Prediction of Sand Production from Oil and Gas Reservoirs in the Niger Delta Using Support Vector Machines SVMs: A Binary Classification Approach

摘要

Sand production is one of the critical research subjects in the petroleum industry. In the oil and gas industry, the production of sand particles associated with the reservoir hydrocarbons has become one of the most common problems a well may experience during reservoir lifetime. Sand production occurs in many fields across the world. This is easily seen in wells in the Niger Delta, Gulf of Mexico, Oman, Canada, Venezuela, Indonesia, Egypt, Trinidad and myriads of other places prolific to sanding. Managing sand production and ultimately its control in the oil and gas industry has been more or less a recurring problem. To fully understand the nature of sanding in an ingenuous way for sand control strategy, it is necessary to predict the conditions at which sanding occurs. Because so much have not been done in the implementation of the support vector machines for the prediction of the sanding onset in petroleum reservoirs, we are, for the first time, applying a robust approach, a binary classification problem approach for the prediction of sanding onset in petroleum reservoirs in the Niger Delta Region. By and Large, for the first time, the support vector machines (SVMs) classification approach, is used to identify whether sand will be produced or not in a hydrocarbon reservoir. The model presented in this paper takes into account different parameters (rock, fluid, geotechnical and other data) that may play a role in sanding. The performance of the proposed SVM model is verified using field data. It is shown that the developed model can accurately predict the sand production in actual field conditions. The results of this study indicate that the implementation of SVM methodology can effectively help engineers to make a proactive sand control plan with insignificant impairment to hydrocarbon production from subsurface reservoirs.