Deep learning-based method for reducing the number of transmitting coils in logging while drilling tool

Abstract

Electromagnetic wave logging while drilling (LWD) technology is an important tool for evaluation of formation oil and gas content. It generally adopts multi-transmitter–receiver coil system structure and the symmetrical coil system arrangement with equal transmitter–receiver spacing can obtain the measurement results with borehole compensation. Here, we develop a method to realize wellbore compensation by deep learning for logging data inversion. This paper focuses on reasonable inversion of logging data through deep learning technology, which is combined with Levenberg–Marquardt (LM) algorithm, modular and fast construction of deep neural network (DNN) model. Under the condition of reducing the outermost transmitting coil, the logging data are inversed, and the inversion effect is evaluated. Our research shows that the combination of neural network and logging data can realize the measurement results with borehole compensation under the condition of reducing one transmitting coil, thereby shortening the instrument length to reduce drilling tool sticking risk and effectively reducing the LWD instrument structure complexity, high power and other problems. At the same time, the accuracy of logging data inversion is tested. The test results show that the DNN method can achieve high-precision inversion, and the average error is reduced by about 50% compared with the traditional algorithm such as linear regression.