基于神经网络算法的井下裂缝诊断与堵漏技术

在钻井过程中，常常钻遇不同宽度的井下地层裂缝。钻遇裂缝时容易发生钻井液漏失现象，甚至发生钻井液失返现象，严重影响了安全、高效钻井。目前裂缝封堵的方法常存在封堵成功率不高、堵漏承压能力低的问题，其中一个重要的原因是对井下地层的裂缝宽度等特征认识不清。基于地层裂缝产生的岩石力学机理，确定影响裂缝宽度关键的6个力学和工程因素，并利用神经网络计算的非线性、大数据特点建立了井下地层裂缝宽度的分析模型，模型包含输入层、输出层和3个隐藏层。通过该模型诊断井下裂缝宽度，提高了计算精度，平均误差仅为2.09%，最大误差为5.88%，解决钻井现场仅凭经验判断裂缝误差较大和依靠成像测井成本较高的问题。同时根据神经网络模型诊断得到的裂缝宽度优化堵漏材料的粒径配比，提高了裂缝内的架桥封堵强度和架桥的稳定性，封堵层的承压能力达到12.8 MPa，反向承压能力达到4.5 MPa。现场堵漏试验最高憋压10 MPa，经过封堵作业后大排量循环不漏，达到了裂缝性地层高效堵漏的目的，堵漏一次成功。      

淮河入江水道行洪能力分析

入江水道是淮河下游最大的泄洪河道,承担着淮河上中游70%以上的洪水泄入长江。根据1961—2018年大洪水期间的实测资料,利用水位流量法计算分析入江水道的泄洪能力和防洪能力。结果表明:因历史客观条件限制以及4个梯级控制河段整治的难度与复杂性,在不同时期各控制河段的行洪能力呈现各自不同的特点;经过多年持续有效治理,河道行洪能力整体得到提高;由近年来实测资料推算,各控制河段的行洪能力基本达到设计要求。对入江水道行洪能力的分析为淮河下游区的防汛抗洪和降低特大洪水威胁提供借鉴和参考,对区域经济社会又好又快发展具有现实意义。     

电驱液压式大截面导线压接设备自动化研究

现有的压接设备可控性差、操作复杂,导致压接质量因人而异。针对此问题,提出了基于DDVC技术的导线压接设备自动化控制方案,建立了速度控制系统数学模型,应用Simulink进行仿真,分析系统性能。建立了基于DEFORM的耐张线夹压接实验模型,获得了耐张线夹压接的最佳加载速度曲线,并以此作为压接设备速度控制系统的Simulink仿真模型输入,对压接过程进行了仿真分析。结果表明:开环控制时系统存在一定的超调,加入PID控制后可有效抑制超调;所设计的压接设备可很好地跟随最佳压接曲线,跟随误差仅为0. 904 mm·s-1。研究成果对改进压接设备、提高压接质量提供了新的解决方案。     

TLCD基础隔震框架结构的减振控制研究

为了避免和减轻由过大隔震层位移引起的损害,对基础隔震框架结构装设调频液柱阻尼器(tuned liquid column damper,简称TLCD)后混合系统的减振效果进行研究。建立了单层和多层混合控制系统在地震作用下的运动方程,采用TLCD-结构体系转化为调频质量阻尼器(tuned mass damper,简称TMD)-结构体系的等效方法,利用TMD参数优化公式,得到单个TLCD初始设计参数,并采用状态空间方程得到多个TLCD最优设计参数。通过对某8层基础隔震结构进行模拟,证明了该理论设计方法的合理性。该混合结构不仅可以减小隔震层位移和加速度,而且对上部结构位移和加速度反应都能更有效的控制。     

瓦斯抽采集成旁通阀二位放水器研制

针对井下瓦斯抽采中原放水器配气阀安装不规范、双截止阀使用繁琐、工人劳动强度大等现状,研制了具有集成调控装置的放水器,能够实现一个阀门控制所有气路和水路,自主放水和负压排水,从源头上杜绝了瓦斯喷孔和水流溢出等情况,大大降低了职工的劳动强度,实现了井下抽采系统安全可靠运行。     

高瓦斯矿井U型通风工作面瓦斯治理技术

针对李雅庄煤矿U型通风工作面上隅角及回风流瓦斯浓度高、瓦斯治理难度大的问题，根据工作面瓦斯来源及在采空区三带的运移储存规律，李雅庄煤矿开展了本煤层抽采工艺优化和裂隙带抽采技术研究。对本煤层钻孔封孔深度、联孔工艺、管路连接方式等进行优化，钻孔抽采浓度由抽采4个月后降低到9%提高到抽采10个月后维持在19%；通过调整裂隙带钻孔布置方式、优化钻孔布孔层位、采取下筛管护孔等技术措施，裂隙带钻场最高瓦斯抽采纯流量达13.6 m³/min，平均瓦斯抽采纯流量达8 m³/min，2个钻场联合抽采瓦斯纯流量在13 m³/min以上；取消了瓦斯措施巷、井下移动泵和上隅角风帘，上隅角和回风流平均瓦斯浓度分别控制在0.5%和0.4%以下，对高瓦斯矿井U型通风工作面瓦斯治理有借鉴意义。     

An Improved Torque Sensorless Speed Control Method for Electric Assisted Bicycle With Consideration of Coordinate Conversion

In this paper, we propose an improved torque sensorless speed control method for electric assisted bicycle, this method considers the coordinate conversion. A low-pass filter is designed in disturbance observer to estimate and compensate the variable disturbance during cycling. A DC motor provides assisted power driving, the assistance method is based on the real-time wheel angular velocity and coordinate system transformation. The effect of observer is proved, and the proposed method guarantees stability under disturbances. It is also compared to the existing methods and their performances are illustrated through simulations. The proposed method improves the performance both in rapidity and stability.      

Scalable Clock Synchronization Analysis: A Symmetric Noncooperative Output Feedback Tubes-MPC Approach

In the cyber-physical environment, the clock synchronization algorithm is required to have better expansion for network scale. In this paper, a new measurement model of observability under the equivalent transformation of minimum mean square error (MMSE) is constructed based on basic measurement unit (BMU), which can realize the scaled expansion of MMSE measurement. Based on the state updating equation of absolute clock and the decoupled measurement model of MMSE-like equivalence, which is proposed to calculate the positive definite invariant set by using the theoretical-practical Luenberger observer as the synthetical observer, the local noncooperative optimal control problem is built, and the clock synchronization system driven by the ideal state of local clock can reach the exponential convergence for synchronization performance. Different from the problem of general linear system regulators, the state estimation error and state control error are analyzed in the established affine system based on the set-theory-in-control to achieve the quantification of state deviation caused by noise interference. Based on the BMU for isomorphic state map, the synchronization performance of clock states between multiple sets of representative nodes is evaluated, and the scale of evaluated system can be still expanded. After the synchronization is completed, the state of perturbation system remains in the maximum range of measurement accuracy, and the state of nominal system can be stabilized at the ideal state for local clock and realizes the exponential convergence of the clock synchronization system.      

镀液温度实时控制系统的设计与实现

针对镀液温度变化的非线性和滞后性,设计了采用专家规则的镀液温度实时控制系统。该系统在实时监测的基础上,构建了综合评判镀液温度偏差和镀液温度偏差变化率的专家规则,实现了镀液温度的自适应快速调节。仿真分析结果表明,该系统克服了非线性和滞后性的不良影响,能够实现镀液温度的实时控制。     

Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes

A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (Q_L) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble oil emulsion (BS&W). The test data points evaluated include a wide range of oil flow rate conditions and values for the four input variables recorded. The TSFIS algorithm applied involves five data processing steps: a) pre-processing, b) fuzzification, c) rules base and adaptive neuro-fuzzy inference engine, d) defuzzification, and e) post-processing of the fuzzy model. The developed TSFIS model for the Resalat oil field database predicted oil flow rate to a high degree of accuracy (root mean square error = 247 STB/D, correlation coefficient = 0.9987), which improves substantially on the commonly used empirical algorithms used for such predictions. TSFIS can potentially be applied in wellhead choke fuzzy controllers to stabilize flow in specific wells based on real-time input data records.