Karst caves exhibit distinctive "string-of-beads" reflection configurations on seismic profiles, with their spatial distribution governed by intricate fracture networks and thus forming complex fracture-cave systems. Conventional methods, constrained by ambiguities in reservoir architecture and limited sample availability, face challenges in achieving accurate delineation. This study proposes a knowledge graph-guided intelligent identification technique based on coupled fracture-cave modeling, which innovatively integrates geological prior knowledge with deep learning through encoding geological topological relationships into adjacency matrix constraints. The methodology establishes a multi-task learning framework by synergistically combining forward modeling-derived label data volumes with expert-annotated data volumes. The approach employs knowledge graphs to characterize connectivity relationships between fractures and karst caves and designs geologically interpretable loss functions to dynamically adjust model optimization trajectories. Application in the Ordovician Lianglitage Formation of the Tarim Basin demonstrates substantial reduction in manual interpretation workload and significant enhancement in boundary delineation precision for fracture-cave systems. This methodology presents an innovative solution integrating knowledge-driven and data-driven approaches for prediction of strongly heterogeneous carbonate reservoirs.
Porosity is an important indicator for evaluating reservoirs and calculating reserves. However, the traditional coring method is costly to obtain porosity, and the porosity prediction method based on regression analysis and a statistical model often has significant errors. Therefore, a reservoir porosity prediction model that combines convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism is constructed, and its performance is verified using actual well logging data. Firstly, the complex nonlinear spatio-temporal relationships of logging data are captured with CNN and BiLSTM. Then, the convolutional self-attention mechanism is embedded, which generates queries and keys by causal convolution and allows better integration of local information into the attention mechanism. Compared with traditional self-attention mechanisms, this approach avoids the influence of abnormal data on the prediction results. Finally, the Monte Carlo dropout approach is used to quantify the uncertainty of the model, providing confidence intervals for reservoir porosity prediction and further assessing prediction credibility. The comparison experiments among multiple models show that the proposed method has high accuracy in predicting reservoir porosity. The experiments on two wells with different characteristics show that the method has strong generalization ability.
Seismic-well tie is an important step in seismic data interpretation. The traditional seismic-well tie method synthesizes seismic records by using well logging data and extracted seismic wavelets and matches them with the seismic traces beside the well by dragging. This method has significant human factors, is highly time-consuming, and can easily cause overstretching. Therefore, a deep learning method based on convolutional neural network (CNN) and gated recurrent unit (GRU) network is proposed to achieve automatic seismic-well tie. Firstly, seismic records are synthesized using typical models, and time correcting values are introduced to correct the records of seismic traces beside the well. Secondly, the relationship between two seismic traces and the time correcting values is established through a trained CNN-GRU network, and the correlation coefficients of the two seismic traces are used as constraint conditions to directly predict the time correcting values by using the synthetic seismic records and seismic traces beside the well. Finally, the neural network is tested using actual data from 30 wells, and the obtained results are compared with manual calibration results. The correlation coefficients between the calibrated synthetic seismic records and the seismic traces beside the wells are calculated. The following findings are obtained. ① The correlation coefficients of automatic calibration with the network are greater than or equal to those of manual calibration for 25 wells and are basically consistent for the other wells. ② Manually calibrating 30 wells takes about 30 min, while calibrating them with the network only takes 5 s. Therefore, compared with the traditional method, the proposed method has higher accuracy and better efficiency in seismic-well tie, which verifies the feasibility and progressiveness of the method.
In oil and gas exploration and development, porosity is an important parameter to evaluate reservoir physical properties, especially in flooded well evaluation. Accurate porosity prediction is the key significance to the evaluation of remaining oil and subsequent production and development. The conventional linear porosity model has limitations in prediction accuracy, and the random forest regression model often faces the problems of low optimization efficiency, complex parameter adjustment, and large consumption of computing resources when traditional parameter optimization methods are employed. To improve the accuracy and efficiency of porosity prediction, this paper proposes a new method to optimize the random forest regression model based on the nutcracker optimization algorithm (NOA). This method is inspired by the foraging, storage, and food retrieval behavior of the North American bird nutcracker. In this study, the random forest regression model is subjected to hyperparameter optimization through NOA with the acoustic time difference, compensated density, and compensated neutron curve as the input features of the model and the core porosity as the target value, which avoids locally optimal solutionand thus determines globally optimal parameter combination. Compared with the traditional grid search method, NOA shows higher efficiency in hyperparameter optimization. The results of data analysis and model prediction show that this method not only speeds up the training speed of the random forest model but also effectively improves the fitting effect and prediction accuracy of the porosity model.
Fluid identification in carbonate reservoirs is crucial for reservoir assessment and hydrocarbon development. However, carbonate reservoirs are strongly non-homogeneous, which makes it difficult to realize their accurate identification by traditional methods. Although machine learning-based methods can deeply explore the intrinsic connection between logging data and oil, gas, and water information to improve the identification effect, they are easily affected by the noise of logging data, and the sample category ratio is imbalanced. In this paper, a reservoir fluid identification method based on cost-sensitive learning is proposed with carbonate reservoirs in the Sichuan Basin as the research object. First, the wavelet transform is used to reduce the noise of logging data to solve the data noise problem.Then, the correlation test of logging curves is carried out by integrating analysis of variance, decision tree, and reservoir theory to screen out the logging curves that are highly correlated with the reservoir fluid types.Finally, the neural network model is designed to address sample category imbalance by using the cost-sensitive learning strategy, so as to improve identification accuracy. The results show that the wavelet transform reduces data noise and improves the accuracy of fluid identification. The logging curves AC, CNL, CAL, RT, GR, and RXO are highly correlated with the fluid types in carbonate reservoirs.The cost-sensitive learning method effectively addresses the problem of low identification accuracy of a few classes due to the imbalanced data, and the identification accuracy of the model reaches 97.61%, which is better than that of other comparative models. It provides a feasible solution for fluid identification in carbonate reservoirs.
In seismic geometric attributes, stratigraphic dip is the basis for calculating attributes such as curvature and coherence, and has been widely applied in seismic data interpretation. However, traditional multi-window scanning algorithms are inefficient, and the existing intelligent algorithms based on end-to-end supervised training are constrained in their generalization and transferability due to the diversity of seismic data. Therefore, this paper proposes an unsupervised training method for intelligent dip calculation using deep neural networks. This method is based on a three-dimensional convolutional neural network (3D CNN) and achieves unsupervised optimization of the deep neural network by establishing and solving an optimization objective for the structure tensor. It does not require the prior creation of a large number of labels, and combined with transfer learning and fine-tuning for actual work area, achieves efficient and stable 3D dip angle calculation based on the efficient computation of seismic feature vectors. Extensive applications on models and actual data have shown that this intelligent method significantly improves computational efficiency while maintaining stable computation results. Specifically, the geometric curvature obtained based on intelligent dip calculation results exhibits more advantages in expressing fracture information.
Ensuring strong correlation among samples of the same category in seismic facies images and determining the number of seismic facies categories are the core of unsupervised pre-stack seismic facies analysis. This paper proposes an unsupervised pre-stack seismic facies analysis algorithm with correlation threshold constraints for category determination. First, the pre-stack seismic trace set data is transformed into two-dimensional images, and the high-level nonlinear, discriminative, and invariant features of the images are extracted using unsupervised deep learning networks, which can highlight strongly concealed information. Subsequently, the threshold for the number of seismic facies categories is determined based on the cross-correlation values of deep features of pre-stack seismic images corresponding to different categories of seismic facies within the study area, ensuring that samples within the same class in the obtained pre-stack seismic facies images exhibit highly strong correlation, and the number of seismic facies categories is determined based on discriminant thresholds. Finally, the obtained pre-stack seismic facies images are calibrated using existing drilling information to provide a basis for geological experts to infer sedimentary environments and reservoir distributions. Theoretical model testing confirms that this method not only determines the number of pre-stack seismic facies depending on discriminant thresholds but also ensures strong correlation among samples within the same class in the seismic facies images, demonstrating greater robustness compared to other methods. Application of actual data shows that this method improves the accuracy of predicting seismic facies of fracture-cavity reservoirs in the Permian Maokou Formation and provides a reliable scientific basis for well deployment and the discovery of undrilled fracture-cavity reservoirs.
Microseismic localization is a major challenge in microseismic monitoring tasks and is helpful for analyzing the effect of hydraulic fracturing. Physics-informed neural network (PINN) can achieve microseismic localization. However, the trade-off among multiple loss terms plays a crucial role in the training stage of PINN. Thus, this paper proposes a novel microseismic localization method based on an adaptive-loss-weighting gradient-enhanced PINN. First, a combined loss function is constructed by integrating the residuals of relative arrival time and the eikonal equation. Second, an adaptive term is introduced to automatically update the loss weights, and gradient information is also incorporated to enhance the performance of the network. Finally, network training is performed to obtain the traveltime distribution across the computational domain, and the hypocenter location is predicted by identifying the minimum traveltime. Test results demonstrate that this method can enhance the training stability and prediction accuracy of the network and achieve a reliable microseismic localization effect.
Salt bodies are geological structures with good airtightness, which are conducive to oil and gas storage. It is extremely necessary to achieve refined interpretation of salt bodies. However, unlike faults, salt bodies have more complex characteristics and significant morphological differences, and thus conventional methods can easily lead to confusion and misjudgment. In addition, since data-driven salt body recognition models have poor generalization ability on actual datasets, there are still challenges in interpreting and visualizing salt bodies in seismic exploration. The paper regards salt body interpretation as a semantic segmentation problem for seismic images and proposes an intelligent salt body segmentation method based on the context fusion of transfer learning and mixed attention (multi-path structure mixed attention and transfer optimized net, MMTONet). At the same time, a salt body context feature fusion module is designed, and an improved attention convolution mixed skip connection mechanism is established to better compensate for the information loss caused by down-sampling, thereby improving the pixel-level discrimination ability of the model for salt body boundaries and highamplitude noise. On this basis, a transfer learning adapter fine-tuning strategy is also designed to improve the generalization ability of the model on actual data. The experimental results on seismic datasets show that MMTONet outperforms mainstream semantic segmentation methods in improving segmentation accuracy and reducing computational and parameter complexity.
Wave impedance inversion based on deep learning often requires a large number of label data to drive the model for network training, but in practice, the acquisition of label data (logging data) is difficult and costly, and usually only a small amount of label data is used for training. Therefore, a semi-supervised wave impedance inversion method based on data augmentation and pseudo-label generation is proposed. Firstly, the label wave impedance data is interpolated by cubic spline interpolation method and then randomly resampled, and then the seismic records corresponding to the augmented wave impedance are calculated by forward modeling method. The amplified seismic record and wave impedance are used as the network training set to train the network and predict the wave impedance. High quality prediction data is selected as pseudo-labels, and the pseudo-labels are augmented to greatly expand the training data set. However, Temporal Convolutional Network (TCN) has advantages in time series data modeling, which can capture the long-term dependency of data and complete the wave impedance inversion task well. In this paper, the Marmousi model test results show that the proposed method is suitable for the wave impedance inversion of a small amount of label data, has good anti-noise performance, and still has good inversion accuracy for different label distributions. The inversion results of the actual exploration data show that this method can effectively solve the problem of seismic impedance inversion.
The absorption and attenuation of seismic wave energy in complex loess plateaus has always been a focal point for geophysicists. This problem makes it difficult to improve the resolution and signal-to-noise ratio of seismic data and satisfy the requirements for describing reservoirs and exploring residual oil in mature oilfields. The distributed acoustic sensing (DAS) multi-well borehole-surface joint exploration (BSJE) technology, characterized by identical-source excitation, consistent reception, small downhole trace distance, and high coverage, can reduce absorption and attenuation of near surface by receiving seismic waves in the well, which is one of the main approaches to solve the aforementioned problem. However, the most critical issue facing BSJE technology is the difficulty in vertical seismic profile (VSP) imaging due to uneven seismic excitation points on complex surfaces. At the same time, there are problems such as the lack of BSJE scheme demonstration method and immature techniques for synchronous reception and quality control of DAS multi-well BSJE. Therefore, based on wide-azimuth, wide-bandwidth, and high-density 3D seismic, a technical study on the DAS BSJE, processing, and interpretation of 3D-VSP is conducted with seven wellsin a block in the eastern part of the Ordos Basin.This paper, as the first part, "Acquisition", of the technical study systematically studies the key issues of DAS BSJE technology in complex loess plateau areas. A sector method-based BSJE excitation scheme demonstration technique aimed at VSP imaging is proposed, and a scheme with uniform excitation, DAS multi-well continuous reception, and quality control for BSJE is developed. High-quality DAS 3D-VSP data are obtained by applying the proposed method, which lays a solid foundation of high-quality data for multi-well BSJE-based 3D-VSP migration imaging and reservoir prediction.
As shale gas exploration in the southern Sichuan area continues to advance, the exploration environment becomes increasingly complex. To obtain higher quality seismic acquisition data, it is important to improve the accuracy of excitation well depth design. However, the current interior design only defines the dynamic range of excitation well depths, while the actual drilling depth still relies on the driller's identification of the lithology of the bottom well, impacting the accuracy of the excitation depth. Therefore, we propose a method of pointwise excitation well depth design in the NJ-RC areas. Initially, the reasonable range of well depths is analyzed theoretically and the optimal excitation interval in mudstone is determined through comparative experiments.The mid-depth of the mudstone is used as the base data to identify the best excitation surface across the entire area. Subsequently, the design process is refined by integrating geostatistics. The principle analysis, effect comparison, and actual data verification of the entire region and local areas for the four interpolation methods are conducted, considering both qualitative and quantitative analyses and sedimentary rock constraints. In this way, the minimum curvature interpolation method is found to be most suitable for the study area. Verification and application of actual data indicate that the optimized pointwise excitation well depth design can determine the optimal excitation surface across the entire area and specify the best excitation depth for each well, effectively enhancing the precision of well depth design. This significantly improves the quality of seismic data and provides a reference for the acquisition of similar work.
Microtremor imaging technology extracts surface wave signals from the weak vibrations and noise that persist on the Earth, and then utilizes dispersion characteristic of the surface wave to detect underground structures. In recent years, it has been widely used in imaging the internal structures of the Earth at different scales. Obtaining accurate and reliable dispersion curves is the key to microtremor imaging. However, due to the low signal-to-noise ratio of microtremor surface wave signal, the dispersion energy spectrum of virtual shot-gather has low resolution, is cluttered, and is subject to much interference. Thus, the efficiency and accuracy of manual extraction of dispersion curves are low. This paper introduces digital image processing technology, regards the dispersion energy spectrum of the virtual shot-gather as an image, and uses the connected domain analysis algorithm to label each energy group in the image. The surface wave information is identified and preserved based on the band-like connectivity feature, and the interfering energy groups are suppressed. Consequently, a high-resolution energy spectrum image containing only surface wave dispersion strips is obtained, and the peak values of each frequency section in the image are automatically identified to obtain phase velocities. Finally, the Hampel filtering is used to eliminate highly deviated values in phase velocities, and the dispersion curve is automatically extracted by fitting reasonable phase velocities for each frequency. The accuracy and effectiveness of the automatic extraction method are validated through simulated data from two sets of theoretical stratigraphic models. The proposed method is applied to the processing of actual microtremor data in a certain research area in Jiangxi, the dispersion curves of multiple sets of virtual shot-gather are successfully extracted, and a two-dimensional shear wave velocity profile of the research area is established through inversion. This method achieves automatic processing and imaging of microtremor data while ensuring accuracy.
In the middle and deep oil and gas exploration in the Junggar Basin, the imaging quality of seismic data is seriously affected by multiple waves caused by strong impedance interface. Because of the small difference between multiple waves and primary waves and the unclear periodic characteristics of multiple waves due to multi-source superposition, it is difficult to distinguish between primary waves and multiple waves during data processing. Thus, accurate recognition of multiple waves is a prerequisite for multiple wave suppression. In this paper, the reflection coefficient, vertical seismic profile (VSP) calibration, and forward modeling results are first analyzed to determine the source of multiple waves. Then, the plane zone of multiple wave development is determined by making clear the characteristics of source plane distribution through well-seismic combination. Furthermore, the longitudinal segments affected by multiple waves are determined by precise calibration using synthetic recording, VSP, and other methods. At last, the differences in frequency and energy between multiple waves and primary waves are identified using data volumes of different frequency bands, and the seismic response characteristics of multiple waves are determined. On this basis, a multiple wave recognition system based on the "four determination" method is established. The practical application results in Fuzhong and Mahu areas of the Junggar Basin show that the formation structure and occurrence of the new data have high coincidence rates with well drilling, and the relationship between sand pinchout and overlaying is clear. The multi-wave recognition system can effectively identify the development characteristics of different multiple waves, providing an important basis for multiple wave suppression and greatly improving data fidelity.
The seismic wave propagation environment of marine seismic exploration is a coupling medium consisting of fluid (upper seawater) and solid (the rock formation below the seabed). The fluid-solid coupling equation can simulate the propagation law of seismic waves in marine seismic exploration with high accuracy. However, most of current fluid-solid coupling equations are based on acoustic and elastic wave equations, with the frequency dissipation effect caused by quality factors in solids ignored. In this paper, based on the acoustic wave equation and the viscoelastic wave equation, the acoustic and viscoelastic wave coupling equation is constructed through introducing the relationship between pressure component and normal stress into the viscoelastic wave equation. Moreover, the multi-parameter full waveform inversion of the coupling equation is realized. The model experiment results show that the accuracy of the multi-parameter full waveform inversion based on the acoustic and viscoelastic wave coupling equation is obviously better than that of the acoustic and elastic wave coupling equation for velocity and other parameters because it fully considers the frequency dissipation effect in seismic wave propagation. The proposed method has a good application prospect in the full waveform inversion of actual OBN data.
The second kind of Chebyshev expansion has been widely used in numerical approximations since it has stricter constraints on the objective function compared to the first kind of Chebyshev expansion, resulting in smaller error accumulation. Starting from the frequency-domain acoustic wave equation, this paper introduces the second kind of Chebyshev expansion into the Fourier one-way wave continuation operator to achieve wavefield continuation. On this basis, with the use of the Born approximation theory and cross-correlation imaging conditions, prestack depth migration imaging is realized based on the direct expansion of the second kind of Chebyshev polynomials. This method utilizes the accurate velocity at each location during depth propagation, reducing errors compared to the conventional one-way wave continuation method which uses the mode of depth-layer background velocity combined with perturbation velocity. This achieves higher accuracy of the wavefield continuation operator under strong lateral velocity variations. The effectiveness of the method is verified through wavefield snapshot analysis and model imaging tests. It has significantly improved imaging accuracy compared to conventional prestack depth migration methods for one-way wave.
Underground rocks commonly feature multiscale fractures, which induce fluid motion relative to solid when seismic waves pass through them and thus lead to wave dispersion and attenuation. However, most current rock physics models assume background media to be isotropic and consider fractures that are horizontally or directionally oriented, often overlooking the elastic properties of rocks with fractures of varying orientations and inclinations across different frequency ranges and less analyzing seismic attenuation caused by fractures. In this regard, this study constructs a rock physics model of fractures oriented in arbitrary directions within a transversely isotropic (TI) background. Based on the linear slip fracture model, this study reconstructs the expression form of compliance matrix of fractured rocks for the model under the low-frequency and high-frequency limits, constructs the frequency-dependent compliance matrix of fractured rocks, and incorporates fluid by using the Gassmann equation to develop a physics model for rocks with inclined fractures under the TI background. Using this model, this study analyzes how fluid-filled fractured rock velocity and attenuation vary with frequency, fracture inclination and azimuth angle, and quantitatively assesses the influence of background aniso-tropy on elastic properties of fracture media. The results demonstrate significant impacts of fracture inclination and azimuth angle on velocity and attenuation changes. This study underscores the effects of background aniso- tropy on both velocity and attenuation of fractured rocks, highlighting varying effects across different frequencies.
In deepwater zones under high-pressure conditions, the complex and heterogeneous pore structures of reservoirs lead to significant variations in rock elastic properties, making conventional forward modeling and inversion techniques insufficient for accurate reservoir prediction. Petrophysical models serve as a critical bridge between physical properties and elastic parameters of reservoirs. To improve reservoir prediction accuracy in sparsely drilled deepwater zones under high pressure with limited well data, this study incorporates the effects of effective pressure into the petrophysical modeling process. A pressure-dependent correction is applied to the aspect ratio of sandstone pores, and adaptive sandstone porosity is fitted. Based on this, a deepwater sandstone-mudstone petrophysical model with effective pressure correction is established. The proposed petrophysical modeling approach is applied to the deepwater A block in the southern offshore region of China. Compared with conventional petrophysical models, the developed model exhibits higher accuracy in well log reconstruction. Furthermore, to address the low prediction accuracy due to sparse well data, the pressure adaptive petrophysical model is utilized to generate pseudo-wells. The practical application of the method demonstrates that incorporating multiple pseudo-wells facilitates more accurate reservoir identification with inversion results. The inversion outcomes show strong agreement with sedimentary facies and drilling data, which validates the effectiveness and reliability of the proposed approach.
Continental shale oil has become the major unconventional oil resource in China. The reservoir features include the development of lamellation crack, low porosity, large difference between horizontal and vertical permeability, and complex pore structure. Therefore, clarifying the relationship between the elastic and physical parameters of shale oil reservoirs is important.In addition, there are few studies on the characteristics of shale lamellation crack and its influencing factors. Based on anisotropic background media, a rock physical modeling method for fracture-porosity shale rich in kerogen is proposed in this paper.Through rock physical modeling, the effects of matrix porosity, aspect ratio, connectivity coefficient and the lamellation crack length, width and numbers of the shale are compared and analyzed. The results show that the P wave velocity is greatly affected by the connectivity coefficient, while the S wave velocity is not affected by the connectivity coefficient. The effect of lamellation crack over 100 microns should not be ignored and its induced anisotropy could not be summarized as the effect of intrinsic anisotropy.Vp/Vs positively correlates with the length of lamellation crack, while the relationship with the number of lamellation crack decreases first and then increases. The method in this paper is applied to the logging data of a practical working area. The P‐wave velocity and S‐wave velocity prediction in shale reservoirs through this method are highly consistent with actual data, which verifies the effectiveness and applicability of the proposed method, and the method can be used as a bridge in the characterization of unconventional oil and gas reservoirs.
To elucidate the characteristics of hydrocarbon migration along source faults and their distribution patterns in different sand bodies on both sides of the faults, a predictive method is developed based on the classification of fault-sand configurations. This method determines the hydrocarbon migration pathways by identifying the sealing sections of regional mudstone cap rocks and the sealing sections by assessing the degree of sealing in the fault fill material at the fault-sand configurations. The method utilizes the fault-connection thickness and the degree of sealing in the fault fill material to determine transport direction in fault-sand configuration and thereby predict the distribution of transitional zones between migration pathways and sealing sections. This method is applied to the Niuju area in the eastern part of the Liaohe Depression, Bohai Bay Basin. The results indicate that the transitional zones between migration pathways and sealing sections in the fault-sand configurations of the Niuxi fault and the upper Es3 sand bodies are mainly distributed in the central part, with a minor distribution in the eastern part. This distribution favors the accumulation of hydrocarbons generated from the Es3 source rocks within the traps of the upper Es3, which aligns with the currently discovered hydrocarbon distribution near the Niuxi fault. This demonstrates that the method is suitable for delineating the distribution of transitional zones between migration pathways and sealing sections in fault-sand configurations.
The joint AVO inversion objective function of PP- and PS-wave is usually constructed based on the L2 norm, and its effect is greatly affected by the signal-to-noise ratio (SNR) of seismic data. In this paper, a joint AVO inversion method of PP- and PS-wave is proposed. Based on the Bayesian theory, this method combines PP- and PS-wave seismic data, and constructs an objective function based on normalized zero-delay cross-correlation algorithm for inversion. The combination of PP- and PS-wave seismic data can enhance the stability of the inversion algorithm. The strategy of normalizing seismic data and the cross-correlation objective function can enhance the noise immunity of the inversion algorithm. Therefore, the method in this paper can provide the P-wave velocity, S-wave velocity and density parameters with high accuracy using the seismic data with lower SNR. The test results of model data whose SNRs are 7 dB and 1 dB respectively and actual data show that the proposed method can achieve high-accuracy inversion results by seismic data with lower SNR. Compared with the joint inversion using the objective function based on the L2 norm, the results show that the proposed method has smaller error and more robust anti-noise performance.
Clastic rock reservoirs are widely distributed in the Barbados accretionary wedge basin with the primary reservoir type of deepwater turbidite sandstone and the hydrocarbon type of biogas reservoir. Since gas-bearing sandstones and water-bearing sandstones in the basin are both presented as "bright spot" anomalies in seismic data, it is difficult for conventional post-stack hydrocarbon detection methods to accurately predict the distribution of gas reservoirs, which leads to a low success rate of early exploration. Thus, after analyzing logging response characteristics of drilled reservoirs, this paper proposes a new fluid detection method based on post-stack relative amplitude decoupling for "bright spot" sandstone reservoirs. Firstly, the main controlling factors of the seismic response of reservoirs are quantitatively analyzed by means of fluid substitution and forward modeling. Secondly, the post-stack relative amplitude decoupling factor is designed for "bright spot" sandstone reservoirs, and the relative amplitude relationship template is established for different lithologies and fluids. Finally, gas-bearing sandstones and water-bearing sandstones are distinguished utilizing the relative amplitude relationship template to realize reservoir prediction and fluid detection with multi-age post-stack seismic data. The application results show that the new methodology has a stronger ability to distinguish gas-bearing sandstones and water-bearing sandstones in the study area, which overcomes the blind area of traditional post-stack hydrocarbon detection methods, significantly improves the prediction accuracy of biogas reservoirs, and provides an important basis for exploration decision.
The research object of oil and gas exploration is gradually shifting toward complex oil and gas reservoirs. The periphery of the Banghu Syncline in the Qianjiang Sag is a typical inland salt lake deposit. The complex thin sand and mudstone interbedded reservoir structure in the Qian-3 section of the Banghu Syncline area requires high-precision and high-resolution exploration technology to support actual production. In view of this, the study conducts forward modeling analysis and complex lithology classification based on well logging data. First, it analyzes and calculates the lithological data based on the original logging data, analyzes the lithological characteristics of sandstones containing different fluids (water-bearing sandstones, oil-bearing sandstones, and dry-layer sandstones), establishes different wedge forward models based on the convolution theory, and investigates the seismic response characteristics of different lithological combinations. Then, lithology curves are reconstructed using the K-means algorithm with known logging lithology data, and density attributes are used to correct natural gamma values for lithology classification. Finally, a geological model of the
The exploration targets such as metallic minerals, sulfides, and oil and gas resources often exhibit a strong induced polarization (IP) effect, which causes distortion of transient electromagnetic (TEM) signals. Neglecting the IP effect frequently leads to difficulties in fitting TEM data and even results in erroneous interpretations. This study employs the time-domain finite volume method (FVM) and approximates the Cole-Cole model by using a Padé series to achieve 3D forward modeling of the TEM method considering the IP effect. Traditional methods for selecting the central frequency in the Padé series rely on qualitative analysis, which makes the forward modeling results highly sensitive to changes in the frequency-dependent parameter. This sensitivity limits the application of Padé approximation in regions with significant variability in the frequency-dependent parameter. To address this limitation, this study quantitatively evaluates the errors introduced during the Padé approximation process and proposes an adaptive selection algorithm based on error information. Multiple numerical experiments demonstrate the accuracy and effectiveness of the proposed algorithm. Finally, the differences in TEM responses between polarized and non-polarized media models are analyzed, and the charging and discharging processes of polarization anomaly bodies in TEM fields are simulated using an instance model.
The time-frequency electromagnetic method is widely used in the field of oil and gas exploration. Three-dimensional forward modeling can obtain the electromagnetic response of the three-dimensional electrical structure of subsurface, serving as an essential tool for qualitative interpretation of electromagnetic data and observation system design for electromagnetic exploration, as well as the basis for three-dimensional inversion. This paper utilizes a three-dimensional adaptive finite element forward modeling algorithm of the time-frequency electromagnetic method to solve the second-order partial differential equations that the electric field satisfies and estimate the posterior error based on the continuity conditions of the electromagnetic field. This guides the adaptive refinement of the mesh, achieving efficient and accurate simulation of the electromagnetic response. Compared to traditional forward modeling methods, this approach effectively reduces computational load while ensuring accuracy. Numerical examples validate the accuracy and efficiency of the algorithm. Additionally, simulations of oil and gas models are conducted to analyze the sensitivity of time-frequency electromagnetic responses to reservoirs. This study provides significant reference for oil and gas exploration with the help of the time-frequency electromagnetic method.
