Prediction of lost circulation zones using artificial neural network and functional network

Drilling high-pressure high-temperature (HPHT) wells lead to many difficulties and issues. One of the most difficulties during the drilling is the loss of circulation. 40% of the drilling's cost is belong to the drilling fluid, so the loss of these fluid causes an increasing in the total drilling operation's cost. Circulation loss has many consequences related to well control which can lead to the worst case of blowout. There are several approaches to avoid loss of return, one of these approaches is preventing the occurrence of the losses by identifying the lost circulations zones. However, most of these approaches are difficult to be applied due to some constraints in the field. The purpose of this work is to apply two artificial intelligence (AI) techniques to identify the zones of lost circulation. A data of real-time surface drilling parameters from three wells were obtained using real-time drilling sensors. The two methods of AI are functional networks (FN) and artificial neural networks (ANN). Well (A) was utilized to build the two AI models by dividing their data into training and testing. Then, well (B) was utilized to validate the developed AI models. A high accuracy was achieved by the two AI models based on the root mean squared error (RMSE), confusion matrix and correlation coefficient (R). Both AI models identified the zones of lost circulation in Well A with high accuracy where the R is more than 0.98 and RMSE is less than 0.09. ANN is the most accurate model with R = 0.99 and RMSE = 0.05. Moreover, ANN was able to predict the lost circulation zones in the unseen well B (R = 0.952 and RMSE = 0.155).