Overflow is an important precursor of blowout.Timely detection of early overflow is the direct and critical way to successfully and effectively control blowout.In response to the challenge of timely detection of drilling overflow risks,active exploration is conducted to achieve intelligent early warning of overflow risks using real-time data during drilling.Hydrocarbons in the formation fluid invade the wellbore in the form of fractured gas,diffused gas,and percolated gas,exhibiting different characteristics of gas logging data.The gas logging curve of fractured gas appears as"peak-shaped"or"box-shaped",the curve of diffused gas shows a rise in baseline,and the curve of percolated gas generally appears as"box-shaped".Through in-depth study of the ways in which formation fluid enters the wellbore and the characteristics of gas logging data,the early warning mechanisms of overflow risks for"high pressure and low permeability"and"connection gas monitoring analysis"are analyzed and summarized.A sample learning case set is formed,and a fully-connected neural network model for intelligent early warning of overflow risks is established.Furthermore,this can lead to the automation and intelligence of early warning systems.Tests indicate that the early warning model can adapt to a wide range of geological and engineering conditions that induced overflow risks in wells,especially suitable for overflow monitoring in unconventional wells.It can dynamically reflect the balance relationship between hydraulic pressure and formation pressure.At the beginning of overflow risks,accurately alarm the overflow risks in time,and guide the effective prevention and control of overflow on the drilling site.This enables the transition from overflow detection to active prevention.The intelligent early warning model of overflow based on gas logging data has conducted active and effective exploration for real-time monitoring and analysis of drilling overflow risks,demonstrating high promotion and application value.
万方数据
维普
