Production prediction of pumping wells based on multi-mode transfer learning

Abstract In the modern petroleum industry, it is difficult to establish an intelligent oil well production prediction model due to the limited number of samples for actual oil well operating conditions and the uneven distribution of samples between different operating conditions. To address this issue, this article proposes a new method for predicting oil well production. Firstly, by analyzing the working process of the pumping well, key parameters describing the fault conditions of the oil well are proposed, and a dynamic prediction simulation model for the lifting of the pumping unit under different working conditions is established; Then, the grey wolf optimizer (GWO) algorithm is used to optimize the fault parameters, so that the simulation model can adapt to the commonalities of various operating conditions. Finally, the target model is pre trained using the source domain model, and small sample data from the target domain is added for model optimization to accurately predict oil well production under limited electric power data conditions. The experimental results show that this method can simulate different oil well operating conditions and accurately predict oil well production, providing an important reference for applying artificial intelligence technology to the traditional energy industry.