Flow deviation correction through the thermal expansion effect enhances the precise detection of overflow in time-series neural networks
As oil and gas drilling advances into deeper and more complex formations,traditional methods for monitoring overflow accidents show delays and offer multiple potential solutions. The challenging formation conditions,along with high temperature and pressure underground,result in flow measurement deviations,which significantly affect the accuracy of overflow detection. To address this issue,a flow correction model was developed,accounting for the thermal expansion of mud to correct these deviations by mitigating non-overflow influencing factors. Furthermore,based on the corrected flow data,an overflow identification and warning model was established using multivariate data fusion and a temporal neural network. This model eliminates missed overflow reports and significantly improves the timely detection rate compared to conventional methods. It can provide warnings up to 5 minutes in advance,offering substantial application value and potential in drilling operations.
万方数据
维普
