Prediction of post-refracture production of low-productivity wells using deep time series models:A critical review

JIA Jing; FAN Qinghu; WANG Lichang; LI Diquan

Abstract：At present,unconventional crude oil production in China accounts for less than 2%of total oil output,while mature oilfields remain the primary contributors to stable production over an extended period.Re-fracturing is a crucial component of reservoir stimulation,and accurate post-fracturing production prediction plays a key role in the selection of target wells for re-fracturing.However,due to internal discontinuities in the reservoir,heterogeneity in porosity and per-meability,and missing critical reservoir parameters,conventional post-fracturing production prediction methods based on empirical formulas or numerical simulations exhibit limited applicability in mature oilfields.Deep learning models provide a promising alternative.Traditional deep learning approaches,such as Recurrent Neural Network(RNN)and long short-term memory(LSTM)networks,suffer from gradient vanishing and limited capability in modeling long-term dependen-cies,making them inadequate for handling petroleum time-series data characterized by high dimensionality,non-stationar-ity,and noise interference.The Transformer architecture,leveraging its multi-head attention mechanism and parallel com-puting capabilities,effectively captures both short-and long-term dependencies in production time series.A comprehens-ive review of re-fracturing technology advancements and recent progress in deep time-series forecasting models has been conducted.Based on this,a Transformer-based deep time-series prediction model is proposed for forecasting post-refrac-turing production in low-efficiency wells.A case study is performed using historical production data from Block W in an oilfield located in the Junggar Basin.This study represents an innovative attempt to establish a theoretical and methodolo-gical framework for large-scale,efficient,and precise well selection in mature oilfields undergoing re-fracturing,offering novel perspectives and solutions for maintaining stable production.Future research should focus on two key directions.First,to control computational costs,optimizing the attention mechanism in the classical Transformer architecture and in-tegrating time-series decomposition techniques is recommended to enable low-computation refracturing production predic-tion.Second,for multi-block collaborative well selection,incorporating domain adaptation theories—particularly ad-versarial domain adaptation and pseudo-label domain adaptation—should be explored to develop a Transformer backbone with transfer learning capabilities.