A Comprehensive Statistically-Based Method to Interpret Real-Time Flowing Measurements

Dawkrajai, Pinan ; Romero, Analis A. ; Yoshioka, Keita ; Zhu, Ding ; Hill, A.D. ; Lake, Larry W.

University of Texas at Austin

关键词: Oil Wells;    02 Petroleum;    Flow Rate;    Monitoring;    Fluid Flow;   

DOI  :  10.2172/835630
RP-ID  :  NONE
RP-ID  :  FC26-03NT15402
RP-ID  :  835630

美国|英语

来源: UNT Digital Library

【 摘 要 】

In this project, we are developing new methods for interpreting measurements in complex wells (horizontal, multilateral and multi-branching wells) to determine the profiles of oil, gas, and water entry. These methods are needed to take full advantage of ''smart'' well instrumentation, a technology that is rapidly evolving to provide the ability to continuously and permanently monitor downhole temperature, pressure, volumetric flow rate, and perhaps other fluid flow properties at many locations along a wellbore; and hence, to control and optimize well performance. In this first year, we have made considerable progress in the development of the forward model of temperature and pressure behavior in complex wells. In this period, we have progressed on three major parts of the forward problem of predicting the temperature and pressure behavior in complex wells. These three parts are the temperature and pressure behaviors in the reservoir near the wellbore, in the wellbore or laterals in the producing intervals, and in the build sections connecting the laterals, respectively. Many models exist to predict pressure behavior in reservoirs and wells, but these are almost always isothermal models. To predict temperature behavior we derived general mass, momentum, and energy balance equations for these parts of the complex well system. Analytical solutions for the reservoir and wellbore parts for certain special conditions show the magnitude of thermal effects that could occur. Our preliminary sensitivity analyses show that thermal effects caused by near-wellbore reservoir flow can cause temperature changes that are measurable with smart well technology. This is encouraging for the further development of the inverse model.