Unconventional oil production forecasting based on PiAM meta-learning

Accurate production forecasting serves as a critical determinant for optimizing extraction strategies, guiding long-term field management in reservoir development. Both conventional methods and deep learning techniques face significant challenges in production forecasting due to the increasing complexities of reservoir extraction. Firstly, traditional production forecasting methods often fail to fully capture the complex reservoir behavior. Finally, these approaches demonstrate suboptimal performance in wells with limited data. These problems can lead to a decrease in prediction accuracy. To address these challenges, this paper introduces the Patching-iTransformer method and applies meta learning. The method improves prediction accuracy and overcomes the problem of few samples in production forecasting. Specifically, we implement a patching mechanism that segments the input time series, thereby converting the univariate time series into a two-dimensional representation. This architectural enhancement significantly strengthens the modelʼs capability to capture latent interdependencies among variables. Currently, we develop a PiAM meta-learning algorithm with domain-specific adaptation for oil field applications by quantitatively assessing individual well contributions to reservoir exploitation. We use time series data from real wells to evaluate the accuracy of multiple wells under the PiAM model. The experimental results demonstrate that Patching-iTransformer achieved better performance improvements than the iTransformer method. R² increased by 0.297, RMSE decreased by 11.64% and MAE decreased by 3.49%. PiAM meta-learning method demonstrated superior performance over the Patching-iTransformer model, showing a 0.535-point improvement in the R² coefficient along with a reduction of 27.54% in RMSE and a decrease of 28.22% in MAE.