油田定向井随钻信号检测及处理
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摘要
油田定向井钻探过程中需要及时准确地测量出钻具所处井眼方向参数,也要了解已完钻的井眼轨迹,因此随钻信号的检测与处理成为衡量定向井水平的一个重要因素。
传统的钻孔测斜仪采用偏重块和罗盘磁针,现代则采用灵敏度和精度很高的加速度计以及防振性能好的磁通门。把新型传感器和计算机技术相结合,出现了多种单、多点和连续测斜仪器。而把井下记录与实时传输相结合的MWD系统,既能实现实时检测,又保证了随钻测量资料的高质量,所以以MWD系统为主的无线随钻测斜技术代表着定向井未来的发展方向。
随着石油勘探开发的不断深入,地层结构越来越复杂,深井、超深井和高温、高压井等特殊复杂井变得越来越多。因为油田定向井随钻测量参数主要为磁通门与加速度传感信号,为了提高随钻信号检测精度,建立了磁通门传感器数学模型与加速度传感器数学模型,并对随钻测量误差进行了分析。
随钻测量传感器受诸如温度、湿度、电源波动等环境因素的影响,致使其测量准确度大大下降,造成测量精度不高、稳定性差等问题,因此需要对其进行补偿,故开展了磁通门传感器、加速度传感器输出信号的硬件补偿、线性补偿以及非线性补偿,形成了一套综合补偿方案。
随钻传感信号硬件补偿和线性补偿具有一定效果,但由于大部分的传感器本身就是非线性的,而且在测量过程中又受到各种环境因素的干扰,所以采用小波神经网络对距离传感器信号进行了非线性补偿,建立了利用BP算法训练小波对传感器信号进行非线性补偿的算法,取得了比线性补偿更好的效果；之后,根据动态神经网络思想,构造了用于随钻测量信号补偿的Elman神经网络,进一步提高了随钻信号补偿精度,并达到了较高的运算速度。再后,又提出了基于遗传Elman神经网络的补偿方案,经过GA-Elman神经网络补偿后的井斜方位与实测井斜方位相比有了很大的改善,基本接近理论值；最后,基于蚁群算法,根据自适应思想,在Elman神经网络的基础上,提出了一种自适应蚁群Elman神经网络对随钻测量信号的补偿方案,使得补偿误差进一步减小
基于协同设计思想开展了油田定向井随钻测量系统的设计,采用嵌入式设计提高了实时性；采用在线系统的编程技术,缩短了开发时间,节省了开发成本；采用模块化设计,便于扩展与通信。经测试,所设计的随钻测量系统精度高于油田在用的随钻测斜系统,提高了定向井钻井水平,且系统的稳定性、实时性、准确性和安全性均相应提高。
目前,绝大多数的无线随钻测量系统都是采用泥浆脉冲传输方式,因而信号的传输速度是关键因素。然而,钻井液中含有粘土、岩屑、重晶石粉等固相物质,并且其中存在着的游离状态的气体往往形成气泡,从而增加了信号传输速度问题的复杂性。为此,基于泥浆脉冲传输速度的计算模型,分析了钻井液脉冲的传输速度是随着钻井液密度的关系,以及与含气量的关系；分析了固体、液体之间密度和压缩性的差异对传输速度的影响,以及正、负脉冲的传输速度的变化。
The hole direction parameters of drilling tool must be measured promptly and accurately during the process of directional drilling in oilfield, and the hole trajectory while completion is also needed, so that Measurement While Drilling signal detection and process become an important factor of weighing directional drilling level.
Centrifugal weight and magnetic compass are adopted in the traditional inclinometers, while accelerometer with high sensibility and precision, and the fluxgate sensor with high anti-vibration are used in modern times. Single/multiple point and continuous inclinometers are developed by combining new type sensors with computer technology. MWD(Measurement While Drilling) system, combining downhole logging with real-time transmission, which can make sure real-time detection and high quality measurement material, mainly in Wireless measurement while drilling technology will become the development direction of directional well in the future.
With the development of petroleum exploration and production, the geology status has become more complicated, the number of deep well, ultra-deep well, high-temperature well, high-pressure well and so on, will become more and more. The main parameters of MWD of directional well in oilfield are accelerometer and fluxgate sensor signals, in order to improve the detection precision of measurement will drilling signal, the mathematic models of fluxgate and accelerometer are established, and the measurement while drilling error is analyzed.
The sensors of measurement while drilling are affected by the environmental factors, such as temperature, humidity, power supply fluctuation, which enormously induce the measurement veracity, so that the measurement precision is not high and the stability is not good, must be compensated. Hardware compensation, linear compensation and nonlinear compensation for fluxgate and accelerometer signals are formed to a synthesized compensation scheme.
Hardware compensation and linear compensation for sensor signals of measurement while drilling have some effect, but most of sensors are nonlinear, which are affected by the environmental factors in the process of measurement. Range sensor is compensated nonlinearly by wavelet neural network, nonlinear compensation algorithm for sensor signals is established, BP algorithm training wavelet, which has a better effect than linear compensation. Then, based on the idea of dynamic, the neural Elman neural network is constructed for measurement of drilling signal compensation, which further improves the precision and has a highly operation speed. And the compensation scheme based on genetic Elman neural network is also presented, after using GA-Elman neural network, the inclination and azimuth have improved a lot compared to the actual values, approaching theoretical values approximately. At last, based on ant colony algorithm, adaptive idea, and Elman neural network, a compensation scheme is presented of adaptive ant colony Elman neural network for measurement while drilling signals, which induced the compensation error to a greater extent.
Based on the cooperative design idea, Measurement While Drilling system of directional well in oilfield is designed, in which embedded design is applied to improve the real-time performance, the In System Programming technology is applied to shorten system developing time and declines the development cost, model design is applied to expand and communicate conveniently. This measurement while drilling system has a higher precision then the system in the oilfield at present, which improves the drilling level of directional well, and the stability, real-time performance, accuracy and security are also improved to some extent.
At present, the mud pulse signal transmission method was applied to the most of wireless measurement while drilling system, so the key factor of wireless MWD system is the transmission speed. The drilling fluids is mainly composed by clay, rock debris, barite powder, and bubble produced by air existing in free state. Therefore signal transmission speed become even more complex. The computing model of mud pluse signal transmission speed considers the relation with drilling fluid density and gas content. It also considers the influence of density and contraction difference between the solid and liquid, and the transmission speed variation of positive pulse or positive pulse.
传统的钻孔测斜仪采用偏重块和罗盘磁针,现代则采用灵敏度和精度很高的加速度计以及防振性能好的磁通门。把新型传感器和计算机技术相结合,出现了多种单、多点和连续测斜仪器。而把井下记录与实时传输相结合的MWD系统,既能实现实时检测,又保证了随钻测量资料的高质量,所以以MWD系统为主的无线随钻测斜技术代表着定向井未来的发展方向。
随着石油勘探开发的不断深入,地层结构越来越复杂,深井、超深井和高温、高压井等特殊复杂井变得越来越多。因为油田定向井随钻测量参数主要为磁通门与加速度传感信号,为了提高随钻信号检测精度,建立了磁通门传感器数学模型与加速度传感器数学模型,并对随钻测量误差进行了分析。
随钻测量传感器受诸如温度、湿度、电源波动等环境因素的影响,致使其测量准确度大大下降,造成测量精度不高、稳定性差等问题,因此需要对其进行补偿,故开展了磁通门传感器、加速度传感器输出信号的硬件补偿、线性补偿以及非线性补偿,形成了一套综合补偿方案。
随钻传感信号硬件补偿和线性补偿具有一定效果,但由于大部分的传感器本身就是非线性的,而且在测量过程中又受到各种环境因素的干扰,所以采用小波神经网络对距离传感器信号进行了非线性补偿,建立了利用BP算法训练小波对传感器信号进行非线性补偿的算法,取得了比线性补偿更好的效果；之后,根据动态神经网络思想,构造了用于随钻测量信号补偿的Elman神经网络,进一步提高了随钻信号补偿精度,并达到了较高的运算速度。再后,又提出了基于遗传Elman神经网络的补偿方案,经过GA-Elman神经网络补偿后的井斜方位与实测井斜方位相比有了很大的改善,基本接近理论值；最后,基于蚁群算法,根据自适应思想,在Elman神经网络的基础上,提出了一种自适应蚁群Elman神经网络对随钻测量信号的补偿方案,使得补偿误差进一步减小
基于协同设计思想开展了油田定向井随钻测量系统的设计,采用嵌入式设计提高了实时性；采用在线系统的编程技术,缩短了开发时间,节省了开发成本；采用模块化设计,便于扩展与通信。经测试,所设计的随钻测量系统精度高于油田在用的随钻测斜系统,提高了定向井钻井水平,且系统的稳定性、实时性、准确性和安全性均相应提高。
目前,绝大多数的无线随钻测量系统都是采用泥浆脉冲传输方式,因而信号的传输速度是关键因素。然而,钻井液中含有粘土、岩屑、重晶石粉等固相物质,并且其中存在着的游离状态的气体往往形成气泡,从而增加了信号传输速度问题的复杂性。为此,基于泥浆脉冲传输速度的计算模型,分析了钻井液脉冲的传输速度是随着钻井液密度的关系,以及与含气量的关系；分析了固体、液体之间密度和压缩性的差异对传输速度的影响,以及正、负脉冲的传输速度的变化。
The hole direction parameters of drilling tool must be measured promptly and accurately during the process of directional drilling in oilfield, and the hole trajectory while completion is also needed, so that Measurement While Drilling signal detection and process become an important factor of weighing directional drilling level.
Centrifugal weight and magnetic compass are adopted in the traditional inclinometers, while accelerometer with high sensibility and precision, and the fluxgate sensor with high anti-vibration are used in modern times. Single/multiple point and continuous inclinometers are developed by combining new type sensors with computer technology. MWD(Measurement While Drilling) system, combining downhole logging with real-time transmission, which can make sure real-time detection and high quality measurement material, mainly in Wireless measurement while drilling technology will become the development direction of directional well in the future.
With the development of petroleum exploration and production, the geology status has become more complicated, the number of deep well, ultra-deep well, high-temperature well, high-pressure well and so on, will become more and more. The main parameters of MWD of directional well in oilfield are accelerometer and fluxgate sensor signals, in order to improve the detection precision of measurement will drilling signal, the mathematic models of fluxgate and accelerometer are established, and the measurement while drilling error is analyzed.
The sensors of measurement while drilling are affected by the environmental factors, such as temperature, humidity, power supply fluctuation, which enormously induce the measurement veracity, so that the measurement precision is not high and the stability is not good, must be compensated. Hardware compensation, linear compensation and nonlinear compensation for fluxgate and accelerometer signals are formed to a synthesized compensation scheme.
Hardware compensation and linear compensation for sensor signals of measurement while drilling have some effect, but most of sensors are nonlinear, which are affected by the environmental factors in the process of measurement. Range sensor is compensated nonlinearly by wavelet neural network, nonlinear compensation algorithm for sensor signals is established, BP algorithm training wavelet, which has a better effect than linear compensation. Then, based on the idea of dynamic, the neural Elman neural network is constructed for measurement of drilling signal compensation, which further improves the precision and has a highly operation speed. And the compensation scheme based on genetic Elman neural network is also presented, after using GA-Elman neural network, the inclination and azimuth have improved a lot compared to the actual values, approaching theoretical values approximately. At last, based on ant colony algorithm, adaptive idea, and Elman neural network, a compensation scheme is presented of adaptive ant colony Elman neural network for measurement while drilling signals, which induced the compensation error to a greater extent.
Based on the cooperative design idea, Measurement While Drilling system of directional well in oilfield is designed, in which embedded design is applied to improve the real-time performance, the In System Programming technology is applied to shorten system developing time and declines the development cost, model design is applied to expand and communicate conveniently. This measurement while drilling system has a higher precision then the system in the oilfield at present, which improves the drilling level of directional well, and the stability, real-time performance, accuracy and security are also improved to some extent.
At present, the mud pulse signal transmission method was applied to the most of wireless measurement while drilling system, so the key factor of wireless MWD system is the transmission speed. The drilling fluids is mainly composed by clay, rock debris, barite powder, and bubble produced by air existing in free state. Therefore signal transmission speed become even more complex. The computing model of mud pluse signal transmission speed considers the relation with drilling fluid density and gas content. It also considers the influence of density and contraction difference between the solid and liquid, and the transmission speed variation of positive pulse or positive pulse.
引文
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