Fluid Production Analysis Of Intelligent Completion Horizontal Wells Based On Wellbore Pressure And Temperature Data

The ability of intelligent wells for downhole monitoring and fluid production control allows well control decisions to be implemented that optimize current production and long-term recovery.Recently,along with the development of fiber optic sensors,accurate monitoring of the downhole pressure and temperature is a common practice.Interpretation and analysis of the monitoring data to locate water or gas influxes and quantify distributed as well as zonal,flow performance is an important step towards a comprehensive well and field control strategy.Temperature measurements have the potential to be as informative as pressure measurements in reflecting the reservoir’s and the well’s production performance.However,currently available tools are not applicable to inverse temperature modeling in intelligent completion.For the purpose of fluid production analysis and inflow profile quantification based on monitoring pressure and temperature data in intelligent well,study on coupled intelligent well pressure and temperature model and inversion model is conducted in the thesis.Firstly,considering the process of fluid flow and heat transfer in the intelligent wellbore,novel pressure and temperature models for steady state multiphase flow in both wells with intelligent completion and in the reservoir are presented.Secondly,using the presented pressure and temperature models,this thesis show that the water entry and gas entry can be detected by analyzing the downhole pressure and temperature profile.Then,based on the forward pressure and temperature models,the inversion model is established,by making the forward calculated temperature and pressure match the observed data,temperature and pressure data can be inversed to reservoir permeability distribution.By comparing the inversion speed of three commonly used gradient algorithm,the most efficient inversion method is choosed.Lastly,synthetic applications illustrate the feasibility of using this model to interpret the measured data and assist production optimization.