The shear wave velocity and thickness of near-surface strata can be obtained through the inversion of Rayleigh wave dispersion curves. However, traditional nonlinear inversion algorithms often have disadvantages such as poor convergence effect and being prone to fall into local extremity. A new improved beluga whale optimization (DWBWO) algorithm is proposed in this paper and applied to the inversion of the Rayleigh surface wave dispersion curve. Based on the beluga whale optimization (BWO) algorithm, this algorithm introduces the Cubic chaotic initialization strategy to improve the uniformity of the initial population. Meanwhile, the dimensional reverse learning strategy is used to improve the convergence efficiency of the algorithm, and the whirlwind foraging strategy (WFS)is adopted to improve the local optimization ability of the algorithm. The DWBWO algorithm is tested by applying the multi-extremum functions, simulated data and measured data, and compared with the grey wolf optimization (GWO) algorithm, sparrow optimization (SSA) algorithm, whale optimization (WOA) algorithm and BWO algorithm. It was proved that the improved algorithm in this paper has higher stability and accuracy.
Accurately segmenting pores in scanning electron microscope (SEM) images can provide a scientific basis for oil and gas exploration and development, and more. At present, pore segmentation methods mainly rely on data-driven approaches, requiring a large amount of manual annotation of data, which is time-consuming and costly. To this end, this paper proposes the semi-supervised pore segmentation network PoreSeg for SEM images. Firstly, a semi-supervised framework is constructed based on consistency regularization and pseudo labeling. Secondly, a high-intensity combined perturbation strategy is introduced to enhance data diversity. Finally, combined with the pore aware fusion (Pore-CutMix) method, the sparse pore information is fully utilized to improve the segmentation ability of the model for pores. Experimental results show that under the condition of equal labeled samples, PoreSeg improves the pore intersection over union (IoU) by 15.10% compared with the fully supervised network. At the same time, compared with existing semi-supervised methods, PoreSeg is more sensitive to pores and has higher segmentation accuracy. PoreSeg significantly reduces dependence on annotated data while maintaining high accuracy, and has huge application potential.
During waterflooding, evaluating interwell connectivity is essential for reservoir management, production strategy optimization, and improved hydrocarbon recovery. Existing methods, those are based on graph neural networks fail to characterize the temporal and spatial relationships within well networks through graph structures, limiting the accurate description of delayed dynamic responses. To bridge this gap, this study proposes a multi-level graph-structured temporal network model to capture dynamic relationships from time-series well network data, and enable precise interwell connectivity evaluation. Specifically, considering the injection-production response delay of a well network, a multi-level graph-structured temporal-spatial dependence representation method is proposed, which integrates production time-series responses with the spatial structure information of the well network is introduced. Subsequently, a multi-level temporal graph neural network model is established; Next, an attention mechanism based hierarchical information interaction and update method is developed. These together enable the model to explore dynamic interactions between injection and production wells and achieve accurate inversion of interwell connectivity. Experimental results demonstrate that the proposed model exhibits superior accuracy over conventional temporal models, with a consistency of 93.8% with tracer test results. Moreover, it conforms to the gradual evolution of connectivity in accordance with physical principles, demonstrating the method's strong practical applicability in engineering.
The accurate prediction of tight sandstone reservoir parameters is a key scientific issue and technical challenge in unconventional oil and gas exploration. Traditional prediction methods based on linear or nonlinear regression have limitations in characterizing the complex nonlinear relationship between logging curves and reservoir parameters, leading to insufficient prediction accuracy. This study takes the Chang 6 reservoir in the Tang 157 well area of the Ganguyi Production Plant in Yanchang Oilfield as an example. Based on logging data and core analysis porosity data, multi-source data fusion preprocessing is conducted, and a novel neural network architecture (CNN-Transformer Network) that integrates the core advantages of CNN and Transformer is innovatively proposed. The prediction performance of the CNN-Transformer model is comprehensively compared with that of traditional linear regression (LR), TCN-LSTM, GRU, and ResNet models using RMSE, MAE, and R2 metrics. Experimental results show that the prediction accuracy of the CNN-Transformer model reaches 96.7%, significantly outperforming the other comparative models. This model effectively captures the unique complex nonlinear mapping relationship between logging curves and porosity in tight sandstone reservoirs, significantly improving the accuracy of reservoir parameter prediction and providing reliable technical support for the efficient exploration and development decision-making of tight sandstone reservoirs.
The seismic data of the Lower Cambrian Qiongzhusi Formation in the Sichuan Basin has weak reflected energy without distinct characteristics of interlayer wave groups and clear description of faults, which makes it difficult to meet the requirements for fine prediction of high-quality shale reservoirs and the accurate design of horizontal well trajectories. In response, taking the 3D seismic project for shale gas in Well Z201 as an example, this paper proposes a high-precision seismic acquisition technology of deep shale gas in Qiongzhusi Formation of Sichuan Basin. First, the observation system parameter optimization technology for pre-stack inversion of reservoirs is applied to design the observation system. Then, the intelligent layout of physical points in the obstacle area based on the contribution degree is conducted to improve the uniformity of coverage times in the target zone. Finally, the surface velocity and lithology constrained modeling technology is used to characterize the near-surface structure and spatial distribution of lithology within the survey area. The application results of the proposed method in seismic acquisition of shale gas in Qiongzhusi Formation demonstrate that high-resolution, broadband seismic data can be obtained using the technology, which provides a data basis for the subsequent high-resolution processing and fine reservoir description.
Noise in post-stack seismic data can severely interfere with the identification and interpretation of reflection signals, making efficient denoising essential for improving the accuracy of seismic data interpretation. Owing to its superior multi-scale and multi-directional characteristics, curvelet transform has been widely adopted for post-stack data denoising. However, conventional curvelet transform tends to produce pseudo-Gibbs effects at boundaries, which leads to edge oscillations and spurious reflections. In addition, aggressive noise suppression often results in the loss of valid signals, which limits the practical applicability of these methods. To address these challenges, this paper proposes a curvelet transform approach incorporating multiscale adaptive block curvelet-domain thresholding (MABCDT). First, cycle spinning and MABCDT are introduced in the curvelet domain to enhance the preservation of weak signals and significantly mitigate pseudo-Gibbs phenomena. Then, a fast non-local mean filtering is applied to the data after inverse curvelet transform, which further retains valid signals while removing residual noise. Finally, a directional smoothing diffusion algorithm is introduced, which utilizes gradient direction information to perform directionally weighted smoothing and diffusion, thereby further suppressing noise and enhancing the continuity of valid signals. Both synthetic and field data tests demonstrate that the proposed method outperforms conventional post-stack denoising techniques in terms of signal-to-noise ratio enhancement and waveform fidelity. The method preserves the continuous structural features of seismic signals while suppressing noise and significantly reduces pseudo-Gibbs effects caused by high-frequency truncation.
Full waveform inversion (FWI) is currently the most accurate velocity field inversion technique. FWI employs gradient-based local optimization algorithms to minimize the error between the forward data and the original data, thereby obtaining an accurate subsurface velocity field. The success of FWI on real data relies on the careful processing of original data and the prudent selection of inversion strategies. This paper discusses and experiments with the parameter control and implementation strategies of FWI, considering the characteristics of marine data, and proposes practical techniques for full waveform inversion of marine data. Specifically, the technology is as follows. ① The fraction frequency noise suppression technology is used for turning waves, which primarily improves the signal-to-noise ratio of low-frequency turning waves while avoiding damage to the effective signal. ② The wavelet adjustment techniques to enhance the accuracy of the initial wavelet are applied, which allows for more precise calculation of the error between forward data and actual data and leads to a more accurate calculation of the velocity update gradient. ③ The first-arrival tomography inversion velocity models for near-seafloor velocity modeling improve the accuracy of the initial velocity field, better avoid cycle skipping, and ensure that the objective function converges towards a more accurate direction and at a faster rate. ④ The offset increment strategy is adopted during FWI iterations, progressively updating the velocity field from shallow to deep, reducing the solution multiplicity of FWI, and ensuring more accurate convergence of the FWI velocity field. This technology has achieved relatively good results in practical marine data projects, obtaining more accurate velocity fields and better depth migration imaging effects.
Surface-consistent deconvolution is a useful tool to improve the resolution of seismic data by compressing the seismic wavelets and enhancing the wavelets' waveform consistency. However, the conventional surface-consistent deconvolution mainly uses the least-squares method or the Gauss-Seidel method for spectral decomposition. Their decomposition results are easily affected by noise, which results in an increase of noise energy in the seismic data after deconvolution. Therefore, this paper proposes the three-dimensional (3D) surface-consistent deconvolution method based on the over-relaxation Jacobian iteration algorithm to improve the anti-noise ability of the surface-consistent deconvolution. First, the logarithmic power spectrum is effectively decomposed into five components, including source, receiver, common midpoint (CMP), offset and global term, by using the weighted least-squares objective function under strong noise conditions. Then, the five-component convolution model with the global term is used instead of the conventional four-component model, and the changing near-surface conditions are transformed into surface conditions similar to the global term, which thus effectively eliminates the waveform changes caused by inconsistent near-surface conditions. Test results of two sets of synthetic data and one set of 3D field data show that the over-relaxation Jacobian iteration algorithm has relatively strong anti-noise ability. The algorithm can effectively compress the seismic wavelets, enhance the wavelets' waveform consistency, effectively compress non-surface-consistent noise, and has achieved relatively good deconvolution performance, which shows great significance for enhancing the vertical resolution of seismic data in complex geological structures.
In seismic exploration, owing to the viscoelastic properties of subsurface media, the seismic wavefield experiences substantial attenuation, loss of high-frequency components, and phase distortion during propagation. This is because energy is absorbed, which ultimately results in a reduction of the resolution and imaging accuracy of seismic data. Precisely acquiring the Q-value of the near-surface media and conducting inversion processing is a crucial approach to address this issue. Consequently, an adaptive near-surface Q-value estimation method based on the asymmetric wavelet spectrum is herein proposed. Initially, a synthetic wavelet is generated by taking the geometric mean of the input and output signals. Subsequently, an adaptive algorithm is introduced in light of the synthetic wavelet to determine the key parameters, thereby minimizing the errors arising from subjectively selecting constant parameters. Finally, a formula is derived to compute the near-surface Q-value, enabling precise estimation of the Q-value. Both theoretical model validation and practical application outcomes demonstrate that the method presented in this study can automatically adapt to the changes in spectral asymmetry during the absorption attenuation of seismic wave propagation. It can more accurately fit various asymmetric source amplitude spectra, enhance the accuracy of Q-value modeling, and offer technical support for high-precision seismic data processing.
In recent years, the Keping fault uplift in Tarim Basin has emerged as a significant breakthrough zone for hydrocarbon exploration, where well-developed high-quality source rocks coexist with complex structural settings, creating a unique exploration environment. The combination of intense surface relief, pronounced velocity contrasts from shallow high-velocity rock masses, and multi-phase superimposed fault systems has led to technical bottlenecks in conventional seismic imaging methods, including substantial static correction errors, low velocity modeling accuracy, and structural distortion. This study systematically investigates key pre-stack depth migration (PSDM) technologies tailored for piedmont zones based on geological characteristics of the 3D seismic survey in the Kepingnan area. Three main innovations are presented: ① Integrated shallow layer modeling technology. To address near-offset data deficiencies caused by complex surface acquisition geometries, this paper develops a constrained tomographic inversion algorithm integrating uphole survey data with wide-azimuth seismic information was developed. This approach effectively enhances shallow velocity model accuracy while overcoming velocity-thickness coupling limitations inherent to conventional methods. ② True surface migration datum construction technology. By optimizing surface-consistent static correction schemes and implementing high-frequency static correction time-difference correction, the paper establishes a dynamic matching mechanism between true surface elevation and migration datum was established. This innovation significantly mitigates topographic effects on wavefield continuation and improves structural fidelity in steeply dipping stratigraphic imaging. ③ Multi-scale velocity iterative inversion technology. The paper develops a multi-azimuth grid tomography method incorporating structural constraints. Through azimuth-dependent hierarchical inversion strategies, this technique achieves synergistic improvements in both vertical and lateral velocity resolution, providing precise velocity models for deep nappe structures and buried fault systems. Field applications demonstrate that this technology system substantially enhances imaging quality of depth-migrated seismic data in Kepingnan area. Target horizons exhibit improved signal-to-noise ratios with clearer fault imaging and more accurate structural configurations. The proposed methodology provides an effective technical solution for hydrocarbon exploration in complex piedmont zones, offering practical value for advancing regional exploration progress.
The control effect of strike-slip fault systems on hydrocarbon accumulation, reservoir development, and enrichment has been fully validated in the Tarim Basin of northwestern China. The internal structural characteristics of strike-slip fault zones are extremely complex, and fine characterization of their internal structures calls for finding favorable reservoirs. Meanwhile, how to characterize the internal structural characteristics of strike-slip faults by adopting seismic methods is of great significance for the exploration and development of fault-controlled oil reservoirs. Therefore, to study the wavefield characteristics of deep strike-slip fault core-zone structures, this paper carries out model material proportioning experiments and develops physical model materials simulating deep and ultra-deep carbonate rocks and volcanic rocks. Then by using techniques such as block casting and thin-sheet implantation, a 2D seismic physical model of the core-zone structure in strike-slip fault systems is built, followed by data acquisition, processing, and analysis on the model. The model experiments show that different core-zone structural units exhibit various seismic response characteristics: specifically, the broken fractured zone presents weak and chaotic reflections, the broken breccia zone forms strong "beaded" and banded reflections, and the fault core shows a "blank" or weak reflection zone, with stronger reflections in the shallow layer than those in the deep layer. Additionally, volcanic rocks have a significant energy shielding effect on the imaging of the underlying fault zone. These seismic response characteristics of different core-zone structural units can provide a theoretical basis and technical support for identifying the core-zone structure of strike-slip faults based on seismic data.
Viscoacoustic reverse time migration (VRTM) as a high-precision imaging tool demonstrates superior adaptability for complex geological models. Conventional accurate staining VRTM can only mark single structures for staining, resulting in limited application effectiveness. To address this limitation, the paper propose a regional staining algorithm based on VRTM method. First, the paper introduces the staining algorithm for acoustic reverse time migration. The algorithm is then extended to viscoacoustic wave equations, with an implementation using staggered finite difference scheme for the stained viscoacoustic wave equation. Finally, imaging is performed using the source-normalized cross correlation imaging condition. Unlike conventional accurate staining VRTM that only images individual structures, the paper method designates specific velocity model regions as staining domains, enabling precision imaging of entire structural zones. The proposed approach was tested using both horizontal layered models and salt models. Numerical simulation of stained shot records confirm that the imaginary wavefield contains reflections exclusively from the stained structures. Regional staining imaging results provide detailed characterization of the target structures, demonstrating improved imaging quality compared to accurate staining methods.
Full waveform inversion (FWI) can provide high-resolution velocity information in marine seismic exploration. However, it often faces challenges such as inaccurate source wavelets, low-accuracy initial models, and the absence of low-frequency components in seismic data, which may lead to unreliable FWI results. This paper proposes a wavelet-independent integral wavefield waveform inversion method based on acoustic-elastic coupled equations. The method combines time integral operation on the wavefield, which enhances the low-frequency components of seismic data, with convolution operation that eliminates the influence of the wavelet. By constructing the corresponding objective function, the gradient expression for the inversion process is derived. Tests on the Marmousi-2 model show that, compared with conventional integral wavefield FWI under conditions of inaccurate wavelets, the proposed method can still effectively update the background information of P-wave and S-wave velocities, demonstrating its ability to recover background information. Moreover, in cases where the low-frequency components of seismic data are not available, the results obtained by the method proposed in this paper are largely consistent with those from the inversion of full-bandwidth seismic data, indicating its good applicability to seismic data with missing low-frequency components.
Geological modeling techniques adapting to various complex structures are widely used, such as seismic acquisition, processing, interpretation, and reservoir development. Due to the diverse and intricate nature of subsurface structures, developing modeling techniques that can handle such complexity has been a major technical bottleneck in the field. Traditional methods struggle to efficiently and accurately construct complex structural framework models, particularly when confronted with hundreds of intricately intersecting normal and reverse faults. To address this, a novel 3D complex structural modeling approach based on implicit functions is proposed. This method tightly integrates the geological conceptual model, abstract mathematical model, and geometric-topological model. It constructs an implicit mathematical model of the strata by fusing multiple types of linearly constrained geological information based on a minimum energy variational functional. The stratigraphic implicit scalar field is efficiently solved using a multi-level mesh splitting solution technique. Using logical operations on the stratigraphic implicit scalar field, the method constructs the 3D structural framework model and the stratigraphic unit model. Practical applications demonstrate that this modeling technique can adapt to various complex structural types, possesses a high degree of automation, and offers real-time incremental modeling update capabilities. It can effectively support diverse modeling applications within the geological domain.
The tomography method for tilted transversely isotropic (TTI) media faces many challenges in updating the parameter field. These challenges include the large difference in magnitude between the velocity field and the anisotropic parameter fields, which result in abnormal updates of the anisotropic parameter fields, the high computational complexity and slow convergence speed of conventional algorithms, and the inaccuracy of the parameter field update principle. In this study, the computational stability of inversion is improved by introducing the regularization matrix in the tomographic equation. A penalty function is introduced to constrain the updates of the anisotropic parameter fields, ensuring the reasonableness of each update. The VSP constraints are applied to accelerate convergence speed and improve computational efficiency. The Thomsen parameters δ and ε are updated based on the sensitivity of the angle domain common imaging gathers to the anisotropic parameter fields, and the weights and norms constraints are incorporated during the update process. The tests on TTI media model data and the processing of actual data demonstrate that the tomography method based on angle domain common imaging gathers and multi-parameter constraints have advantages such as high accuracy, fast convergence, and strong stability, which makes it suitable for high-precision velocity modeling in areas with complex underground structures and strong anisotropy.
Diffraction imaging is an important method to improve the imaging accuracy of underground small-scale geologic bodies. However, conventional seismic surveys are mainly based on reflection imaging, with weak-energy diffraction suppressed, as a result of which diffraction need to be separated and imaged separately. At present, the localized damped rank-reduction with adaptively chosen ranks (LDRRA) method widely used improves the separation accuracy of diffraction by damping the adaptively chosen singular value matrix with a damping operator, but its damping factor is mainly given manually, and all local window data use the same given damping factor. Different local windows contain different seismic data, and using the same damping factor will reduce the separation accuracy of diffraction. Therefore, a localized adaptive damped rank-reduction (LADRR) method for diffraction separation is proposed. First, based on the LDRRA framework, the Hankel matrix undergoes singular value decomposition (SVD) to truncate singular values. Second, a squared ratio of singular value is introduced to adaptively compute a damping factor for each localized data window, through which the optimal damping factor is selected to apply damping effects to the truncated singular values, thereby preserving the reflection components. Finally, the damped localized window data is subjected to inverse Hankelization and inverse Fourier transform, and then subtracted from the original wavefield to yield the separated diffraction. Theoretical simulation and field data test results demonstrate that the proposed method can separate diffraction with high accuracy, and the imaging results of the separated diffraction can get more accurate location of the underground small-scale geologic body.
The Ordos Basin holds significant natural gas exploration potential in its bauxite rocks, but currently lacks a systematic seismic prediction workflow specifically for these deposits. Research on the bauxite rocks of Benxi Formation in the northeastern part of the basin is particularly insufficient. To address this, this paper builds an integrated multi-information seismic prediction workflow for bauxite rocks in Benxi Formation of Linxing Area, northeastern Ordos Basin, with multiple technical approaches. On one hand, based on the well logging petrophysical analysis, the characteristic petrophysical responses of high gamma-ray (GR) and high Pwave impedance in bauxite rocks are identified. Waveform indication simulation and waveform indication inversion techniques are optimally selected to predict the distribution of thin bauxite layers. On the other hand, seismic response characteristics analysis reveals three typical seismic response patterns of the bauxite rocks. Through seismic forward modeling and multi-well crossplot analysis, it is pointed out that these patterns can qualitatively reflect the paleotopography before the deposition of Benxi Formation, while seismic amplitude intensity is associated with the thickness of bauxite rock and mud content. The strong amplitudes in "wide and gentle wave peaks" and "wide and gentle complex wave peaks" are favorable indicators for bauxite rock development. Therefore, waveform clustering analysis and root mean square amplitude attribute analysis techniques are selected to predict the distribution patterns of bauxite rocks. Practical application demonstrates that this workflow effectively predicts the distribution of bauxite rock in Benxi Formation in the study area, and gets a high consistency between prediction results and drilling data. The workflow can be promoted and applied to the exploration of bauxite rock target within Benxi Formation across the Ordos Basin.
Significant velocity dispersion and energy attenuation may occur when seismic waves propagate in the gas-bearing reservoirs. Based on the time-frequency analysis techniques, the inversion of the seismic wave dispersion parameters can be used to predict the gas-bearing property of reservoir. In this paper, the time-frequency spectruml analysis method based on matching pursuit is improved by the wavelet shaping technique which can be used to compress the duration of the wavelet amplitude envelope and to enhance the time resolution of the spectral decomposition result. The proposed time-frequency analysis method is combined with dispersion parameter inversion to calculate the dispersion gradient of the reflection coefficient, which is corresponding to the gas bearing property. The test results of synthetic seismic data indicate that the proposed wavelet-shaping-based time-frequency spectrum analysis shows higher time-frequency resolution compared to the conventional time-frequency analysis methods. The dispersion parameter inversion based on the proposed spectrum analysis method is applied to the real seismic data from X exploration area in the Yinggehai Basin, which verifies the effectiveness of the proposed method in gas reservoir prediction. The practical application results provide important guidance for oil and gas exploration and development.
The balanced section technology is a kind of simulation technology based on the principle of length and area conservation to restore all deformed structures to a reasonable undeformed state on the section perpendicular to the structural strike. It is difficult to obtain reliable compression or tension quantities for layered detachment deformation structures, high-angle thrust structures with intense stratum deformation, and multi-phase structures with uneven denudation. In response, this paper proposes a balanced section technology based on sample point projection through illustrative examples. First, sample points are set at fault breakpoints and key stratigraphic points, with fault sample points projected vertically and stratigraphic sample points projected layer by layer perpendicular to the top surface line. Second, starting from the baseline of the section, the balanced section is constructed block by block and layer by layer, adhering to the principles of maintaining true stratigraphic thickness and preserving the lengths of the top and bottom boundaries of the strata. This approach produces a structural evolution profile consistent with geological understanding and quantitatively determines the compression or extension of the section. Additionally, the paper examines a complex section with a double-layer structure involving detachment and bidirectional thrust deformation. It explores a layered construction method: for the upper structural layer, thickness and stratigraphic line conservation is maintained via sample point projection; for the middle ductile layer, area conservation is applied; for the lower structural layer, thickness conservation is prioritized. The research shows that geological understanding plays a dominant role in constructing balanced sections. Under the condition of maintaining true stratigraphic thickness, only one of the two principles—stratigraphic line length conservation or stratigraphic area conservation—can be applied. This technology provides a valuable reference for constructing balanced sections in similarly complex structural regions.
The reservoir space of the Lower Paleozoic carbonate rocks in the Jiyang Depression is mainly composed of fractures and dissolution pores. The reservoirs are highly heterogeneous both vertically and horizontally, which makes the prediction of the buried hill reservoirs quite challenging. Therefore, based on the development characteristics of the reservoir space and the correlation of seismic reflections, this paper proposes a seismic forward modeling method for the buried hill interior based on the dual-porosity medium model, which characterizes the seismic response features of the Lower Paleozoic buried hill interior. Meanwhile, a buried hill reservoir prediction method based on structure-oriented filtering is developed, achieving effective prediction of the favorable reservoir development zones in the Lower Paleozoic buried hill. The research results show that the development of the Lower Paleozoic carbonate reservoirs in the Jiyang Depression is the main factor causing seismic reflection anomalies, and the effective reservoir sections exhibit obvious seismic response anomalies. By comparing the data residuals before and after structure-oriented filtering, the development positions of the main inner reservoirs can be indicated. This method has achieved good results in the Pingnan buried hill and Dawangzhuang buried hill in the Dongying Sag.
Overpressure is widely developed in the deep layers of the Bozhong Sag in the Bohai Bay Basin. The complex genesis of overpressure and limited pressure measurement data in some exploration well areas as well as the low prediction accuracy of the existing Eaton method pose significant risks to safe drilling. Targeting the deep overpressure development area in the Bozhong Sag, this paper proposes an innovative three-step method for quantitative prediction of overpressure, which includes determining the cause, building a model, and conducting strong training. Specific implementation steps are as follows: ① The genesis of overpressure is analyzed with pressure measurement, logging, and other data, and the contribution rates of overpressure arising from different causes are quantitatively evaluated. ② A new pressure prediction model is established for overpressure from composite genesis based on the acoustic velocity difference (ΔV) and the contribution rate of undercompaction overpressure (α). ③ The sample data calculated with the new model is input into the convolutional neural network to achieve the multi-parameter pre-drilling prediction of overpressure from composite genesis. The research results indicate the followings: ① The overpressure of deep mudstone in the Bozhong Sag is mainly caused by hydrocarbon generation and undercompaction. The contribution rate of undercompaction overpressure varies greatly among different structures and layers, and the difference in the same area and layer is relatively small. The overall contribution rate of undercompaction overpressure is between 20% and 98%. ② Based on the model and data-driven overpressure prediction technology, accurate prediction of overpressure from different causes can be achieved in the target area. The average relative error of the pressure factor prediction results of test wells does not exceed 6.2%. This research provides a new idea for quantitative prediction of overpressure from complex genesis in areas with few wells.
The study on the sedimentary evolution process of the continental lacustrine basin in the Yanchang Formation of the Ordos Basin is of great significance for understanding the laws of generation, migration, and enrichment of petroleum resources. Based on the seismic data from the middle-upper part of the Yanchang Formation in the Ordos Basin, fractional derivative frequency-increasing processing is utilized to track the strike line of the slopes in angle-dependent horizon slice and seismic reflection event and realize isochronal classification and regional contrast. A total of 12 isochronal progradational sequences (fs1~fs12) are established within the work area. According to the size of the slopes and the depth of the lacustrine basin in different periods, a three-stage lacustrine basin evolution mode of "initial-development-shrinkage period" is proposed through the combination of the plane and section of 3D seismic data: ① The lacustrine basin during the initial period (fs1~fs3) was shallow to semi-deep water; ② in the development period (fs4~fs7), it was deep water, with sediment sources mainly from the west and southwest directions; ③in the shrinkage period (fs8~fs12), the lacustrine basin changed from deep to shallow water, and the paleocurrent underwent channel shift in the southwest sediment source area. Consequently, the filling of the lacustrine basin was completed. The progradation pattern mainly shows an S-shape or double S-shape in the lake area, and the migration angles (< 2°) show a slow upward trend. The filling of the lacustrine basin is composed of thick slope deposits and relatively thin topset deposits, belonging to normal lake regression-type sedimentation. Seismic inversion and drilling and logging data indicate that each progradational sequence exhibits positive grading sedimentary characteristics. The bottom is dominated by low-velocity dark mudstone, while the top by high-velocity sandstone. The alternating development of mudstone and sandstone indicates that during the evolution of the lacustrine basin, the paleoclimate alternated periodically between warm-humid and arid conditions. The research results provide theoretical support for the sedimentary dynamics evolution of continental lacustrine basins and the prediction of shale oil sweet spots.
Fractured reservoirs developed along strike-slip faults typically exhibit superimposition of pores, fractures, cavities, and fluids inside upon the fault structure itself. Seismic facies analysis alone is insufficient for accurate interpretation of such systems. Conventional coherence attributes can only delineate the outer boundaries of these fractured reservoirs, while structure tensor attributes merely capture the dominant energy zones—both falling short of the precision required for effective exploration and development. To address this challenge, this study proposes a refined method for identifying the core zone architecture of fault-controlled fractured reservoirs in an overlapped segment of a well area located in the Tarim Basin. First, the influence of strong reflections from the top of the Ordovician and horizontal layering is removed through spatial-wavenumber domain filtering, which thereby isolates the seismic reflection energy associated with the vertically-oriented fractured reservoir. Next, the first eigenvalue attribute volume of the structure tensor is extracted, and faults are tracked by computing the gradient direction of this eigenvalue. Finally, probabilistic fusion is applied to integrate the first eigenvalue and the tracked fault surfaces, yielding a final data volume that delineates the core zone structure of the fractured reservoir. Field examples demonstrate that the proposed method effectively characterizes both the core zones and the surrounding fractured belts of fault-controlled fractured reservoirs, showing strong agreement between well data and seismic interpretations. This method can be effectively extended to other similar well areas.
Seismic impedance inversion is a technical means to quickly estimate the thickness of underground rock layers and evaluate the quality of reservoirs by using post-stack seismic data. Howerver, limited by the inappropriateness of the inversion itself, the process is highly susceptible to the influence of the observed data and the model bias, which leads to the instability of the inversion results. Instability arising from noise or the forward model during the solution process can be suppressed by appropriately introducing smoothness, sparsity or structural assumptions in the objective function. The total-variation regularization optimization method can preserve formation edge features, but may also lead to blocky structures of the inversion results. To overcome this defect, this paper introduces hybrid fractional-order total-variation regularization constraints to characterize the inter- and intra-stratum variability characteristics in a more reasonable way. First, an objective function with hybrid total-variation regularization constraints is built. Then, the orthogonal finite-memory quasi-Newton method is used to solve this complex objective function, so as to improve the resolution and stability of seismic impedance inversion. Test results and real data application demonstrate that the hybrid fractional-order total-variation regularization can be better adapted to the inversion of lumped and smooth hybrid models than total-variation regularization and
With the development of deep learning (DL) techniques, multimodal learning (MML) has been widely applied to solve geophysical inversion problems. Currently, the electromagnetic data with different feature attributes are usually grouped as a whole input to the network when DL is used to invert controlled-source electromagnetics (CSEM) data, which can be regarded as a single-modality learning strategy. MML involves extracting unique features from various modalities of data and leveraging associated information to establish an input-output mapping relation. In this study, electromagnetic data of different frequencies are used as different modalities, and MML is combined with UNet to realize 2D inversion of frequency-domain marine CSEM data. The inversion results on a test set of synthetic data show that the proposed method can accurately reconstruct the resistivity structure of seafloor media, determine the location of seafloor high-resistance bodies, and map their distribution. Compared to traditional inversion methods, this approach is notably efficient and stable. In addition, its inversion performance on noisy data indicates that it has the potential to be applied to field data.
