With the gradual disappearance of traditional structural reservoirs, widely distributed deep thin lithologic reservoirs developed in continental and transitional sedimentary environments may offer significant petroleum reserves. Reservoir characterization through conventional impedance inversion methods without appropriate selection of regularization parameters and other empirical constraints cannot easily detect and assess these reserves, however, due to their size, variation, and features that conceal them. We have evaluated a nonlinear chaos inversion method that uses well data and seismic facies to characterize the oil and gas reservoirs of the Tazhong oil field in the Tarim Basin, China. The inverted results exhibited high agreement with the well data in highlighting the interfaces and lithologic bodies. This integrated method also provided enhanced resolution of depositional contacts and variable lithologic bodies. Specifically, the approach was able to describe a highly variable, approximately 5 m thick Carboniferous formation at depths greater than 3400 m. Borehole and core data provided a complementary hydrocarbon accumulation model. We used background information on hydrocarbon-bearing units within the main structure (an anticline) to detect and access (drill) ultra-thin lithologic reservoirs located in deeper areas of the structural high. Two wells drilled according to predictions derived from this new approach have each reached production rates of more than 30 tons of oil per day. Thus, we have determined the effectiveness of combining chaos inversion methods with empirical constraints in exploration of deep thin hydrocarbon reservoirs.

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.

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.

Recently, lots of waterlogging disasters happened in cities of China, which endangers social safety and residents’ properties. The basic reason of urban waterlogging is that rainfall intensity exceeds drainage capacity of urban underground rainwater pipe system. In order to control and treat urban waterlogging, reconstruction and expansion of the drainage pipelines is a useful method in projects. Thus, it is very necessary and important to explore and analyze the redundancy of the urban underground space before the design stage for the projects. Our paper defines a parameter for space redundancy from smallest distance between pipelines and presents an algorithm for space redundancy of urban drainage pipelines based on geometric analysis. The value of space redundancy will show the possibility of the reconstruction and expansion of the pipe network. This algorithm could also be applied in smart city pipe network information system, from which the space redundancy value of any pipeline can be calculated out quickly and showed intuitively. Moreover, the effect of reconstruction and expansion can also be displayed by BIM modeling in information system, which can provide a reliable and scientific basis for the design of projects for treatment of urban waterlogging.