With the increasing use of horizontal wells in oil and gas development, measurement-while-drilling (MWD) systems require higher magnetic azimuth accuracy to ensure precise trajectory control. This study proposes a three-stage magnetic azimuth error compensation method based on multi-station analysis (MSA). First, the OPTICS clustering algorithm is utilized to identify and remove noise points, and ellipse fitting is applied to suppress radial magnetic interference. Second, an improved MSA model incorporating wellbore trajectory constraints is developed to minimize axial interference and enhance correction stability. Finally, a Gaussian Process Regression (GPR) model, using accelerometer and magnetometer data as features, is introduced to model and compensate for residual nonlinear errors. Experimental validation under simulated complex magnetic conditions shows that OPTICS-based preprocessing significantly improves ellipse fitting and reduces hard magnetic interference. The improved MSA lowers the mean azimuth error to approximately 2.5°, while integrating GPR further decreases it to below 0.04°. The proposed GPR model achieves an R² of 0.99972 and an RMSE of 0.02928° on the test set, confirming its strong nonlinear compensation capability. Overall, the proposed framework effectively suppresses magnetic interference and enhances azimuth accuracy, providing a robust solution for high-precision MWD applications in horizontal wells.

In magnetized casing environments, casing-induced magnetic interference can significantly reduce azimuth calculation accuracy and compromise the reliability of downhole tool spatial localization. To address this issue, this study proposes a magnetic-field-based spatial localization method for downhole tools in magnetized casing environments. First, a joint azimuth correction framework is developed. This framework combines ellipse fitting for radial distortion correction, single-axis multi-station analysis (MSA) for axial interference suppression, and a radial basis function neural network (RBFNN) for residual nonlinear error compensation. Subsequently, based on corrected azimuth information, the magnetic field distribution around the magnetized casing is analyzed through theoretical modeling and finite element simulation, and a cosine-type azimuthal response model is established. On this basis, a minimum-residual localization model is constructed to invert the measurement-point height and radial distance. The results show that the proposed correction framework effectively improves azimuth calculation accuracy, with the average RMSE reduced to 0.0371° after RBFNN compensation. In addition, the inverted height and radial distance show good consistency with the experimental values, demonstrating the effectiveness of the proposed localization method. This study provides an effective approach for spatial localization in complex downhole magnetic environments.