Improving Quality Control of Directional Survey Data With Continuous Inertial Navigation

Author:

Stephenson Mark A.1,Wilson Harry1

Affiliation:

1. Eastman Christensen

Abstract

Summary Continuous inertial navigation systems (INS) for high-accuracy surveying canbe used to enhance quality control of survey data significantly. This papershows how to use the unique capabilities of these systems while retainingcompatibility with widely accepted quality-control methods. A system now in usein the North Sea provides concrete examples. Introduction Continuous INS's are desirable for fast, high-accuracy surveying. Theirrelative independence from inclination and latitude makes them particularlyuseful for surveying high-angle wells at high latitudes. A less obvious, butequally important, benefit of these systems is the improved quality control ofsurvey data possible both at the rig site and in the validation of systemperformance before and after surveying. In many applications, continuousinertial navigation is a mature technology for determining real-time positionand velocity, but this technology is new to surveying. The same innovationsthat make improved surveying possible can cause confusion when approached fromthe viewpoint of conventional surveying. We show where and how current methodsof survey-data quality control can be used with continuous INS's, introduce newtechniques, and point out pitfalls to be avoided. Background: Aided Strapdown Inertial Navigation An INS determines the position and velocity of a moving body in threedimensions by integrating measured components of the acceleration of the bodymathematically. A conventional directional survey system measures inclinationand azimuth angles at stations along the wellbore; positions are calculatedfrom these angles and from measured depths by assuming some shape for thewellbore between stations. Both a conventional system and an INS can be used todetermine positions. The main difference is that an INS does so more directly. An INS is better described as a positional surveying device than as adirectional one. A body moving in three dimensions does not provide a stablecoordinate system for performing integrations. There are two common solutionsto this problem. A gimbaled INS maintains a stationary platform for theaccelerometers with torque motors. The system uses the angular rate outputs ofgyros attached to the platform to control the motors. A strapdown INSmathematically platform to control the motors. A strapdown INS mathematicallytransforms the outputs of accelerometers attached to the body into a locallylevel coordinate system before performing integrations. The system uses theoutputs of gyros attached to the body to update continuously the transformationmatrix for converting from body coordinates to level coordinates. A strapdownsystem does mathematically what a gimbaled system does mechanically. Theruggedness and smaller size that come with eliminating gimbals make strapdownsystems desirable for survey applications. Fig. 1 shows the basic operation ofa strapdown INS. Because integration amplifies the effects of system errors, the outputs of an INS will drift increasingly with time. If uncontrolled, positional drift errors of roughly 500 mm/s [6,000 ft/hr] are typical. One wayto overcome drift errors is to stop the survey tool periodically. Whilestopped, the system uses the known zero velocity to update the integrators. This is the approach used with the large-diameter gimbaled system that, untilrecently, was the only INS commonly available for surveying. A differentapproach is necessary to overcome drift errors without stopping the surveytool. To survey continuously, an additional measurement independent of the INSis needed as a reference. For a wireline system, measured cable length is anappropriate choice. A cable-aided system compares this measurement with thecourse length calculated by the INS. A feedback loop uses the differencebetween the two values to prevent the buildup of errors. Cable aiding makesaccurate prevent the buildup of errors. Cable aiding makes accurate continuoussurveying possible with an INS. The navigation system can use a Kalman filterto implement cable aiding. A Kalman filter is an algorithm for optimallyestimating the error state of a system from measurements corrupted by noise. For surveying, the error state of the system includes errors in survey toolposition, velocity, and orientation; cable length parameters; and varioussensor parameters. By blending the two parameters; and various sensorparameters. By blending the two values of course length from the INS and thecable measurement, the filter can improve error estimates of all the navigationparameters. The navigation system then can correct these parameters for theestimated errors continuously. Kalman filters enhance the performance of INS'sin many ways. They reduce the effects of noise during alignment and navigation. They can blend pure INS outputs with independent measurements and withconstraints imposed by the application. They also generate real-timestatistical data related to the accuracy of estimated values. How well Kalmanfilters perform depends mainly on how well the system is modeled. Estimatesbased on bad assumptions are optimal only in a vacuous sense. Fortunately, modeling of aided INS's is well understood. Fig. 2 illustrates cable-aidednavigation. A navigation computer compensates sensor data for known erroreffects and calculates probe position, velocity, orientation, and the lineardistance traveled along the wellbore (the calculated course length). Thedifference between the measured cable length and the calculated course lengthis an error signal input into a Kalman filter. The filter inputs also includethe inertially computed lateral displacements of the probe in the wellbore, which should be zero. The Kalman filter uses the inputs to calculate correctionvalues to update the sensor compensation and inertial navigation data. Outputsfrom the computer include position components and uncertainties in the positiondata. Savage gives a detailed description of aided navigation. Cable aidingwith a Kalman filter is not a new idea. Sandia Natl. Laboratories developed anexperimental wellbore INS between 1979 and 1982. The system did not use cablemeasurements, but in a report on system software Wardlaw suggested cable aidingas a topic for further investigation. In other applications, odometer aiding isa direct analog of cable aiding and has been in use for many years. Although wehave discussed cable aiding for a strapdown INS, note that this technique alsocould be used with a gimbaled system. Similarly, zero-velocity updates can beapplied to a strapdown system as well as to a gimbaled one. Common Misunderstandings of Inertial Navigation There are two common misunderstandings in the survey industry about what an INS is and what it does. One is the assumption that an INS is either agyrocompass or an attitude reference system. SPEDE P. 100

Publisher

Society of Petroleum Engineers (SPE)

Subject

General Engineering

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3