Analyses of the Accuracy and Reliability of Magnetic Directional Surveys

Abstract

Abstract Multi-station processing techniques can be used to improve the accuracy of wellbore directional surveys by removing the effects of systematic errors. The main intention of this paper is to provide improved understanding and documentation of such methods when applied to magnetic surveys. Basic properties of the method are discussed in detail. The combined use of statistical tests and multi-station estimation techniques are evaluated, together with the ability to improve the accuracy of wellbore positions. This is demonstrated for a wide range of surveying conditions. Due to a number of reasons; poor geometry, low redundancy, high random noise levels, the presence of gross errors, and errors in the Earth's gravity and magnetic field references, it is often possible that there will be a misinterpretation of the estimation results. This may lead to reduced accuracy and quality of the surveys. Even under the best possible conditions it is demonstrated that the processing of multiple surveys does not necessarily improve accuracy, rather it impairs accuracy and produce unreliable results. The study also shows the importance of having a sound understanding of the simultaneous effects of potential uncorrected systematic effects, in order to increase the chance of making correct conclusions. Introduction By processing multiple surveys simultaneously, systematic errors can be estimated and accounted for[1]. A basic assumption is that the conditions affecting the measurements are equal. Multi-station estimation techniques (MSE) are based on the least squares method. In this study, the systematic errors terms are estimated together with the directional parameters for each survey station; the magnetic azimuth Am, inclination I and high side gravity toolface t. Since the measurements are non-linear functions of the unknown parameters (see Equations (A-1) - (A-6)), the Gauss-Newton method will be used to estimate the parameters. This method is based on linear approximations about initial values of the unknown parameters, see Appendix B for details. The estimation is based on the linear regression model: (1)