ACGAN and BN based method for downhole incident diagnosis during the drilling process with small sample data size

Abstract

During the drilling process, the complicated geological environment makes drilling operations more difficult as the drilling depth increases, leading to a greater susceptibility to drilling incidents. The parameters obtained from a drilling incident are usually incomplete and the sample size is small, which is difficult to be used for incident analysis. This paper proposes a new method for the diagnosis of downhole drilling incidents. The drilling data is generated based on an Auxiliary Classifier Generative Adversarial Networks (ACGAN) and an incident diagnosis model is built using the Bayesian network (BN). The effectiveness and practicality of the proposed method are proved by the actual case study. Based on historical data, data augmentation is performed using the ACGAN model, and then parameter learning of BN is conducted. The established BN model based on large data samples can be used for the diagnosis of downhole incidents. The precision and F1-score of diagnosis are above 80%. Root cause diagnosis of downhole incidents can be performed by backward inference of Bayesian methods. It can prevent the occurrence of downhole incidents. The results prove the proposed method can diagnose downhole incidents in real time and obtain the causes of downhole incidents.