Fast simulation of array laterlog utilizing optimized computational model and neural networks

A new method has been developed to rapidly simulate array laterolog (ALL) responses in invaded formations drilled by deviated wells. This method is characterized by two key aspects: simplification of the computational model and acceleration using neural networks. Initially, a five-layered model in combination with an equivalent resistivity scheme is chosen to describe the formations with arbitrary vertical layers. Additionally, three radially invaded layers among the five vertical layers are identified, with the remaining two invaded layers assumed to an uninvaded bed using the radial geometrical factor. These simplifications result in a computational model with only comprises 17 parameters, ensuring both accuracy and generalization. The ALL database for the simplified model is then established using the three-dimensional finite element method (FEM). The Convolutional Neural Network (CNN) algorithm is employed to train the nonlinear mapping between formation parameters and ALL responses. Subsequently, this new ALL simulation method is applied to classical Oklahoma formations with varying well deviations. Numerical results demonstrate the simplified model’s excellent generalization ability for accommodating formations with arbitrary layers while maintaining a relative computation error within 2%. Compared to the traditional simulation method, the CNN-predicted ALL responses improves the computational speed by over two orders of magnitude, establishing a robust foundation for expeditious ALL data processing.