instability dominates when the magnitude of the effective stress increases(constant injection at each fracturing stage) and the change rate of effective stress decreases(the injection process is suddenly stopped). Fracturing with a high injection rate, a fault adjacent to the hydraulic fracturing location and the position of the junction between the reservoir and fault are important to reduce the Coulomb failure stress(CFS) and enhance the critical stiffness as the significant disturbance of stresses at these positions in the coupled process. Therefore,notable attention should be given to the injection rate during fracturing, fault position, and position along faults as important considerations to help reduce the potential for induced seismicity. Our model was verified and confirmed using the case of the Longmaxi Formation in the Sichuan Basin, China, in which the reported microseismic data were correlated with high critical stiffness values. This work supplies new thoughts of the seismic risk associated with HF engineering.
基金This research has been made possible by contributions from the Natural Sciences and Engineering Research Council(NSERC)/Energi Simulation Industrial Research Chair in Reservoir Simulation and the Alberta Innovates(iCore)Chair in Reservoir ModelingThis research was supported by the Science Foundation of China University of Petroleum,Beijing(No.2462023BJRC001)the National Natural Science Foundation of China Joint Fund Key Support Project(No.U19B6003).
文摘A comprehensive dataset from 594 fracturing wells throughout the Duvernay Formation near Fox Creek, Alberta, is collected to quantify the influences of geological, geomechanical, and operational features on the distribution and magnitude of hydraulic fracturing-induced seismicity. An integrated machine learning-based investigation is conducted to systematically evaluate multiple factors that contribute to induced seismicity. Feature importance indicates that a distance to fault, a distance to basement, minimum principal stress, cumulative fluid injection, initial formation pressure, and the number of fracturing stages are among significant model predictors. Our seismicity prediction map matches the observed spatial seismicity, and the prediction model successfully guides the fracturing job size of a new well to reduce seismicity risks. This study can apply to mitigating potential seismicity risks in other seismicity-frequent regions.
文摘This paper explores possible synergies between techniques used to minimise seismicity in deep South African gold mines and their applicability to control coal bumps. The paper gives a summary of the techniques used in the deep gold mines and a critical appraisal if these are useful in coal mines. The techniques typically include control of mining rate, preconditioning, optimisation of extraction sequences and centralised blasting. Of particular interest to the coal bump problem is an experimental limit equilibrium fracture zone model implemented in a displacement discontinuity code. This was recently developed for the gold mines to enable the interactive analysis of complex tabular mine layout extraction sequences. The model specifically accommodates energy dissipation computations in the developing fracture zone near the edges of these excavations. This allows the released energy to be used as a surrogate measure of ongoing seismic activity and addresses a number of the weaknesses in the traditional usage of this quantity as a criterion for the design of seismically active layouts. This paper investigates the application of the model to a hypothetical coal longwall layout and the specific problem of coal bumps.
基金Financial support for‘‘Analysis of Mine Seismicity and Geotechnical Modeling for Improved Safety in Underground Coal Mines”by NIOSH/CDC
文摘The challenge of estimating mine-wide subsidence and linkages to seismicity over tabular deposits is addressed by a special finite element technique(dual node–dual mesh). Subsidence and mine-induced seismicity begins near the face when caving occurs and propagates to the surface as extraction reaches a critical extent. Thus, the challenge is to obtain details at the face at the meter scale and also at the surface over the whole mine at the kilometer scale. Interactions between old and new sections of a mine are automatically taken into account with this technique. The finite element method is well established technology based on fundamentals of physical laws, kinematics and material laws. With this technique, no empirical ‘‘scaling" or fitting computer output by input data ‘‘adjustment" to mine measurements is necessary. Capability is demonstrated for doing practical whole-mine subsidence analysis from first principles. Mine-induced seismicity is shown to correlate well with face advance and element failure.
基金Supported by the National Natural Science Foundation of China under Grant No.49725410。
文摘The frequency-size distribution of model earthquakes in a one-dimensional Burridge-Knopoff spring-block model[Phys.Rev.Lett,22(1989)2632]is studied.Analytical approach is taken to avoid the limitations on the size of models and computation time in numerical calculations.By using the Hamiltonian of the spring-block system a concept of‘seismic phonon’is proposed which is similar to the concept of phonon in solid state physics.The result of the occurrence frequency of model earthquakes shows a distribution similar to the modified gamma distribution proposed by Main and Burton using information theory.
基金financial support for this work provided by the National Natural Science Foundation of China(Nos.52274147,52374101,and 32111530138)the Jiangsu Province Basic Research Special Fund-Soft Science Research(No.BZ2024024)the State Key Research Development Program of China(No.2022YFC3004603).
文摘Multistage hydraulic fracturing of horizontal wells(MFHW)is a promising technology for controlling coal burst caused by thick and hard roofs in China.However,challenges remain regarding the MFHW control mechanism of coal burst and assessment of the associated fracturing effects.In this study,these challenges were investigated through numerical modelling and field applications,based on the actual operating parameters of MFHW for hard roofs in a Chinese coal mine.A damage parameter(D)is proposed to assess the degree of hydraulic fracturing in the roof.The mechanisms and effects of MFHW for controlling coal burst are analyzed using microseismic(MS)data and front-abutment stress distribution.Results show that the degree of fracturing can be categorized into lightly-fractured(D≤0.3),moderately fractured(0.3<D≤0.6),well-fractured(0.6<D≤0.9),and over-fractured(0.9<D≤0.95).A response stage in the fracturing process,characterized by a slowdown in crack development,indicates the transition to a wellfractured condition.After MFHW,the zone range and peak value of the front-abutment stress decrease.Additionally,MS events shift from near the coal seam to the fractured roof layers,with the number of MS events increases while the average MS energy decreases.The MFHW control mechanisms of coal bursts involve mitigating mining-induced stress and reducing seismic activity during longwall retreat,ensuring stresses remain below the ultimate stress level.These findings provide a reference for evaluating MFHW fracturing effects and controlling coal burst disasters in engineering.
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.
文摘The Bozhong Sag is the largest petroliferous sag in the Bohai Bay Basin,and the source rocks of Paleogene Dongying and Shahejie Formations were buried deeply.Most of the drillings were located at the structural high,and there were few wells that met good quality source rocks,so it is difficult to evaluate the source rocks in the study area precisely by geochemical analysis only.Based on the Rock-Eval pyrolysis,total organic carbon(TOC)testing,the organic matter(OM)abundance of Paleogene source rocks in the southwestern Bozhong Sag were evaluated,including the lower of second member of Dongying Formation(E_(3)d2L),the third member of Dongying Formation(E_(3)d_(3)),the first and second members of Shahejie Formation(E_(2)s_(1+2)),the third member of Shahejie Formation(E_(2)s_(3)).The results indicate that the E_(2)s_(1+2)and E_(2)s_(3)have better hydrocarbon generative potentials with the highest OM abundance,the E_(3)d_(3)are of the second good quality,and the E_(3)d2L have poor to fair hydrocarbon generative potential.Furthermore,the well logs were applied to predict TOC and residual hydrocarbon generation potential(S_(2))based on the sedimentary facies classification,usingΔlogR,generalizedΔlogR,logging multiple linear regression and BP neural network methods.The various methods were compared,and the BP neural network method have relatively better prediction accuracy.Based on the pre-stack simultaneous inversion(P-wave impedance,P-wave velocity and density inversion results)and the post-stack seismic attributes,the three-dimensional(3D)seismic prediction of TOC and S_(2)was carried out.The results show that the seismic near well prediction results of TOC and S_(2)based on seismic multi-attributes analysis correspond well with the results of well logging methods,and the plane prediction results are identical with the sedimentary facies map in the study area.The TOC and S_(2)values of E_(2)s_(1+2)and E_(2)s_(3)are higher than those in E_(3)d_(3)and E_(3)d_(2)L,basically consistent with the geochemical analysis results.This method makes up the deficiency of geochemical methods,establishing the connection between geophysical information and geochemical data,and it is helpful to the 3D quantitative prediction and the evaluation of high-quality source rocks in the areas where the drillings are limited.
基金supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(No.2021QNLM020001)the Major Scientific and Technological Projects of Shandong Energy Group(No.SNKJ2022A06-R23)+1 种基金the Funds of Creative Research Groups of China(No.41821002)National Natural Science Foundation of China Outstanding Youth Science Fund Project(Overseas)(No.ZX20230152)。
文摘Forward modeling of seismic wave propagation is crucial for the realization of reverse time migration(RTM) and full waveform inversion(FWI) in attenuating transversely isotropic media. To describe the attenuation and anisotropy properties of subsurface media, the pure-viscoacoustic anisotropic wave equations are established for wavefield simulations, because they can provide clear and stable wavefields. However, due to the use of several approximations in deriving the wave equation and the introduction of a fractional Laplacian approximation in solving the derived equation, the wavefields simulated by the previous pure-viscoacoustic tilted transversely isotropic(TTI) wave equations has low accuracy. To accurately simulate wavefields in media with velocity anisotropy and attenuation anisotropy, we first derive a new pure-viscoacoustic TTI wave equation from the exact complex-valued dispersion formula in viscoelastic vertical transversely isotropic(VTI) media. Then, we present the hybrid finite-difference and low-rank decomposition(HFDLRD) method to accurately solve our proposed pure-viscoacoustic TTI wave equation. Theoretical analysis and numerical examples suggest that our pure-viscoacoustic TTI wave equation has higher accuracy than previous pure-viscoacoustic TTI wave equations in describing q P-wave kinematic and attenuation characteristics. Additionally, the numerical experiment in a simple two-layer model shows that the HFDLRD technique outperforms the hybrid finite-difference and pseudo-spectral(HFDPS) method in terms of accuracy of wavefield modeling.
基金funded by the Fundamental Research Project of CNPC Geophysical Key Lab(2022DQ0604-4)the Strategic Cooperation Technology Projects of China National Petroleum Corporation and China University of Petroleum-Beijing(ZLZX 202003)。
文摘With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.
基金supported by the National Natural Science Foundation of China (NSFC): Grant number 42274147。
文摘High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher resolution seismic images in petroleum exploration. Although there have been successful applications of conventional signal processing and machine learning for post-stack seismic resolution enhancement,there is limited reference to the seismic applications of the recent emergence and rapid development of generative artificial intelligence. Hence, we propose to apply diffusion models, among the most popular generative models, to enhance seismic resolution. Specifically, we apply the classic diffusion model—denoising diffusion probabilistic model(DDPM), conditioned on the seismic data in low resolution, to reconstruct corresponding high-resolution images. Herein the entire scheme is referred to as SeisResoDiff. To provide a comprehensive and clear understanding of SeisResoDiff, we introduce the basic theories of diffusion models and detail the optimization objective's derivation with the aid of diagrams and algorithms. For implementation, we first propose a practical workflow to acquire abundant training data based on the generated pseudo-wells. Subsequently, we apply the trained model to both synthetic and field datasets, evaluating the results in three aspects: the appearance of seismic sections and slices in the time domain, frequency spectra, and comparisons with the synthetic data using real well-logging data at the well locations. The results demonstrate not only effective seismic resolution enhancement,but also additional denoising by the diffusion model. Experimental comparisons indicate that training the model on noisy data, which are more realistic, outperforms training on clean data. The proposed scheme demonstrates superiority over some conventional methods in high-resolution reconstruction and denoising ability, yielding more competitive results compared to our previous research.
基金supported by the National Natural Science Foundation of China under grant no.42374133the Beijing Nova Program under grant no.2022056+1 种基金the Fundamental Research Funds for the Central Universities under grant no.2462020YXZZ006the Young Elite Scientists Sponsorship Program by CAST(YESS)under grant no.2018QNRC001。
文摘(Multichannel)Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression.It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem.However,in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers.The presence of this kind of noise,called erratic noise,makes singular spectrum analysis(SSA)reconstruction unstable and has undesirable effects on the final results.We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA(RD-SSA)method.It incorporates the damped SSA,an improved version of SSA,into a reweighted framework.The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise.The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations.The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance.The feasibility of RD-SSA is validated via both synthetic and field data examples.
基金Supported by the project of Sanya Yazhou Bay Science and Technology City (Grant No:SCKJ-JYRC-2022-14)。
文摘To ensure project safety and secure public support, an integrated and comprehensive monitoring program is needed within a carbon capture and storage(CCS) project. Monitoring can be done using many well-established techniques from various fields, and the seismic method proves to be the crucial one. This method is widely used to determine the CO_(2) distribution, image the plume development, and quantitatively estimate the concentration. Because both the CO_(2) distribution and the potential migration pathway can be spatially small scale, high resolution for seismic imaging is demanded. However, obtaining a high-resolution image of a subsurface structure in marine settings is difficult. Herein, we introduce the novel Hcable(Harrow-like cable system) technique, which may be applied to offshore CCS monitoring. This technique uses a highfrequency source(the dominant frequency>100 Hz) to generate seismic waves and a combination of a long cable and several short streamers to receive seismic waves. Ultrahigh-frequency seismic images are achieved through the processing of Hcable seismic data. Hcable is then applied in a case study to demonstrate its detailed characterization for small-scale structures. This work reveals that Hcable is a promising tool for timelapse seismic monitoring of oceanic CCS.
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financially supported by the National Key R&D Program of China(2018YFA0702504)the National Natural Science Foundation of China(42174152)+1 种基金the Strategic Cooperation Technology Projects of China National Petroleum Corporation(CNPC)and China University of Petroleum-Beijing(CUPB)(ZLZX2020-03)the R&D Department of China National Petroleum Corporation(2022DQ0604-01)。
文摘The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio(SNR)situations.To address this issue,we propose a regression approach to pick FBs based on bidirectional long short-term memory(Bi LSTM)neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region.We employ a regressive model that represents the relationships among the elevation of shots,offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs,from common-shot gathers with sparsely distributed traces.Different from image segmentation methods which automatically extract image features and classify FBs from seismic data,the proposed method can learn the inner relationship between field geometry and FBs.In addition,the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution.The picking results of synthetic data shows that the proposed method has low dependence on label data,and can obtain reliable and similar predicted results using two types of label data with large differences.The picking results of9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data.The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method.The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.
基金supported by the National Natural Science Foundation of China (grant numbers: 41874160 and 92055213)。
文摘Seismic finite-difference(FD) modeling suffers from numerical dispersion including both the temporal and spatial dispersion, which can decrease the accuracy of the numerical modeling. To improve the accuracy and efficiency of the conventional numerical modeling, I develop a new seismic modeling method by combining the FD scheme with the numerical dispersion suppression neural network(NDSNN). This method involves the following steps. First, a training data set composed of a small number of wavefield snapshots is generated. The wavefield snapshots with the low-accuracy wavefield data and the high-accuracy wavefield data are paired, and the low-accuracy wavefield snapshots involve the obvious numerical dispersion including both the temporal and spatial dispersion. Second, the NDSNN is trained until the network converges to simultaneously suppress the temporal and spatial dispersion.Third, the entire set of low-accuracy wavefield data is computed quickly using FD modeling with the large time step and the coarse grid. Fourth, the NDSNN is applied to the entire set of low-accuracy wavefield data to suppress the numerical dispersion including the temporal and spatial dispersion.Numerical modeling examples verify the effectiveness of my proposed method in improving the computational accuracy and efficiency.
基金the financially supported by the National Natural Science Foundation of China(Grant No.52104013)the China Postdoctoral Science Foundation(Grant No.2022T150724)。
文摘Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.
基金supported by the National Natural Science Foundation of China (42274144,42304122,and 41974155)the Key Research and Development Program of Shaanxi (2023-YBGY-076)+1 种基金the National Key R&D Program of China (2020YFA0713404)the China Uranium Industry and East China University of Technology Joint Innovation Fund (NRE202107)。
文摘Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.
基金Thanks to the Northwest Oilfield Branch,SINOPEC,for providing the seismic data.We thank Dr.Yi-Duo Liu of University of Houston,Ying-Chang Cao and Fang Hao of China University of Petroleum(East China)for their constructive suggestions of this manuscript.We also thank two anonymous reviewers for their comments that helped us to improve the manuscript.This research is jointly supported by the National Natural Science Foundation of China(No.42272155)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA14010301)+1 种基金the Science Fund for Creative Research Groups of the National Natural Science Foundation of China(Grant No.41821002)National Natural Science Foundation of China(No.41702138).
文摘With the theoretical and technological developments related to cratonic strike-slip faults,the Shuntuoguole Low Uplift in the Tarim Basin has attracted considerable attention recently.Affected by multi-stage tectonic movements,the strike-slip faults have controlled the distribution of hydrocarbon resources owing to the special fault characteristics and fault-related structures.In contrast,the kinematics and formation mechanism of strike-slip faults in buried sedimentary basins are difficult to investigate,limiting the discussion of these faults and hydrocarbon accumulation.In this study,we identified the characteristics of massive sigmoidal tension gashes(STGs)that formed in the Shunnan area of the Tarim Basin.High-resolution three-dimensional seismic data and attribute analyses were used to investigate their geometric and kinematic characteristics.Then,the stress state of each point of the STGs was calculated using seismic curvature attributes.Finally,the formation mechanism of the STGs and their roles in controlling hydrocarbon migration and accumulation were discussed.The results suggest that:(1)the STGs developed in the Shunnan area have a wide distribution,with a tensile fault arranged in an enéchelon pattern,showing an S-shaped bending.These STGs formed in multiple stages,and differential rotation occurred along the direction of strike-slip stress during formation.(2)Near the principal displacement zone of the strike-slip faults,the stress value of the STGs was higher,gradually decreasing at both ends.The shallow layer deformation was greater than the deep layer deformation.(3)STGs are critical for connecting source rocks,migrating oil and gas,sealing horizontally,and developing efficient reservoirs.This study not only provides seismic evidence for the formation and evolution of super large STGs,but also provides certain guidance for oil and gas exploration in this area.
基金funded by the CNPC (China National Petroleum Corporation)Scientific Research and Technology Development Project (Grant No.2023ZZ0205,2021DJ0506)sponsored by the National Natural Science Foundation of China (41774136,41374135)。
文摘The deep-ultra deep carbonate reservoir in China,commonly subjected to modification of multi-stage diagenesis,has extremely high heterogeneity.Conventional rock physics analysis cannot accurately identify the elastic responses of reservoir.Here,the rock physics properties of the dolomite from the 4th Member of the Sinian Dengying Formation are experimentally measured,and the change law of rock physics characteristics is investigated within the framework of the diagenetic processes by the analysis of the elastic and petrologic characteristics,pore structure,and sedimentary environments.The results show that the differentiated diagenesis results in different pore structure characteristics and microtexture characteristics of the rock.The microbial dolomite of the algal mound-grain beach facies is subjected to the contemporaneous microbial dolomitization and seepage-reflux dolomitization,penecontemporaneous selective dissolution,burial dolomitization,and hydrothermal dolomitization.The resultant crystalline dolomite is found with one main type of the dolomite crystal contact boundaries,and the dissolution pore is extensive development.The siliceous,muddy,and limy dolomite of the interbeach sea environment mainly experiences the weak capillary concentration dolomitization,intensive mechanical compaction-induced densification,and burial dolomitization.Such crystalline dolomite is observed with four types of contact boundaries,namely the dolomite contact,clay contact,quartz contact,and calcite contact boundaries,and porosity mostly attributed to residual primary inter-granular or crystalline pores.The samples with the same crystal boundary condition have consistent correlations between the compressional-and shear-wave velocities,and between the compressional-wave velocity and the velocity ratio.Additionally,the variation of the acoustic velocity with effective pressure and the intensity of pore-scale fluid-related dispersion are controlled by the differentiation of pore structure types of the samples.The varied effects of soft pores like micro-cracks on the compressional-and shearwave velocity causes considerable changes in the relationships between the compressional-and shearwave velocities,compressional-wave velocity and velocity ratio,and porosity and acoustic velocity.This research is an attempt to demonstrate a novel method for investigating the rock physics variation of rock during the geological process,and the obtained findings can provide the rock physics basis for seismic prediction of the characteristics of deep carbonate reservoirs.
