裸眼井中扩张式封隔器力学行为研究

封隔器在石油天然气开采中起着非常重要的作用,而扩张式封隔器在裸眼井中广泛应用。本文采用有限元软件建立了裸眼封隔器与地层的模型,对胶筒在坐封过程中与井壁接触应力的变化进行了研究,并研究了在不同摩擦系数下接触压力的变化,结果表明,建立粗糙井壁面能够更加符合实际情况,胶筒肩部为应力集中的区域,地层与胶筒的接触应力会随着摩擦系数的增加而减小。研究结果为裸眼扩张式封隔器的设计和改进提供了理论依据。     

Off‐grid DoA estimation method of wideband signals

In this paper, an off‐grid direction of arrival (DoA) estimation method is proposed for wideband signals. This method is based on the sparse representation (SR) of the array covariance matrix. Similar to the time domain DoA estimation methods, the correlation function of the sources was assumed to be the same and known. A new measurement vector is obtained using the lower‐left triangular elements of the covariance matrix. The DoAs are estimated by quantizing the entire range of continuous angle space into discrete grid points. However, the exact DoAs may be located between two grid points; therefore, this estimation has errors. The accuracy of DoA estimation is improved by the minimization of the difference between the new measurement vector and its estimated values. Simulation results revealed that the proposed method can enhance the DoA estimation accuracy of wideband signals.     

压裂水平井产能预测方法研究进展

水平井压裂技术在低渗透及非常规储层中得到了广泛应用,压裂后水平井的产能预测关系到油田开发方案的制定,因此,国内外学者对压后渗流模型的建立和求解方法做出了不懈的努力。本文详细回顾了国内外学者所建压裂水平井产能预测模型及求解方法,指出了不同模型和求解方法的优缺点,并展望了压裂水平井产能模型的发展方向。     

Decomposition of first-order hybrid Petri nets for hierarchical control of manufacturing systems

Although hybrid Petri net (HPN) is a popular formalism in modelling hybrid production systems, the HPN model of large scale systems gets substantially complicated for analysis and control due to large dimensionality of such systems. To overcome this problem, a typical approach is to decompose the net into subnets and then control the plant through hierarchical or decentralized structures. Although this concept has been widely discussed in the literature for discrete PNs, there is a lack of research for HPNs. In this paper, a new method of decomposition of first-order hybrid Petri nets (FOHPNs) is proposed first and then the hierarchical control of the subnets through a coordinator is introduced. The advantage of using the proposed approach is validated by an existing example. A sugar milling case study is analysed by using a decomposed FOHPN model and the optimization results are compared against the results of the approaches presented in other papers. Simulation results show not only an improvement in production rate, but also show the ability to control the plant online. In addition, by using the hierarchical control structure for an FOHPN model, it is possible to reduce the cost of communication links, improve the reliability of the system, maintain the plant locally, and partially redesign the system.     

基于分段加权的反向稀疏跟踪算法研究

为提高稀疏表示跟踪模型性能，提出一种分段加权的反向稀疏跟踪算法，将跟踪问题转化为在贝叶斯框架下寻找概率最高的候选对象问题，构造不同的分段权重函数来分别度量候选目标与正负模板的判别特征系数。通过池化来降低跟踪结果的不确定性干扰，选择正负模板加权系数差值最大的候选表示作为跟踪结果。实验表明，在光照变化、遮挡、快速运动、运动模糊情况下，所提出的算法可以确保跟踪结果的准确性和鲁棒性。     

LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net

The falling down problem has become one of the very important issues of global public health in an aging society. The specific equipment was adopted as the detection device of falling-down in the early studies, but it is inconvenient for the elderly and difficult for future application. The smart phone more commonly used than the specific fall detection equipment is selected as a mobile device for human fall detection, and a fall detection algorithm is developed for this purpose. What the user has to do is to put the smart phone in his/her thigh pocket for falling down detection. The signals detected by the tri-axial G-sensor are converted into signal vector magnitudes as the basis of detecting a human body in a stalling condition. The Z-axis data sets are captured for identification of human body inclination and the occurrence frequencies at the peak of the area of use are used as the input parameters. A high-level fuzzy Petri net is used for the analysis and the development of identifying human actions, including normal action, exercising, and falling down. The results of this study can be used in the relevant equipments or in the field of home nursing.     

抽油井不压井作业装置及抽油泵底阀设计

江苏油田的部分油井存在油管短路、地层高压低渗、漏失严重等问题,这些问题造成压井作业困难,并且压井作业过程中的井控与环境污染风险较大。为了进行不压井检泵作业,同时缩短检泵之后的产量恢复周期,进行了不压井作业技术研究,并研制了不压井作业装置。为解决起下抽油杆过程中的防喷问题,改进了抽油泵底阀结构,该结构可实现起下抽油杆过程中油管不带压、无溢流,并保障起下油管过程中的井控安全。该技术在江苏油田现场应用9井次,效果良好。     

随钻扩眼工具及技术研究

基于随钻扩眼技术优势的分析，研究了机械式、液压式和偏心式随钻扩眼工具执行机构和扩眼总成的机构原理，并介绍了国内所开展的相关研究。文章提出应加强现有工具的适应性和匹配性研究，发掘各类工具的优势。优化执行机构、扩眼总成和水力机构、重视底部钻具组合力学特性的分析是今后发展和完善随钻扩眼工具及技术的几个重要内容。实践表明，随钻扩眼技术在处理井下复杂情况、降低钻井综合成本、提高钻井速度、提高建井质量和安全性等方面具有显著的优势，随钻扩眼工具也正逐渐成为一种重要的石油钻井配套工具。     

测试(试井)成果影响因素浅析

随着测试技术的发展、测试质量及资料解释水平的提高，测试（试井）成果的可靠程度越来越受到重视。影响解释成果的环节和因素很多，从客观和主观两方面分析其主要因素，找出症结所在，从而完善现场环节，完善资料解释过程，提高测试技术及解释水平。