A prediction method of annular pressure in high-pressure gas wells based on the RF and LSTM network models
The continuous annular pressure of a high-pressure gas well in the production may cause casing string deformation or collapse,which is one of the main reasons for wellbore integrity failure.However,traditional methods for predicting the annular pressure yield unexpected accuracy.This paper presents a new prediction method of annular pressure based on random forest(RF)and long short-term memory(LSTM)network model.By taking a high-pressure gas well in the Sulige gas field of the Ordos Basin as an example,the method is verified.Firstly,the principal component analysis(PCA)and correlation coefficient method are used to identify the main factors affecting the annulus pressure.Then,the theoretical value of wellbore temperature and pressure field of high-pressure gas wells and an isolated forest model are used to physically interpret the principal components and clean the data.Based on the cleaned data,a quantitative prediction model of annulus pressure is established by using the RF and LSTM models.Finally,a combined RF-LSTM annulus pressure prediction model with a higher accuracy than any single model is established by combining the weights of the two models.The following results are obtained.First,the main influencing factors of annulus pressure are temperature component,pressure component,yield component,corrosion degree and production state.The temperature component has the highest correlation with annulus pressure.Second,through the error format,outliers and data cleaning based on wellbore temperature and pressure field,the training set of influencing factors of annulus pressure after data cleaning can be obtained.Third,the mean absolute error(MAE)method can be used to establish a combined model with error scores less than any single model and goodness of fit between the two.Therefore,the two types of models with high goodness of fit and low error scores can be combined to form a combined model that meets the two scores at the same time.In conclusion,the proposed method for quantitatively predicting annular pressure based on big data technology and algorithm is innovative,accurate and feasible.It provides a referential decision-making tool for field annular pressure prediction and risk control,and also a theoretical support for the realization of real-time prediction,early warning and control of annulus pressure risk.
万方数据
维普
