Pattern Recognition for Steam Flooding Field Applications based on Hierarchical Clustering and Principal Component Analysis

Steam flooding is a complex process that has been considered as an effective enhanced oil recovery technique in both heavy oil and light oil reservoirs. Many studies have been conducted on different sets of steam flooding projects using the conventional data analysis methods, while the implementation of machine learning algorithms to find the hidden patterns is rarely found. In this study, a hierarchical clustering algorithm (HCA) coupled with principal component analysis is used to analyze the steam flooding projects worldwide. The goal of this research is to group similar steam flooding projects into the same cluster so that valuable operational design experiences and production performance from the analogue cases can be referenced for decision-making. Besides, hidden patterns embedded in steam flooding applications can be revealed based on data characteristics of each cluster for different reservoir/fluid conditions. In this research, principal component analysis is applied to project original data to a new feature space, which finds two principal components to represent the eight reservoir/fluid parameters (8D) but still retain about 90% of the variance. HCA is implemented with the optimized design of five clusters, Euclidean distance, and Ward\u27s linkage method. The results of the hierarchical clustering depict that each cluster detects a unique range of each property, and the analogue cases present that fields under similar reservoir/fluid conditions could share similar operational design and production performance

TripleNet: A Low Computing Power Platform of Low-Parameter Network

With the excellent performance of deep learning technology in the field of computer vision, convolutional neural network (CNN) architecture has become the main backbone of computer vision task technology. With the widespread use of mobile devices, neural network models based on platforms with low computing power are gradually being paid attention. This paper proposes a lightweight convolutional neural network model, TripleNet, an improved convolutional neural network based on HarDNet and ThreshNet, inheriting the advantages of small memory usage and low power consumption of the mentioned two models. TripleNet uses three different convolutional layers combined into a new model architecture, which has less number of parameters than that of HarDNet and ThreshNet. CIFAR-10 and SVHN datasets were used for image classification by employing HarDNet, ThreshNet, and our proposed TripleNet for verification. Experimental results show that, compared with HarDNet, TripleNet's parameters are reduced by 66% and its accuracy rate is increased by 18%; compared with ThreshNet, TripleNet's parameters are reduced by 37% and its accuracy rate is increased by 5%.Comment: 4 pages, 2 figure

Efficiency of different annuloplasty in treating functional tricuspid regurgitation and risk factors for recurrence

AbstractBackgroundFunctional tricuspid regurgitation (FTR) is frequent in patients with mitral valve disease. Untreated tricuspid regurgitation (TR) may cause poor clinical outcomes. The surgical factors involved in annuloplasty for FTR remain controversial. Our objective was to compare effectiveness of different tricuspid annuloplasty (TVP), and reveal the risk factors of recurrence.MethodsWe analyzed the clinical details of 399 consecutive patients who underwent mitral surgery with concomitant TVP, from 2006 to 2011, in two Chinese single-centers. Three methods were used for TVP: De Vega surgery was completed in 242 patients; annuloplasty using a flexible band was completed in 98 patients; and surgery with a rigid ring was performed in 59 patients.ResultsThe operative mortality rate was 2.3%. After surgery, the TR grade of all patients decreased significantly. At three years postoperatively, 13.7% of patients were diagnosed with recurrent FTR. At the three year time point, severe TR in the De Vega group was 18%, which was higher than those in the flexible (8.4%) and rigid planner ring groups (5.2%). During follow-up, the recurrent rates in the rigid group were significantly lower than in the flexible group. Multivariate analysis revealed that pre-operative atrial fibrillation, severe TR, large left atrial, ejection fraction (EF)<40%, De Vega annuloplasty, and postoperative permanent pacemaker installation were independent risk factors for severe recurrent TR.ConclusionsRigid ring annuloplasty efficaciously improved post-operative tricuspid valve function in patients with FTR. Atrial fibrillation, a large left atrium, low EF and postoperative permanent pacemaker installation were independent risk factors for severe recurrent TR

Magnetic-field-induced splitting of Rydberg Electromagnetically Induced Transparency (EIT) and Autler-Townes (AT) spectra in 87^{87}Rb vapor cell

We theoretically and experimentally investigate the Rydberg electromagnetically induced transparency (EIT) and Autler-Townes (AT) splitting of $^{87}$Rb vapor under the combined influence of a magnetic field and a microwave field. In the presence of static magnetic field, the effect of the microwave field leads to the dressing and splitting of each $m_F$ state, resulting in multiple spectral peaks in the EIT-AT spectrum. A simplified analytical formula was developed to explain the EIT-AT spectrum in a static magnetic field, and the calculations are in excellent agreement with experimental results.We further studied the enhancement of the Rydberg atom microwave electric field sensor performance by making use of the splitting interval between the two maximum absolute $m_F$ states under static magnetic field. The traceable measurement limit of weak electric field by EIT-AT splitting method was extended by an order of magnitude, which is promising for precise microwave electric field measurement.Comment: 12 pages, 4 figure