Downhole gas-kick transient simulation and detection with downhole dual-measurement points in water-based drilling fluid

Chao Wang, Gonghui Liu, Zhirong Yang, Jun Li*, Tao Zhang, Hailong Jiang, Miao He, Ming Luo, Wentuo Li

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Gas kick is generally difficult to discover in time using traditional surface detection methods, which results in a significant wastage of time and money. Owing to the restriction of the low data-transmission speed of measurement-while-drilling system, downhole measured data is usually ignored in gas-kick detection. Furthermore, surface detection methods comprising the use of pressure and flow-rate sensors require professional knowledge and many input parameters, some of which are required to be assumed. In this study, we used downhole dual measurement points for detecting gas kick without the use of other surface input parameters. Firstly, we developed an end-to-end supervised neural network to determine the still and circulation working conditions, which were used for calculating the drilling fluid density and viscosity. Secondly, an unscented Kalman filter was applied to perform a backward gas fraction calculation dynamically. However, this downhole calculation method cannot be used in highly deviated and horizontal wells. Because there is a downhole fluctuating pressure generated during the rock breaking, we proposed an auxiliary gas-kick detection method based on the theory of pressure wave attenuation. This method can be applied to all well types. To evaluate the proposed gas-kick detection method, we used a gas-liquid flow simulation model combined with a pump rate model, screw-drilling-tool pressure-consumption model, rock-breaking model, and formation permeability model to generate transient data with the highest possible accuracy. The advection upstream splitting model was used as the numerical scheme. The accuracy of the simulation model was successfully validated using two field experimental data sets. Finally, we generated a set of vertical and horizontal well data each with the simulation model to test the gas-kick detection method. The experiment results showed that the proposed gas-kick detection model was successful in detecting gas kick and obtaining the accurate gas fraction.

Original languageEnglish
Article number103678
JournalJOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING
Volume84
DOIs
Publication statusPublished - Dec 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Advection upstream splitting
  • Attenuation of pressure wave
  • Downhole pressure measurement
  • Gas-kick detection
  • Machine learning
  • Unscented kalman filter

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