A Model to Predict the Drilling Rate of Penetration in Shadegan Oil Field Using Artificial Neural Network

Time and cost are of great important issues in each project. Therefore, in the drilling industry as well, which is one of the most costly industries, there should be taken appropriate measures to save time and cost. In drilling, the operation can be carried out at a lower cost and time, by choosing the appropriate tool, as well as predicting accurate and timely parameters and probable problems. In this regard, one of the most effective strategies is to investigate field data and subsequently development and improvement of tools for the achieved results. In the drilling industry, in order to identify the problem and improve the drilling performance, generally, either laboratory tests or empirical models are used in accordance with previous experience. In this research, we tried to use novel methods and intelligent modeling to predict the penetration rate in drilling operations. For this purpose, artificial neural network (ANN) approach was used. In this paper, using an artificial neural network and data from Shadegan oil field drilling data (400 data), a model was developed to predict the rate of penetration, and then the results were compared with the results of modeling with multivariate regression. To evaluate the main characteristics of the model, its accuracy was measured using adjacent wells data. The results of the research showed that the correlation coefficient for the neural network is 0.97, and determination coefficient is 0.94 and the error histogram rate is proportional to the zero error 0.005, while the corresponding value in the statistical analysis the correlation coefficient is 0.94 and the determination coefficient is equal to 0.89, which indicates a higher accuracy of neural network modeling.