Yaneng Luo

In poststack seismic inversion, the accurate estimation of the source wavelet is crucial yet poses a significant challenge with field data. We developed a robust, source-independent poststack impedance inversion method based on a novel convolution-based objective function. This function is constructed as the difference between two convolution terms: one between the synthetic record and the observed trace at a known well and the other between the observed data and the synthetic seismogram at the known well. This design makes the objective function inherently agnostic to the source wavelet. Moreover, the implementation of our method, which entails a rederived forward operator and data preprocessing, yields additional benefits. The synthetic seismogram enhances noise robustness by functioning as a band-pass filter. In addition, the linearity of the objective function allows it to be solved directly with the conventional conjugate gradient method. Tests on synthetic and field data demonstrate the superior performance of our function.

Seismic petrophysical inversion extracts petrophysical properties from seismic reflections, but conventional methods typically assume uniform pore structures, which limits their effective application in complex-pore reservoirs. Conventional inversion methods using either elastic or seismic data suffer from cumulative errors or low resolution with poor robustness to noise. To address these issues, we develop a coupled inversion method to simultaneously estimate porosity, fluid saturation, mineral content, and the equivalent pore aspect ratio using elastic (P-/S-wave velocities and density) and seismic (amplitudes of different angle gathers) data. The method adopts a Bayesian linear inversion approach with a seismic-petrophysical linearized forward model that represents pore complexities by including the pore aspect ratio as one of the objective parameters to be estimated. The inversion is conditioned on coupled elastic and seismic data. We conduct a comparison between our coupled inversion and conventional petrophysical inversion techniques, and we determine that our method improves the inversion accuracy and robustness with respect to noise. Moreover, we also define the initial model of petrophysical parameters required for the linearized inversion. This work provides an efficient and stable petrophysical inversion method for reservoirs with complex-pore structures. The inversion output is a model of the petrophysical properties of rock, fluid, and fracture properties.

Following the advancement of machine learning-based seismic feature classification techniques for complex reservoirs, the acquisition and analysis of reliable seismic samples involved in seismic facies analysis and network-based inversion have emerged as a current research hotspot in the field of intelligent seismic processing. Many investigations focus on the improvement of model classification algorithms and neural networks. However, creating and collecting labels for massive seismic data are highly time-consuming and laborious, and suffer from sample unreliability and category imbalance in the case of small-sample labels. To address such problems, a multiscale and multilabel consistent principal component analysis-linear discriminant analysis (PCA-LDA) algorithm to learn a robust feature discriminative dictionary for classification is presented. In addition to the automatic use of multilabels from well logs and core analysis, we have associated multiscale with well trajectory locations to enrich sample information and enhance the reliability of the samples during 3D sample acquisition. More specifically, we begin by proposing an approach for the automatic collection of multiscale multilabel 3D poststack seismic samples along the well track. Next, the multilabel sequence in the scan window is fed into the Boyer-Moore majority vote algorithm for sample segmentation, which constructs multilabel hierarchies for each sample. Then to enhance the model training bias due to small-sample label imbalance, we develop a novel label-shuffling balanced strategy, which obtains a complete database by filling random unduplicated augmented training samples (spatial and frequency-domain augmentation operations). Finally, the linear robustness decision-making space of PCA-LDA is obtained using the feature mapping space of PCA, as well as its visual representation. Experimental results on synthetic and field seismic data demonstrate that robust feature extraction with a trustworthy and complete multiscale and multilabel sample database increases classification accuracy.

Low-frequency information is important in reducing the nonuniqueness of absolute impedance inversion and for quantitative seismic interpretation. In traditional model-driven impedance inversion methods, the low-frequency impedance background is from an initial model and is almost unchanged during the inversion process. Moreover, the inversion results are limited by the quality of the modeled seismic data and the extracted wavelet. To alleviate these issues, we have investigated a double-scale supervised impedance inversion method based on the gated recurrent encoder-decoder network (GREDN). We first train the decoder network of GREDN called the forward operator, which can map impedance to seismic data. We then implement the well-trained decoder as a constraint to train the encoder network of GREDN called the inverse operator. Besides matching the output of the encoder with broadband pseudowell impedance labels, data generated by inputting the encoder output into the known decoder match the observed narrowband seismic data. The broadband impedance information and the already-trained decoder largely limit the solution space of the encoder. Finally, after training, only the derived optimal encoder is applied to unseen seismic traces to yield broadband impedance volumes. Our approach is fully data driven and does not involve the initial model, seismic wavelet, and model-driven operator. Tests on the Marmousi model illustrate that our double-scale supervised impedance inversion method can effectively recover low-frequency components of the impedance model, and we determine that low frequencies of the predicted impedance originate from well logs. Furthermore, we apply the strategy of combining the double-scale supervised impedance inversion method with a model-driven impedance inversion method to process field seismic data. Tests on a field data set indicate that the predicted impedance results not only reveal a classic tectonic sedimentation history but also match the corresponding results measured at the locations of two wells.

Amplitude variation with incidence angle (AVA) analysis is an essential tool for discriminating lithology in hydrocarbon reservoirs. Compared to traditional AVA inversion using only compressional wave (P-wave) information, joint AVA inversion using PP and PS seismic data provides better estimation of rock properties (e.g., density, P- and shear wave [S-wave] velocities). Currently, the most used AVA inversions depend on the approximations of the Zoeppritz equations (e.g., the Shuey and Aki-Richards approximations), which are not suitable for formations with strong contrast interfaces and seismic data with large incidence angles. Based on the previous derivation of the accurate Jacobian matrix, we have found that the sign of each partial derivative of reflection coefficient with respect to the P-, S-wave velocities and density changes across the interface represents a good indicator for the reflection interfaces. Accordingly, we adopt an adaptive stratified joint PP and PS AVA inversion using the accurate Jacobian matrix that can automatically obtain the layer information and can be further used as a constraint in the inversion of in-layer rock properties (density and P- and S-wave velocities). Due to the use of the exact Zoeppritz equations and accurate Jacobian matrix, this proposed inversion method is more accurate than traditional AVA inversion methods, has higher computational efficiency, and can be applied to seismic wide-angle reflection data or seismic data acquired for formations with strong contrast interfaces. The model study shows that this proposed inversion method works better than the classic Shuey and Aki-Richards approximations at estimating reflection interfaces and in-layer rock properties. It also works well in handling a part of the complex Marmousi 2 model and real seismic data.

Gas reservoir characterization is one of the frontiers in seismic exploration. Acoustic impedance, one of the most effective seismic attributes, aims to describe the spatial distribution of rock properties. However, using acoustic impedance only is insufficient to describe gas-bearing layers accurately, in the case of rapid lithologic changes and complex geology in the deepwater area. The reflection seismograms indicate an absorption effect when seismic waves propagate through gas-bearing layers. The absorption effect can be used as an additional attribute to help gas reservoir characterization. Therefore, we have developed a new attribute for gas reservoir characterization in this study, which integrates the results of acoustic impedance and absorption coefficient. We estimate the acoustic impedance model by using poststack impedance inversion and then we calculate probability distribution functions. Functions are classified into gas-bearing and nongas layers. We discuss an absorption coefficient and obtain it from the spectrum gradient, in which the gradient is calculated by spectral decomposition using the matching pursuit method. We apply the new attribute to characterize the spatial distribution and thickness of deepwater gas reservoirs in the Pearl River Mouth Basin. Well-log and geologic information indicate that the study area has an enrichment of gas reservoirs. Field data application indicates the explicit distribution of the gas reservoir and in accordance with the well-log information, which indicates that our attribute can improve gas reservoir characterization.

Subsalt carbonate rocks in Brazil’s deepwater region have broad prospects for oil and gas exploration and development. Due to the low-frequency bandwidth of the seismic data and the poor signal quality for this kind of reservoir target, there is a demand for accurate seismic prediction methods. We employ the facies-controlled inversion using a low-pass filter matrix to ensure the accuracy of the low frequency and to improve the robustness of the inversion results. We integrated the concept of adaptive regularization constraint of the Zoeppritz equation into the generalized linear inversion theory framework, which overcomes the shortcomings of the approximate equation. Making full use of the large angle prestack seismic information, Zoeppritz equation inversion improves the accuracy of the inversion results. The application of this method in carbonate reservoirs under extremely thick salts in the Santos Basin of Brazil indicates the feasibility and practicality of the proposed integrated prediction method.

Prestack acoustic full-waveform inversion (FWI) can provide long-wavelength components of the P-wave velocity by using low frequencies and long-offset direct/diving/refracted waves, which could be simulated via a large space grid, and it is weakly sensitive to density. Poststack impedance inversion can usually quickly yield high-resolution impedance, and it is sensitive to density. Therefore, we have combined these two methods to develop an FWI-driven impedance inversion. Our method first uses FWI to obtain the long-wavelength velocity with a guaranteed overlap between the high frequencies of the velocity and the low frequencies of the poststack data. Then, the fitting rock-physics relationship between the density and the velocity is adopted to translate the FWI velocity into the low-frequency impedance. Finally, the resulting low-frequency impedance is used to construct an a priori constraint for poststack impedance inversion. The method has the ability to solve the overlap between the FWI-based converted prior impedance model and poststack data, and it can thereby yield a broadband absolute impedance result. We adopt a Marmousi II model example and a real data case to test the performances of the FWI-driven impedance inversion and indicate its advantages compared with the conventional well-driven impedance inversion that uses well logs and interpreted horizons to build the prior impedance model. The synthetic data example demonstrates that well-driven impedance inversion produces a result with a relatively large deviation to the true impedance model at complex structure zones. However, FWI-driven impedance inversion favorably recovers all interesting sediment layers at complex structure zones. The real data example illustrates that well-driven impedance inversion yields a result with a distinct footprint of the prior model created from well logs and horizons. On the other hand, we find that FWI-driven impedance inversion yields a geologically reasonable solution, which not only conforms to the time-space variation trend of the well logs, but it also reveals a basin structural-depositional evolution.

In recent years, many important discoveries have been made in the marine deepwater hydrocarbon exploration in the South China Sea, which indicates the huge exploration potential of this area. However, the seismic prediction of deepwater reservoirs is very challenging because of the complex sedimentation, the ghost problem, and the low exploration level with sparse wells in deepwater areas. Conventional impedance inversion methods interpolate the low frequencies from well-log data with the constraints of interpreted horizons to fill in the frequency gap between the seismic velocity and seismic data and thereby recover the absolute impedance values that may be inaccurate and cause biased inversion results if wells are sparse and geology is complex. The variable-depth streamer seismic data contain the missing low frequencies and provide a new opportunity to remove the need to estimate the low-frequency components from well-log data. Therefore, we first developed a broadband seismic-driven impedance inversion approach using the seismic velocity as initial low-frequency model based on the Bayesian framework. The synthetic data example demonstrates that our broadband impedance inversion approach is of high resolution and it can automatically balance between the inversion resolution and stability. Then, we perform seismic sedimentology stratal slices on the broadband seismic data to analyze the depositional evolution history of the deepwater reservoirs. Finally, we combine the broadband amplitude stratal slices with the impedance inversion results to comprehensively predict the distribution of deepwater reservoirs. Real data application results in the South China Sea verify the feasibility and effectiveness of our method, which can provide a guidance for the future deepwater hydrocarbon exploration in this area.