Volcanic rock reservoirs have received extensive attention from scholars all over the world because of their geothermal, mineral, and oil and gas resources. Shear wave velocity is the essential information for AVO (amplitude variation with offset) analysis and the reservoir description of volcanic rocks. However, due to factors such as cost, technical reasons, and so on, shear wave velocity is not provided in many logging data. This paper proposes a shear wave velocity prediction method suitable for the conventional logging of volcanic rocks. Firstly, the Xu–White model is improved. The probability distributions formed by the prior information of the logging area are used to initialize the key petrophysical parameters in the model instead of the fixed parameter value to establish the statistical petrophysical model between the logging curve and shear wave velocity. Then, based on the Bayesian inversion method, the simulated P-wave velocity is matched with the actual P-wave logging data to calculate the key petrophysical parameters, and is then used for S-wave velocity prediction. The method is applied to the actual logging data of the No. 5 structure in Nanpu Sag, eastern China. The prediction effect of shear wave velocity is better than that of the conventional method, indicating the feasibility and effectiveness of this method. This study will provide more accurate shear wave velocity data for the exploration and development of volcanic reservoirs.

A stochastic inversion method of reservoir properties for anisotropic shales is proposed by combing rock physics model and Bayesian estimation. Quantitative relations between elastic parameter such as P- and S-wave impedances and reservoir properties including fracture and porosity are investigated using the statistical rock physics model. During the modeling, the error between the rock physics model and reservoirs, as well as noises in the seismic data are considered. For the process of estimating reservoir properties from elastic parameters, Bayesian inversion based on statistical rock physics model is applicable to the uncertainty problem by computing the posterior probability distribution (PDF) of the reservoir properties. Based on rock physics modeling and given prior knowledge of the reservoir, reservoir properties are obtained by the maximum a posteriori (MAP) criterion and associated uncertainty analysis. In the stochastic inversion, the SA-PSO algorithm which combines the simulated annealing method and the particle swarm optimization method shows its advantages in accuracy and efficiency. The estimated reservoir properties can be used for better characterizations of the sweet spots in shale reservoirs. Presentation Date: Tuesday, September 26, 2017 Start Time: 3:05 PM Location: 330A Presentation Type: ORAL

Accurate shear wave velocity is important for shale reservoir exploration and characterization. However, the effect of the ubiquitous preferred orientation of clay minerals on the velocities of shale has rarely been considered in existing S-wave velocity estimation methods, resulting in limited accuracy of the estimation method. In this study, a S-wave velocity estimation method is proposed for shale while considering the effect of the preferred orientation of clay. First, a compaction model is built by taking the effects of the orientation distribution of clay and the aspect ratio of pores into account. Then, the compaction model is utilized in a workflow to obtain the model parameters by fitting the estimated P-wave velocity with the bedding-normal P-wave velocity from well logging. Finally, the S-wave velocity is estimated using the compaction model and calculated model parameters. The proposed method is verified by laboratory data and successfully applied to a shale gas reservoir. The result shows that the root mean square error almost halves compared with the Xu–White model. Additionally, the correlation coefficient also improves. The improvement in S-wave velocity estimation indicates that the effect of the preferred orientation of clay on the velocities of shale is effectively corrected. The proposed method improves the accuracy of velocity modeling and reservoir characterization for shale.

Compact high-frequency surface wave radars (HFSWRs) utilize miniaturized antennas, resulting in lower antenna gain and a reduced signal-to-noise ratio (SNR) for target echoes. Due to noise interference, ship echoes in the noise region often fall below the detection threshold, leading to missed detections. To address this issue, this paper proposes a cascaded detection method in the time–frequency (TF) domain to improve ship detection performance under such conditions. First, TF features are extracted from TF representations of ship and noise signals. Supervised machine learning algorithms are then employed to distinguish targets from noise, reducing false alarms. Next, a non-constant false alarm rate (CFAR) threshold is computed based on the noise mean, standard deviation, and an adjustment factor to improve detection robustness. Experiments show that the classification accuracy between the ship and noise signals exceeds 99%, and the proposed method significantly outperforms the conventional CFAR and TF-domain CFAR in terms of detection performance.