Fast hierarchical inversion for borehole resistivity measurements in high-angle and horizontal wells using ADNN-AMLM

TL;DR

This paper introduces a fast, noise-robust hierarchical inversion method combining adaptive deep neural networks and modified Levenberg-Marquardt algorithm to accurately determine formation resistivity in complex borehole environments.

Contribution

It presents a novel integration of ADNN with AMLM for rapid, accurate, and noise-resistant resistivity inversion in high-angle and horizontal wells.

Findings

ADNN achieves 0.021s modeling per point with less than 2% error.

Inversion accuracy exceeds 97% in simulated environments.

Convergence is achieved in only 10 steps with AMLM.

Abstract

With the rapid development of deep learning, intelligent scheme is gradually introduced to solve various nolinear inverse problems. In this paper, we combine an efficient adaptive deep neural network (ADNN) framework with adaptive modified Levenberg-Marquardt (AMLM) algorithm based on three-layer inversion model to exact formation resistivity and invasion depth from array laterolog resistivity measurements. ADNN presented in this paper can realize the 2D/3D fast forward modeling of array laterolog. AMLM algorithm and hierarchical inversion scheme are adopted to improve the anti-noise ability and convergence in complex logging environments, which realizing the fast and accurate reconstruction of longitudinal resistivity profile in HA/HZ wells. The numerical simulation shows that the ADNN forward modeling only takes 0.021s for each logging point, and the maximum relative error is less than 2%. Three-layer inversion model can eliminate the effect of surrounding bed and improve the inversion accuracy in thinly layered formation. The error between inverted results and truth model is less than 3%. The AMLM inversion algorithm can effectively suppress the influence of noise, and takes only 10 steps to achieve convergence.