Performance of Air Foam Flooding under Low Frequency Vibration

Foam injection is widely applied in amounts of fields to drilling, production, and formation protection. Sometimes, the application result is disappointing, which is caused by the failure of bubble generation in foam flooding. Therefore, it is necessary to seek ways for improving the performance of foam injection. An increased disturbance to the stratum, like the vibration caused by a seismic oil recovery technique, would be helpful. In the current work, the seepage of air foam in porous media under low frequency (LF) vibration is analyzed with experiments and an investigation of bubble creation/destruction rate change is carried out using mathematical modeling. The resistance factor of foam flooding under indoor vibration increases by 1.5 times and the valid time is obviously extended compared with when no vibration is used. The optimal vibrating acceleration and frequency of 0.7 m/s2 and the natural frequency of the cores-nearby of 18 Hz are achieved in the indoor experiments. Under vibration, the bubble generation rate increases, while bubble break rate by internal expansion or by gas diffusion and transfer decreases. An interesting phenomenon is also observed, which might develop a power level formula between the initially defined dimensionless MRF (maximum foam flooding resistance factor) and dimensionless DMRF (duration of maximum foam flooding resistance factor). The power product and sum of the power exponents of the above formula both equal approximately to 1. With the assistance of LF vibration, the increase of security, adaptability, and efficiency in foam injection may improve the reservoir recovery and extend its application.</span

Risk Factors for Prognosis after the Maze IV Procedure in Patients with Atrial Fibrillation Undergoing Valve Surgery

The present study evaluated risk factors related to persistent atrial fibrillation (AF) at discharge (AF-d) and recurrent atrial fibrillation (rAF) and all-cause death after the maze IV procedure. Two hundred nineteen patients (63 female, aged 52.5±8.8 years) with valve disease and persistent AF undergoing valve surgery and the maze IV procedure in our center between 2015 and 2016 were included. Baseline demographic and clinical data were obtained by review of medical records. The median follow-up period was 27 months (interquartile range 21–34 months) in our patient cohort. The primary end point was all-cause death. The secondary end point was AF-d or rAF. rAF is defined as AF recurrence at 3 months or later after the procedure. Twenty-eight patients (12.8%) died during follow-up. Multiple logistic regression analysis showed that thrombocytopenia, elevated serum total bilirubin level, a larger right atrium, AF-d, and rAF were independent determinants for all-cause death after the maze IV procedure after adjustment for age, sex, and clinical covariates, including New York Heart Association class III/IV disease, hypertension, and aortic regurgitation, while valvular disease duration and left atrial diameter greater than 80.5 mm were independent determinants for AF-d, and thrombocytopenia, elevated serum total bilirubin level, higher mean pulmonary artery pressure, and AF-d were independent predictors for rAF. In conclusion, thrombocytopenia, elevated serum total bilirubin level, an enlarged right atrium, AF-d, and rAF are independent predictors of all-cause death in patients undergoing the maze IV procedure. </p

Journal of Petroleum Science and Technology *Corresponding author Performance of Air Foam Flooding under Low Frequency Vibration

ABSTRACT Foam injection is widely applied in amounts of fields to drilling, production, and formation protection. Sometimes, the application result is disappointing, which is caused by the failure of bubble generation in foam flooding. Therefore, it is necessary to seek ways for improving the performance of foam injection. An increased disturbance to the stratum, like the vibration caused by a seismic oil recovery technique, would be helpful. In the current work, the seepage of air foam in porous media under low frequency (LF) vibration is analyzed with experiments and an investigation of bubble creation/destruction rate change is carried out using mathematical modeling. The resistance factor of foam flooding under indoor vibration increases by 1.5 times and the valid time is obviously extended compared with when no vibration is used. The optimal vibrating acceleration and frequency of 0.7 m/s 2 and the natural frequency of the cores-nearby of 18 Hz are achieved in the indoor experiments. Under vibration, the bubble generation rate increases, while bubble break rate by internal expansion or by gas diffusion and transfer decreases. An interesting phenomenon is also observed, which might develop a power level formula between the initially defined dimensionless MRF (maximum foam flooding resistance factor) and dimensionless DMRF (duration of maximum foam flooding resistance factor). The power product and sum of the power exponents of the above formula both equal approximately to 1. With the assistance of LF vibration, the increase of security, adaptability, and efficiency in foam injection may improve the reservoir recovery and extend its application

Nonlocal Feature Selection Encoder&ndash;Decoder Network for Accurate InSAR Phase Filtering

Accurate interferometric phase filtering is an essential step in InSAR data processing. The existing deep learning-based phase-filtering methods were developed based on local neighboring pixels and only use local phase information. The idea of nonlocal processing has been proven to be very effective for improving the accuracy of interferometric phase filtering. In this paper, we propose a deep convolutional neural network-based nonlocal InSAR filtering method via a nonlocal phase filtering network (NL-PFNet) based on the encoder&ndash;decoder structure and nonlocal feature selection strategy. Thanks to the powerful phase feature extraction ability of the encoder&ndash;decoder structure and the utilization of nonlocal phase information, NL-PFNet can predict an accurately filtered interferometric phase after training using a large number of interferometric phase images with different noise levels. Experiments on both simulated and real InSAR data show that the proposed method significantly outperforms three traditional well-established methods and another deep learning-based method. Compared with the InSAR-BM3D filter and another deep learning-based method, the mean square error of the proposed method is 25% and 11% lower when processing simulated data, respectively, and when processing the real Sentinel-1 interferometric phase, the no-reference evaluation metric Q of the proposed method is 25% and 9% higher, respectively. In addition, the running time of the proposed method is tens of times less than that of the traditional filtering methods

A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network

Phase unwrapping is a critical step in synthetic aperture radar interferometry (InSAR) data processing chains. In almost all phase unwrapping methods, estimating the phase gradient according to the phase continuity assumption (PGE-PCA) is an essential step. The phase continuity assumption is not always satisfied due to the presence of noise and abrupt terrain changes; therefore, it is difficult to get the correct phase gradient. In this paper, we propose a robust least squares phase unwrapping method that works via a phase gradient estimation network based on the encoder–decoder architecture (PGENet) for InSAR. In this method, from a large number of wrapped phase images with topography features and different levels of noise, the deep convolutional neural network can learn global phase features and the phase gradient between adjacent pixels, so a more accurate and robust phase gradient can be predicted than that obtained by PGE-PCA. To get the phase unwrapping result, we use the traditional least squares solver to minimize the difference between the gradient obtained by PGENet and the gradient of the unwrapped phase. Experiments on simulated and real InSAR data demonstrated that the proposed method outperforms the other five well-established phase unwrapping methods and is robust to noise

A Novel Sub-Image Local Area Minimum Entropy Reconstruction Method for HRWS SAR Adaptive Unambiguous Imaging

Multichannel high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is a vital technique for modern remote sensing. As multichannel SAR systems usually face the problem of azimuth nonuniform sampling resulting in azimuth ambiguity, the conventional reconstruction methods are adopted to obtain the uniformly sampled signal. However, various errors, especially amplitude, phase, and baseline errors, always significantly degrade the performance of the reconstruction methods. To solve this problem, in this paper, a novel sub-image local area minimum entropy reconstruction method (SILAMER) is proposed, which has favorable adaptability to the HRWS SAR system with various errors. First, according to the idea of image domain reconstruction, the sub-images are generated by employing the back-projection algorithm. Then, we proposed an estimation algorithm based on sub-image local area minimum entropy to obtain the optimal reconstruction coefficient and the compensation phase, which can greatly improve the estimation efficiency by using a local area of the sub-image as the input for estimation. Finally, the sub-images are weighted by the optimal estimated reconstruction coefficient and calibrated by the compensation phase to obtain the unambiguous reconstruction image. The experimental results verify the effectiveness of the proposed method. Noticeably, the proposed algorithm has two additional advantages, i.e., (1) it can perform well under the condition of low signal-to-noise ratio (SNR), and (2) it is suitable for the curved trajectory SAR reconstruction. The simulations verify these advantages of the proposed method

Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Because of the three-dimensional (3D) imaging scene’s sparsity, compressed sensing (CS) algorithms can be used for linear array synthetic aperture radar (LASAR) 3D sparse imaging. CS algorithms usually achieve high-quality sparse imaging at the expense of computational efficiency. To solve this problem, a fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM) is proposed in this paper. The proposed method calculates the maximum marginal likelihood function under the framework of the RVM to obtain the optimal hyper-parameters; the scattering units corresponding to the non-zero optimal hyper-parameters are extracted as the target-areas in the imaging scene. Then, based on the target-areas, we simplify the measurement matrix and conduct sparse imaging. In addition, under low signal to noise ratio (SNR), low sampling rate, or high sparsity, the target-areas cannot always be extracted accurately, which probably contain several elements whose scattering coefficients are too small and closer to 0 compared to other elements. Those elements probably make the diagonal matrix singular and irreversible; the scattering coefficients cannot be estimated correctly. To solve this problem, the inverse matrix of the singular matrix is replaced with the generalized inverse matrix obtained by the truncated singular value decomposition (TSVD) algorithm to estimate the scattering coefficients correctly. Based on the rank of the singular matrix, those elements with small scattering coefficients are extracted and eliminated to obtain more accurate target-areas. Both simulation and experimental results show that the proposed method can improve the computational efficiency and imaging quality of LASAR 3D imaging compared with the state-of-the-art CS-based methods