Predicting asphaltene precipitation in CO2 flooding processes using artificial intelligence techniques

Abstract

The removal of asphaltene depositions from crude oil wellbores and tubing is a very difficult and expensive process. Therefore, a very accurate predictor for the Asphaltene Precipitation (AP) is a very important tool for decision making. In this paper, the AP during the injection of carbon dioxide into crude oil reservoirs is forecasted using an artificial neural network (ANN). For this purpose, experimental data is collected from six different oil fields. 70% of the collected data is utilized for training and to develop the proposed ANN. Trainlm training algorithm was found to be the best ANN architecture for AP forecasting. In order to validate the capability of the designed ANN 30% of the unseen data is utilized for validation. The forecasting results show the Mean Square Error (MSE) of the forecasting is 0.0018 that confirms the high accuracy of the model. In order to evaluate the capability of the proposed methodology, the results of proposed ANN are compared with modified Hirschberg model. Comparison confirmed the proposed ANN has better capability and higher accuracy. In the final stage, the effects of various operating parameters on the AP during gas injection are examined. The results show that the most sensitive parameter is the reservoir temperature. In addition, there is a high correlation between an increase in the AP with an increase in carbon dioxide concentration in liquid phase.