Precise underwater wellhead positioning method based on multi-source data fusion

Traditional multi-source data fusion algorithms exhibit certain limitations that affect the accuracy of underwater wellhead positioning. To address this issue, this paper proposes a novel positioning method that integrates orthogonal basis function (OBF) decomposition with particle filtering fusion. This study systematically analyzes the current challenges in underwater wellhead positioning, including accuracy requirements, stability constraints, real-time performance demands, and cost-effectiveness considerations. In this method, magnetometers and shallow profiling techniques are combined to acquire wellhead coordinates. The fusion signals are decomposed using OBF algorithms, followed by particle filtering implementation for wellhead positioning. Experimental results demonstrate that this approach achieves high-precision underwater wellhead positioning with errors less than 0.5 m on both X-axis and Y-axis, while maintaining minimal Z-axis (burial depth) deviations. This research not only provides an effective solution for underwater wellhead positioning, but also offers reference for research in related fields.