Artificial neural network-based prediction and geomechanical analysis of lost circulation in naturally fractured reservoirs: a case study

Abstract

Lost circulation is one of the most troublesome and costly problems in drilling industry. This phenomenon can lead to some serious problems such as wellbore instability, differential pipe sticking and blow out. Meanwhile, it is possible to mitigate the risk of lost circulation by carrying out geomechanical analysis and real-time prediction. Artificial neural network (ANN) is an efficient tool to predict the events due to the effective parameters. Generally, there are various factors including geomechanical and non-geomechanical parameters that affect lost circulation. In this paper, two ANNs have been developed to predict the lost circulation in naturally fractured formations. Firstly, an ANN has been designed without considering geomechanical parameters as inputs. Then another ANN has been developed by considering both the geomechanical and non-geomechanical parameters. Finally, two ANNs have been compared to investigate the geomechanical parameters effect on lost circulation. In both the models, principle component analysis has been applied to improve the ANN performance. Results reveal that the developed ANN contains geomechanical parameters which have a higher correlation coefficient and lower error. In addition, an ANN-based sensitivity analysis has been done to find out how different geomechanical parameters can affect lost circulation which is followed by a geomechanical analysis.