Research on intelligent technology for monitoring and early warning of gas invasion in deep-water wells
Aiming at the problems of delayed warnings,high false alarm rates,and low accuracy in gas invasion overflow monitoring techniques during deepwater well drilling,an intelligent gas invasion prediction model that combines early monitoring with logging monitoring is proposed.A comparative analysis of the advantages and disadvantages of the ultrasonic-based riser early monitoring method and the neural network-based logging monitoring method was conducted to select effective features as the foundational dataset for model training;through operating condition identification rules and label processing methods,gas invasion event label matching between the two types of monitoring data is achieved;a training method for the intelligent model based on neural networks was designed,and the architecture and workflow of the prediction analysis software are studied.The model is validated using actual case data,and experimental results show that compared to traditional logging monitoring methods,the new model advances the warning time;compared to early monitoring methods,it can significantly reduce the probability of false alarms,and the accuracy of predicting gas invasion levels has also been greatly improved.Therefore,the intelligent gas invasion prediction technology that combines early monitoring with logging monitoring methods can effectively enhance the accuracy and timeliness of gas invasion prediction during deepwater well extraction,providing innovative technical support for the safety of deepwater drilling.
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