煤层气井动态产能拟合与预测模型

摘要

Based on modern artificial intelligence theory and mathematical statistics theory,BP neural network model and monthly/cumulative production model for fitting and predicting coalbed methane（CBM） wells productivity were established to verify the validity of these models by examples.The application results show that two models can match production data of CBM wells and quantitatively predict it.BP neural network model has high accuracy in matching the data points of gas production and predicting well productivity in a short term but not in a long term.Thus,this model is appropriate for the short-term predictions for productivity of CBM wells,even though the wells with unsteady gas production.Monthly/cumulative production ratio model has high accuracy in matching the change trend of monthly/cumulative production ratio and predicting well productivity not only in a short term but also in a medium-long term.However,the validation of this model is determined by the exponential relationship between monthly/cumulative production ratio and production time.Therefore,this model is very suitable for predicting the futural productivity of CBM wells with stable gas production during the past.%基于现代人工智能理论和数理统计理论,建立了煤层气井动态产能拟合和预测的时间序列BP神经网络模型和月产/累产比值模型,并通过实例分别验证其在煤层气井产能拟合和预测中的有效性。应用实例表明,这两类模型均能很好地拟合煤层气井的生产历史,并能进行准确定量预测,但各有差别。其中神经网络模型对数据点具有极高的拟合程度,且短期预测精度高,但中长期预测精度较差,因此,该模型适合对产气不稳定的气井进行短期产能预测;月产/累产比值模型对月产/累产比值的整体变化趋势具有较高的拟合程度,且中长期预测精度高,但模型的有效性取决于气井产能的稳定性,因此,该模型适用于预测产气稳定的气井产能。