随钻测井数据声波无线传输方法研究

项目名称： 随钻测井数据声波无线传输方法研究

项目编号： No.61201131

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 电子学与信息系统

项目作者： 张伟

作者单位： 电子科技大学

项目金额： 25万元

中文摘要： 随钻测井数据传输是实现智能化钻井的关键技术之一，声波无线传输技术是利用沿钻柱系统传播的弹性波为载波，将测量数据传输到地面的一种随钻数据传输技术，其传输速率比泥浆脉冲传输、电磁波传输等无线传输技术要高1～2个数量级，是一种新兴的、有发展前途的随钻数据遥传技术。然而钻井过程中由钻井设备、泥浆的循环等产生的声波噪声以及信号在介质中传播的衰减吸收，特别是钻杆接头的高反射性，使得钻柱的脉冲响应持续达数百毫秒，这将造成严重的码间干扰，并最终导致信道的传输能力大幅下降。针对这一难题，本课题在深入分析钻柱系统的声传播特性的基础上，提出基于SC-FDE技术的声波无线传输通信系统模型，并研究适合于声波传输信道特性的信道编解码技术以及随钻数据的调制解调技术，降低误码率、提高传输的可靠性。同时提出利用改进的粒子群-小波神经网络在井口对随钻环境强噪声背景下的声波信号进行检测和提取，最大限度地消除噪声，提高传输能力。

中文关键词： 周期性钻柱声波信道；声波无线传输通信系统；矢量OFDM；粒子群-小波神经网络；信号检测

英文摘要： LWD data transmission is one of the key technologies for achieving intelligent drilling. Acoustic wave wireless transmission technology is a kind of LWD data transmission technology which takes the elastic waves propagating along the drill string as carrier to transmit measure data to the ground. Its transmission rate is much more higher than other transmission technologies such as Mud-Pulse-Transmission and Electromagnetic-Transmission. And it is a emerging, promising technology for LWD data distance transmission. However, acoustic noise, being generated during drilling by the drilling equipment and mud circulation, and signal attenuation in the transmission medium and especially the high reflectivity of the pipe joints caused the impulse response of the drill string lasts for hundreds of milliseconds. All those can cause serious Inter-Symbol Interference and eventually lead to the transmission capacity of the channel dropped significantly. To address this problem, after deeply analyzing the sound propagation characteristics of the drill string, project team proposes a transmission technology based on SC-FDE technology. In order to improve the reliability and reduce the error rate of data transmission, we studied the channel coding/decoding technology and signal modulation/demodulation techniques that suitable

英文关键词： periodic drill string periodic drill string acoust；acoustic wireless transmission communication syste；vector OFDM；particle swarm-wavelet neural network；signal detection