Abstract

With ongoing development of oil exploration and techniques, there is a significant need for improved well control strategies and formation pressure prediction methods. In this paper, a gas-liquid transient drift flow model was established according to the gas-liquid two-phase flow characteristics during the gas kick. A Roe scheme was used for numerical calculation based on the finite volume method. The changes of bottom-hole pressure, casing pressure, the development law of cross-sectional gas holdup, and gas velocity, along with the vertical well depth, were analyzed through simulation examples. The time-series characteristics of mud pit gain were obtained by adjusting the formation parameter. The complex nonlinear mapping relationship between the formation parameters and the mud pit gain was established. The long short-term memory network (LSTM) of deep learning was used to obtain a formation pressure inversion when the blowout is out of control and the well cannot be shut-in. Experimental data from a well were used to verify the gas-liquid two-phase transient drift flow model based on the finite volume method, demonstrating that this method is reliable, with greatly improved prediction accuracy. This approach provides theoretical support for the early monitoring of gas kick during drilling, and for well-killing design and construction after uncontrolled blowout.

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