Seismic Impedance Research Articles

Seismic Impedance Inversion Based on Improved Cycle-WGAN

Abstract The application of deep learning methods for seismic impedance inversion usually requires a large amount of labeled data to train the network, while labeled data available in practical applications is often limited, which affects the effectiveness of the relevant methods. In order to address this problem, this paper proposes one kind of deep learning method of a closed-loop cycle Wasserstein generative adversarial network (Cycle-WGAN) for seismic impedance inversion based on the combination of “data-driven and model-driven”. The method uses a small amount of labeled data and unlabeled seismic data generated by the forward modeling. It constitutes a bidirectional cycle of inversion and forward modeling through the generative and adversary networks for the inversion and generative and adversary networks for the forward modeling by convolution model, which improves the conventional GAN networks. The proposed method also introduces the Wasserstein loss function to improve the neural network’s training stability. Tested on the Marmousi model with complex structure, the proposed Cycle-WGAN network can effectively obtain the seismic impedance inversion results. Moreover, it is highly robust when seismic data are noisy and has higher accuracy than the inversion results from some conventional neural networks, such as RNN (Recurrent Neural Network), TCN (Temporal Convolutional Network, WGAN (Wasserstein Generative Adversarial Network), etc.

Sparse-spike seismic inversion with semismooth newton algorithm solver

Seismic prospecting has been widely used in the exploration and development of underground geological resources, such as mineral products (e.x., coal, uranium deposit), oil and gas, groundwater, and so forth. Seismic impedance is a physical characteristic parameter of underground formation, which can be used in lithologic classification, rock characterization, stratigraphic correlation, and further mineral reservoir prediction, reserve estimation, and so forth. To estimate impedance from seismic data, one must perform reflectivity series inversion first. Under a simple exponential integration transformation, the reflectivity series can give the final estimated impedance. Sparse-spike seismic inversion is the most common method to obtain reflectivity series with high resolution. It adopts a sparse regularization to impose sparsity on reflectivity series. From sparse reflectivity series, the final estimated impedance has blocky features to make formation interfaces and geological edges precise, which is very important to accurately delineate the distribution range of mineral resources. The development of sparse-spike seismic inversion is still facing major challenges of fast optimization algorithms in real-life application, especially for massive seismic data in 3D case. Semismooth Newton algorithm (SNA), which is a second order mehtod and has super-linear, even quadratic convergence rate, is used to solve sparse-spike seismic inversion. The proposed algorithm has been compared with common used algorithms through a synthetic seismic trace and a 3D real seismic data volume. The results show that the proposed algorithm has faster convergence rate and fewer computation time. It provides a new effective algorithm to solve sparse-spike seismic inversion.

Mapping Ocean Gateways from 3D Seismic Data Using Color Processing and Analogues from Seismic Data

Mapping sedimentary features to delineate ancient ocean gateways from 3D seismic data has long been a challenge due to their limited vertical resolution. We present the technique of color processing of seismic data which improves the vertical resolution to very few seismic samples corresponding to 5–10 m. The result is a representation of the seismic impedance contrast correlation in Red-Green-Blue (RGB) colors, which creates images similar to satellite images that assist the geological interpretation because the RGB colors can be associated with mud-rich and sand-rich sediments. Using this approach, we have extracted six different sedimentary bedforms from different bottom flow settings and correlated these with the bedform-velocity matrix by Stow et al. (2009). Then we studied bedform assemblies in their regional context for the area covered by the 3D seismic data set. We conclude with comparing a modern and an ancient analogue – both from 3D seismic data – and infer paleo flow direction and velocity using oceanographic measurements available for the modern analogue. The method provides insights into ocean bottom currents and their impact on the formation of contourite depositional systems in modern and ancient ocean gateways which contribute to the reconstruction of the paleoclimate as an input to present day climate change models.

Machine learning assisted reservoir characterization for CO2 sequestration: A case study from the Penobscot field, Canada offshore

Rising greenhouse gas emissions, especially CO2, intensify global warming. Carbon Capture and Storage (CCS) is vital for reducing emissions from industrial sources, crucial for addressing climate change urgently. This study explores reservoir characterization challenges for CCS in the Penobscot field, offshore Nova Scotia. We used to cross-plots of geophysical logs to establish relationships between petrophysical properties and subsurface litho-facies, differentiating sand and shale facies within the Mississauga Formation (Early to Middle Cretaceous). Seismic impedance inversion distinguished between reservoir and non-reservoir facies. Multi-attribute assisted transformed seismic data and a support vector machine algorithm predicted litho-facies probabilities and effective porosity volumes in 3D space. The permutation predictor importance analysis was performed to assess the relative importance of input features in facies probabilities and effective porosity prediction. The structural component delineated the CO2 injection trap boundary. Integrated analysis of structural configuration, litho-facies probability, effective porosity, and cap-rock sealing integrity validated zones for CO2 storage. The dry, abandoned well L-30 in the structural high zone is recommended as a pilot CO2 injector well. This study provides critical insights into reservoir characterization for CO2 sequestration, aiding climate change mitigation through effective CCS, and recommends geomechanical studies and time-lapse seismic monitoring.

A novel approach to understanding the genesis and distribution of widespread reservoirs at the basin edge: A case study of gas-bearing sandstone in the Sangonghe Formation, Junggar Basin, Western China

A 20 m thick sandstone drilled in the second member of the Sangonghe Formation in the Qianshao area of the Junggar Basin has been identified to be a high-quality gas-bearing reservoir. However, recent missions reveal an uncertainty in reservoir distribution and bring high risk to the subsequent gas development, although the gas-bearing reservoir has been encountered by several drillings and proved to be widespread. To investigate the accurate distribution and reveal the evolution of the reservoir, we reconstruct the stratigraphic framework and depositional stages by a high-revolution sequence stratigraphic method, effectively identify the gas-bearing reservoir by seismic attributes refusion and seismic stratigraphic slices, and finally map the distribution strata distribution at different stages. The results indicate that the landward onlap phenomenon in the seismic data indicates a retrogradational stratigraphic structure during the water level rise rather than the equal-thickness stratigraphic pattern under stable water conditions. According to the retrogradational stratigraphic structure, the real driver for the widespread characteristic of a gas-bearing reservoir is the backward staggered movement of multistage sand bodies during the transgressive process rather than single-stage deposition. The fusion of seismic acoustic impedance and main frequency attributes provides an effective survey to identify the accurate distribution of the reservoir, by which interpretation of a stratigraphic slice along the vertical centerline of the reservoir extracted from the multiattribute fusion survey can present the distribution and evolution of the three stages on only one map. Achieved by an appropriate interpretation on the map, two sandy debris flow bodies are identified as being deposited at the bottom successively during the first two transgressive stages. They are completely staggered in the source direction with a clear boundary between well Qs9 and Qs7. Subsequently, the subsequent rise of the water level led to the deposition of a large-scale multibranch shallow-water delta, which exhibits the characteristics of multistage, multisource, and multibranch. The results not only advance our comprehension of widespread sand bodies but also furnish fundamental insights for the development of lithologic condensate gas reservoirs in the Qianshao area.

Using the wavelet transform for seismic wave impedance inversion

Model-based inversion technology has become one of the necessary technologies in the seismic exploration industry. It is frequently used to estimate many subsurface attributes, i.e., velocity, impedance, attenuation, etc. It is important to note that the accuracy of model-based inversion relies on the precision of the initial model, which suffers from insufficiency of low-frequency information in seismic data. Enhancing the high-frequency content and accuracy of low frequencies in the initial model can improve solution accuracy. Conventional initial models fail to adequately represent the high-frequency details of lateral geologic variations and to capture low-frequency components accurately. We develop a wavelet transform solution for this problem. Initially, we demonstrate that seismic records approximate the wavelet transform of logarithmic impedance. Consequently, we use the wavelet inverse transform to reconstruct the mid- and high-frequency information of seismic impedance, highlighting the detailed spatial variations. Furthermore, we apply kriging interpolation, based on subspace interpolation principles, using well impedance and seismic record waveforms to derive the low-frequency impedance from the scale inverse transform. Wavelet transform theory ensures a perfect match between the low- and high-frequency components in the inversion result. The frequency components of the inversion result are balanced, with no deficiencies or redundancies within the seismic data’s high cutoff frequency band. Thus, our method of initial model building is a type of inversion method. In addition, this method is simple and efficient because fast convolution processing can be performed. Model experiments and practical data inversions confirm the method’s feasibility and its ability to enhance resolution.

A depth-variant wavelet extraction method based on the orthogonal matching pursuit for direct inversion

The results of pre-stack depth migration reflect subsurface structures directly. However, it is an urgent geophysical problem that how to deal with this non-stationarity exhibited by depth-domain seismic data and achieve high precision reservoir prediction. To address this issue, we develop a depth-variant wavelet extraction method based on the orthogonal matching pursuit and attain direct inversion of the depth-domain acoustic impedance using the depth-variant wavelets. First, the parameters of source wavelets estimated by complex seismic trace analysis are utilized to construct the overcomplete dictionary. Secondly, the overcomplete dictionary is used to decompose the depth-domain seismic traces by orthogonal matching pursuit. Thirdly, we extract the depth-variant Ricker wavelets from the decomposed optimal atoms. Test of examples demonstrates that the proposed depth-variant wavelet method has high accuracy. The synthetic seismogram obtained by the proposed method has high Pearson correlation coefficients of 99%, which verifies that our method is feasible and accurate. Next, a depth-domain impedance trend constraint is introduced into the objective function of the conventional basis pursuit inversion to enhance the lateral continuity. Finally, the objective function with impedance trend constraint is solved by basis pursuit for depth-domain acoustic impedance. We test the direct depth-domain acoustic impedance inversion method on the synthetic and field data, which indicates that our method has high accuracy and robustness. The mean relative error of its inversion result is only 0.9% for the noise-free example and about 5% for the strong noisy example. Therefore, our method can be effectively applied to the seismic-well calibration and seismic impedance inversion in the depth domain. And it has powerful potential in reservoir characterization, fluid prediction and attribute extraction in the depth domain. Additionally, the energy distribution features of the Ricker wavelet are derived and analyzed, which can guide the application of the Ricker wavelet in seismic signal analysis.

Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis

A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is quite expensive. However, large amounts of unlabeled data exist in these domains. The deep semi-supervised learning (DSSL) approach leverages unlabeled data to improve supervised learning performance using deep neural networks. This approach has succeeded in image recognition, text classification, and speech recognition. Nevertheless, there have been few works on pre-stack seismic reservoir characterization, in which knowledge of rock and fluid properties is fundamental for oil exploration. This paper proposes a methodology to estimate acoustic impedance using pre-stack seismic data and DSSL with a recurrent neural network. The few labeled datasets for training were pre-processed from raw seismic and acoustic impedance data from five borehole logs. The results showed that the acoustic impedance estimation at the well location and outside it was better predicted by the DSSL compared to the supervised version of the same neural network. Therefore, employing a large amount of unlabeled data can be helpful in the development of seismic data interpretation systems.

Appraisal of reservoir quality for hydrocarbon-bearing deep-water Miocene sandstones incised valley, south-east Asian offshore Indus: An application of seismic attributes and instantaneous spectral porosity quantitative reservoir simulations

Incised marine valleys (IVS) are hot topics in exploring the stratigraphic oil and gas-bearing plays. Multiple channelized sandstone lenses at varying depths [m], thicknesses [m], and porosities [%] constrain seismic impedance. The presence of hydrocarbon-bearing resources affects the seismic impedance (density (g/cc) and velocity (m/s)). Therefore, a quantitative prediction has been carried out for determining the thickness [m], porosity [%], and depths [m] of laterally distributed channelized sandstone lenses (SLS) for IVS, Indus offshore Basin (IOB), Pakistan, using 2-D instantaneous spectral porosity quantitative modelling (2DSSM), continuous wavelet transforms-based (CWT) 2-D instantaneous spectral density modelling (2DSSDM), and spectral decomposition tools. The 2DSSM remained limited in predicting the number of channelized sandstone lenses and their quantitative stratigraphic attributes. The 45-Hz-based processing of conventional 2DSSM has resolved the two channelized sandstone lenses of the stratigraphic trap. The deepest channelized sandstone lens has attained 1–6 m thickness with a lateral extent of 3 km, within the porosity range of 18–33 %. The highest confidence level for predicted petrophysical attributes such as 13 m-thick pay zones, −0.08, −0.067, and −0.07 acoustic impedances [g/c.c.*m/s], and 28 % porosities with R2 > 0.85 have validated interpretations. The response of 45-Hz CWT waveform-based inverted density and thickness simulations has predicted the highest thicknesses and lowest densities of reservoir sandstones within the meandering channel belt of the deepwater depositional system. The predicted densities and thicknesses for the coarse-grained sandstone lenses of point bars were 1.8–1.9 g/cc and 15 m, respectively. In the same way, the quantitative estimates of predicted density and simulated thickness have shown a strong coefficient correlation (R2 > 0.80), which confirms the presence of gas-bearing prospects within the IVS. The facies-controlled migration is thought to be the movement of the reservoir facies of the point bars and channelled sandstone-filled lenses to the side.

Stochastic seismic acoustic impedance inversion via a Markov-chain Monte Carlo method using a single GPU card

Seismic acoustic impedance inversion plays an important role in understanding subsurface structures and obtaining subsurface properties. The stochastic approach is one of the methods used for impedance inversion, and it aims to produce more reliable results by accounting for modeling uncertainty. Stochastic inversion represents the uncertainty of a subsurface model as a probability distribution and uses this distribution to estimate model parameters. In this study, seismic acoustic impedance inversion was performed using a Markov-chain Monte Carlo approach, which is a sampling method used in the stochastic process. Utilizing Bayesian inference based on prior information and observed data, we implemented acceptance probabilities and performed acoustic impedance inversion for field data through iterative calculations. We employed a single GPU card to execute the inversion algorithm and conducted a comparative analysis with the results obtained using a cluster composed of multiple CPU cores. Through a computational speed analysis, the efficiency of the algorithm using a GPU was verified, while uncertainty analysis was employed for algorithm validation. Through these analyses, we confirmed the feasibility of applying our developed GPU algorithm to tasks that require the inversion of extensive data, such as high-resolution seismic exploration and CO2 storage monitoring.

Cohesive approach for determining porosity and P-impedance in carbonate rocks using seismic attributes and inversion analysis

AbstractThe assessment of hydrocarbon flow through seismic and well-log data presents a persistent challenge in determining porosity. The acoustic impedance section provides a visual representation of the layers, while the raw seismic data showcase the subsurface reflectors that exist within the rock layers. The accuracy of acoustic impedance is widely acknowledged to surpass that of seismic data as a representation of reality. The primary objective of this study is to convert seismic reflector data into acoustic impedance values, which provide insights into the layer properties based on lithology. This approach enhances the accuracy of seismic inversion results by aligning them more closely with actual geological conditions. Seismic inversion is employed to ascertain the physical characteristics of the rock, including acoustic impedance and porosity. Carbonate reservoirs are recognised for their complex pore structures and heterogeneity, which present difficulties in their characterisation. The objective of this research is to predict the porosity and identify the reservoir within the dense carbonate reservoirs in Central Luconia, Sarawak. These objectives are achieved by employing a porosity and acoustic impedance cross-plot and improved precision and predictability through the integration of seismic attribute interpretation and deterministic seismic inversions. The uniqueness of our approach stems from the incorporation of various geophysical techniques to detect reservoirs that have hydrocarbon deposits. A correlation is observed between seismic inversion acoustic impedance and porosity within the zone of interest, indicating an estimated porosity range of 10–35%. The analysed area demonstrates the possibility of containing a hydrocarbon based on the observed relationship between porosity and impedance, as well as the outcomes of the inversion analysis.

Cycle-consistent convolutional neural network for seismic impedance inversion: An application for high-resolution characterization of turbidites reservoirs

Acoustic impedance is a subsurface layer parameter crucial for reservoir characterization due to its relationship with petrophysical properties. Seismic impedance inversion is the routine conventionally used to calculate the acoustic impedance in 3D seismic datasets. Deep learning-based seismic inversion has recently gained attention due to its capacity to establish non-linear relationships between observed data and model parameters, producing robust acoustic impedance estimates. We employed a 1D cycle-consistent convolutional neural network (CNN) to perform the high-resolution seismic impedance inversion in the turbidite reservoirs of the Jubarte Field, Campos Basin, Brazil. The neural network was trained using geostatistics-based pseudo-wells with high pattern variability. Before applying the trained CNN, we performed the seismic data pre-conditioning to remove high and low-frequency noises affecting the data amplitudes, making the dataset more suitable for seismic impedance inversion. Our results show that the deep learning-based inversion produced a high-resolution estimate, allowing an accurate internal characterization of turbidite lobes. Quantitatively, the estimated average correlation coefficient in the eight wells evaluated in this study was 0.78. We observed that the pre-conditioning step was important for this application since the 1D architecture utilized could not deal properly with the noise as it disregards lateral connections. 2D and 3D networks may address this issue. Compared to the open-loop CNN and the traditional model-based inversion, the cycle-consistent network produced the best estimate, with good lateral continuity, vertical resolution, and correlation. We support that modern deep-learning architectures like the one presented can be efficiently integrated into reservoir characterization workflows for enhancing subsurface assessment.

Seismic vulnerability signal analysis of low tower cable-stayed bridges method based on convolutional attention network

Abstract Due to the particularity and complexity of sedimentary environments, the wave impedance differences between different reflection interfaces in underground media may vary greatly. Therefore, an encoder–decoder neural network is proposed to enhance erroneous seismic weak reflection signals. The convolutional neural network (CNN) has the problem of difficulty in parallel computing, resulting in slow network training and computational efficiency. Considering that attention has an innate global self-attention mechanism, can compensate for long-term dependency deficiencies, and has the ability to perform parallel computing, which greatly compensates for the shortcomings of CNNs and recurrent neural networks, a seismic impedance inversion method based on convolutional attention networks is proposed. To improve the ability to extract noise, residual structure and convolutional attention module (CBAM) were introduced. The residual structure utilizes residual jump to weaken network degradation and reduce the difficulty of feature mapping. The CBAM uses a mixed attention weight of channel and space, which can enhance features with high correlation and suppress features with low correlation. In the decoder, in order to improve the dimension recovery ability of feature fusion, bilinear interpolation is selected for upsampling. The application results of the model and actual data indicate that this method can effectively enhance the weak reflection signals caused by the formation itself and improve the reservoir identification ability of seismic data.

Stable and efficient seismic impedance inversion using quantum annealing with L1 norm regularization

Abstract Seismic impedance inversion makes a significant contribution to locating hydrocarbons and interpreting seismic data. However, it suffers from non-unique solutions, and a direct linear inversion produces large errors. Global optimization methods, such as simulated annealing, have been applied in seismic impedance inversion and achieved promising inversion results. Over the last decades, there has been an increasing interest in quantum computing. Owing to its natural parallelism, quantum computing has a powerful computational capability and certain advantages in solving complex inverse problems. In this article, we present a stable and efficient impedance inversion using quantum annealing with L1 norm regularization, which significantly improves the inversion accuracy compared to the traditional simulated annealing method. Tests on a one-dimensional 10-layer model with noisy data demonstrate that the new method exhibits significantly improved accuracy and stability. Additionally, we perform seismic impedance inversion for synthetic data from the overthrust model and field data using two methods. These results demonstrate that the quantum annealing impedance inversion with L1 norm regularization dramatically enhances the accuracy and anti-noise ability.

Multichannel closed-loop seismic acoustic impedance estimation with nonlinear correction

Multichannel closed-loop seismic acoustic impedance estimation with nonlinear correction

Multitrace Seismic Impedance Inversion With Structure-Oriented Minimum Entropy Stabilizer

Multitrace Seismic Impedance Inversion With Structure-Oriented Minimum Entropy Stabilizer

The Domain Adversarial and Spatial Fusion Semi-Supervised Seismic Impedance Inversion

The Domain Adversarial and Spatial Fusion Semi-Supervised Seismic Impedance Inversion

Semi-Supervised Seismic Impedance Inversion With Convolutional Neural Network and Lightweight Transformer

Semi-Supervised Seismic Impedance Inversion With Convolutional Neural Network and Lightweight Transformer

Deep learning for high-resolution multichannel seismic impedance inversion

Seismic impedance inversion can obtain subsurface physical properties and plays an important role in hydrocarbon and mineral exploration. Due to the inaccurate and insufficient seismic data, the inverse problem is ill posed as characterized by unreliability and nonuniqueness of solutions. Regularization techniques relying on certain prior information often are introduced to force the inverse problem to obtain stable results with predetermined characteristics. However, for complex geologic conditions, these methods usually have difficulty achieving satisfactory accuracy and resolution. We develop a deep-learning (DL)-based multichannel impedance inversion method that flexibly incorporates prior information by training with numerous realistic structural 2D impedance models based on the features of field data. The DL framework is supplemented by the attention mechanism and residual block to automatically learn more features and details from training data. A novel hybrid loss function, combining [Formula: see text] loss and multiscale structural similarity loss, is introduced to enhance the network’s capacity for learning structural features. Synthetic and field data examples demonstrate that our method can effectively produce inversion results with high resolution, good lateral continuity, and enhanced structural features compared with traditional methods.

Evaluation of Carbonate Reservoir with Integration of Petrophysical Analysis and Seismic Impedance Inversion in Banggai Basin, Sulawesi

Banggai Basin is controlled by the collision between Banggai-Sula micro-continent and East Sulawesi ophiolite belt, forming a thrust-sheet anticline structure that has potential to trap hydrocarbon. The Siotu-1 well was drilled right into the thrust-sheet anticline structure targeting Miocene carbonate platform layer. The same structure has proven to be an oil trap at the Tiaka oilfield which is located adjacent to the Siotu-1 well. This study aims to evaluate the presence of hydrocarbon in the Siotu-1 well with characterization of the Miocene carbonate reservoir. It is carried out by integrating petrophysical analysis from the Siotu-1 well log data and 2D seismic impedance inversion of the Tarib-1 line. Five reservoir zones have shale content values range of 21.91-37.53%, effective porosity values range of 9.7-14.62%, water saturation values range of 88.5-98.27%, and permeability values range of 2.53-8.16 mD. The results of the porosity calculation are validated by the model of seismic inversion where the reservoir zone with an acoustic impedance in the range of 7600-13450 ((m/s)/(g/cc)) is a carbonate layer with fair to excellent porosity (10 to> 25%) and reservoir zone with acoustic impedance values range of 13450-16200 ((m/s)/(g/cc)) as carbonate layer with poor porosity (< 10%). The net pay zone in reservoir zone 4 with a thickness of 4.5 feet and a net-to-gross of 0.006 are shown as the lumping result. It is concluded that Miocene carbonate reservoir in the Siotu-1 well met the criteria as a good reservoir rock but insignificant of hydrocarbon potential for further development.