Multilateral Wells Evaluation Utilizing Artificial Intelligence

Abstract

Abstract Multilateral wells are considered to be an advancement revolution in the petroleum industry. The employment of multilateral wells ensured higher drainage and productivity of reservoirs through the utilization of diverse configurations. Achieving higher productivity and maximizing the reach from a multilateral well has highly improved inflow performance relationship (IPR) compared to that of a conventional horizontal well under certain conditions. Several analytical models have been developed to estimate the average oil flow rate of multilateral wells by utilizing reservoir parameters to come up with decent correlations for better accuracy. These models are accompanied with uncertainties and limitations due to the complexity of multilateral wells. Artificial Intelligence (AI) techniques have been proven to predict various parameters associated with high uncertainties in the oil industry. One of these methodologies is Artificial Neural Networks (ANN) which was utilized in this paper as new approach to predict the average oil flow rate of multilateral wells though the use of some reservoir parameters along with flowing wellhead data. As a comparable method, an analytical model was used to calculate the flow rate from several multilateral wells to quantify the value of utilizing ANN against other methods or correlations. Borisov's correlation that was developed for estimating the productivity of multilateral wells of planar configuration was used to calculate the oil flow rate of the multilateral wells and compared the results against actual average oil flow rates. Additionally, PROSPER software was utilized to estimate some wells' parameters including Productivity Index (PI) and flowing bottomhole pressure (FBHP) for oil rate calculations. Rigorous statistical error analyses have been obtained from ANN method and Borisov's correlation. The overall regression correlation coefficient was calculated to be 0.97 for ANN which shows a strong matching between predicted and actual field values with an overall absolute error of 7.85%. High divergence was found between oil rate calculated from Borisov's correlation and the actual average oil rate with an error greater than 50%. This indicates the actual advantage of the ANN method against other correlations. This paper discussed a new method for predicting average oil flow rates for multilateral wells using surface and reservoir parameters obtained from field data via the employment of Artificial Intelligence modeling. A model was constructed for enhancing the prediction of oil flow rate for multilateral wells and resulted in a great prediction accuracy proved by field data comparison.