  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.
