Application of adaptive neuro-fuzzy inference system and data mining approach to predict lost circulation using DOE technique(case study: Maroon oilfield)

Lost circulation is the most common problem encountered while drilling oil wells. Occurrence of such a problem can cause a lot of time and cost wastes. In order to drill oil wells, a fast and profitable way is necessary to predict and solve lost circulation problem. Expert system is a method used lately for problems that deal with uncertainty. In this paper, three approaches are carried out for prediction of lost circulation problem. These approaches include design of experiments(DOE), data mining, and adaptive neuro-fuzzy inference system(ANFIS). Data of 61 wells of Maroon oilfield are selected and sorted as the feed of the systems. Seventeen variables are used as inputs of the approaches and one variable is used as the output. First, DOE is conducted to observe the effects of variables. Plackett-Burman method is used to determine the effects of variables on lost circulation. After that, data mining is conducted to predict the amount of lost circulation. The class of regression is used to determine a function to model the data and the error of the model. Then, ANFIS is applied to predict the amount of lost circulation. The chosen data are used in order to train, test, and control the ANFIS. Furthermore, subtractive clustering is used to train the fuzzy inference system(FIS) of the model. The performance of the ANFIS model is assessed through the root mean squared error(RMSE). The results suggest that ANFIS method can be successfully applied to establish lost circulation prediction model. In addition, results of ANFIS and data mining are investigated through their prediction performances. The comparison of both methods reveals that ANFIS error is much lower than data mining.