Gas production prediction of tight gas fields based on ARIMA model
There are random shut-in operations in the daily production data of natural gas Wells,which is an unpredictable human factor.In order to overcome the error caused by human factors,the cumulative gas production was taken as a time series based on the actual well opening production data,and the differential autoregressive moving average model(ARIMA)was used to model 44 production wells in Block S of the tight gas field in the Ordos Basin based on the ARIMA model.The previous cumulative production series was taken as a training set to predict the cumulative gas production of 300 days.The cross-validation re-sults show that:①As a linear model,ARIMA model has a good fitting effect on cumulative gas production of gas wells with stable production,simple method and high prediction accuracy.It has a poor forecasting effect on single wells with cliff-type production changes,and a small error in forecasting output per block,which has a good fitting effect and application value.②It is difficult to determine the optimal parameters by using the trailing and truncated features of the traditional autocorrelation function and partial correlation function,and the human factors are large.After determining the maximum order of gas production affected by the historical data,the ergodic method is used to establish the model.Taking Akike information criterion(AIC)and Bayesian information criterion(BIC)as the minimum value as the model selection strategy,the predicted cumulative production and actual production errors of Block S in300 days were0.4%(AIC)and 2.11%(BIC),respectively,which met the prediction accuracy of gas production.③According to the sta-tistical results of 44 wells in block S,on the premise that the data series is stable after the difference is satis-fied,the data series predicted by the first and second order difference alone deviates greatly.In order to eliminate random errors,the average value of the first and second order difference is taken as the final pre-diction result,the prediction effect was improved.
cumulative gas production predictionARIMA modeltime series analysisgas field develop-ment
万方数据
维普
