Diagnostic and analysis of long-term transient pressure data from Permanent Down-hole Gauges (PDG)

Permanent Down-hole Gauge (PDG) is the down-hole measuring device installed during the well completion. It can provide the continuous down-hole transient pressure in real-time. Since 1980s, PDG has been widely applied in oilfields. The wide field applications have demonstrated that the long-term pressure monitoring with PDG is useful for production optimization, reservoir description and model calibration. Analysing the long-term, noisy and large volume of PDG pressure data and extracting useful reservoir information are very challenging. Although lots of achievement has been made in PDG data processing, such as denoising, outlier removal and transient identification, analysis of long-term transient pressure from PDG is still difficult due to several challenging problems. The first problem is the dynamic changes in reservoir-well properties, which can cause the linearity assumption for pressure-transient analysis invalid, also the reservoir model needs calibration to match the field performance. The second problem is unknown or incomplete flow rate history. These problems together make it a very challenging task for engineer to interpret long-term transient pressure from PDG. This study investigates novel methods to analyse the long-term transient pressure from PDG with Wavelet Transform (WT). Firstly, a new diagnostic function named as Unit Reservoir System Response Aurc has been developed, and it can effectively diagnose the nonlinearities from PDG pressure due to the changes in reservoir-well properties. The nonlinearity diagnostic and evaluation is an important procedure before pressure analysis. Secondly, a model-independent method of reconstructing unknown rate history has been developed. This method has wide applications, considering the effects of skin, wellbore storage, reservoir heterogeneity and multiphase flow. Thirdly, based on the nonlinearity diagnostic result, sliding window technique is proposed to analyse long-term pressure with nonlinearities and update reservoir model with time-dependent reservoir properties. The synthetic cases and field data application have demonstrated that the developed methods can reveal more useful reservoir information from PDG pressure and realize the potential of PDG as the tool of reservoir management

Projective synchronization analysis for BAM neural networks with time-varying delay via novel control

In this paper, the projective synchronization of BAM neural networks with time-varying delays is studied. Firstly, a type of novel adaptive controller is introduced for the considered neural networks, which can achieve projective synchronization. Then, based on the adaptive controller, some novel and useful conditions are obtained to ensure the projective synchronization of considered neural networks. To our knowledge, different from other forms of synchronization, projective synchronization is more suitable to clearly represent the nonlinear systems’ fragile nature. Besides, we solve the projective synchronization problem between two different chaotic BAM neural networks, while most of the existing works only concerned with the projective synchronization chaotic systems with the same topologies. Compared with the controllers in previous papers, the designed controllers in this paper do not require any activation functions during the application process. Finally, an example is provided to show the effectiveness of the theoretical results

Review of Aircraft Vibration Environment Prediction Methods

AbstractAircraft vibration response environment prediction, which is adopted in aircraft initial vehicle development, has not been got enough attention and wide application yet. This paper briefly reviews theoretical and engineering significance of aircraft vibration response environment prediction firstly. Then the paper summarizes the main aircraft vibration response environment prediction methods and indicates their advantages, disadvantages and applicability scopes, including extrapolation of similar structure, theory analysis and analytical solution of differential dynamical equation, statistical parameter modeling, simulation calculation modeling and machine learning. Finally, the paper points out that uncertainty and non-linear structures, nonstationary signal analysis and complex vibration environment response prediction are major problems for aircraft vibration response prediction and directions for future research work

Novel image markers for non-small cell lung cancer classification and survival prediction

BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients. RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated. CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers

Novel Image Markers for Non-Small Cell Lung Cancer Classification and Survival Prediction

BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients. RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated. CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers