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Smart Solution To Selecting Artificial Lift And Improve Oil Production Using Machine Learning

Abstract:

The main goal of this research is to evaluate the artificial systems used to produce oil through the machine learning technique with the purpose of selecting a single optimal SLA of section 66 of the Gustavo Galindo Velasco oilfield, to increase production, improve operations, reduce the risks of losses and evaluate the viability and feasibility of the proposal. The present work is based on a descriptive, quantitative and comparative investigation of the different methods of artificial lifting with which it is being produced in the GGV oilfield "section 66". Theoretical research is necessary for the collection of information on the SLAs by BM, HL and plunger or SW. The pbkp_rediction of the decision tree algorithm gives as a result that the optimal artificial lift system to be implemented in the entire section 66 is mechanical pumping, because the wells that operate with this system obtained a higher production rate during the 2016-2020 period in comparison. to the wells that produce with other artificial lift systems, and in turn generated lower operating and maintenance costs because the interventions carried out in the wells are low cost, in addition money is saved in fuel, grease, lubricants and spare parts.