An artificial neural network approach to predict asphaltene deposition test result

作者：Nokandeh Nafice Rasuli; Khishvand Mahdi^*; Naseri Ali

来源：Fluid Phase Equilibria, 2012, 329: 32-41.

DOI：10.1016/j.fluid.2012.06.001

• Artificial neural network
• Asphaltene deposition
• Training data
• Validation data

Asphaltene deposition in reservoir, completion string or flow lines causes flow assurance problem including wettability reversal, permeability reduction, increased pressure drop, well and pipeline plugging and finally production rate reduction. Generally, asphaltene deposition in a sample of live oil, in different pressures and temperatures, is measured by High-tech expensive apparatus and used in asphaltene study in pipelines and reservoir. Present study describes an innovative method for easy and fast prediction of the asphaltene deposition test by use of artificial neural network (ANN). Different ANNs are designed and trained with different solution algorithms to find the best predictor for target samples. The output ANN shows significant accuracy for validation data and conclusively is reliable for prediction unknown values of target samples. Prediction of asphaltene deposition test results, gathered by ANN, is much time and cost saving than the conventional experimental studies.

• 出版日期2012-9-15