Intelligent prediction and key factor analysis to lost circulation from drilling data based on machine learning

Abstract

Lost circulation during drilling wells is very detrimental since it greatly increases the non-productive time and operational cost, also seriously lead to wellbore instability, pipe sticking, blow out, etc.. However, in the process of drilling wells, geological characteristics and operational drilling parameters all may have impacts to the lost circulation. This makes the establishment of the relations between the lost circulation and drilling factors very challenging. In this paper, we tested five different kernel function (linear, quadratic, cubic, medium Gaussian and fine Gaussian) derived support vector regression (SVR) models and four-layer artificial neural network (ANN). By combining their accuracy and time efficiency, the ANN is regarded as the optimal predictor of lost circulation. By training ANN using different combination of drilling features, we concluded that depth, torque, hanging weight, displacement, entrance density and export density are the key factors to accurate predict the lost circulation. The corresponding trained ANN network can achieve 99.2% accuracy and evaluate whether a drilling feature vector corresponds to lost circulation or not in milliseconds.