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A risk prediction method of pipe sticking accidents due to wellbore uncleanness for long horizontal section wells
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NSTL
Elsevier
The prediction of pipe sticking accidents due to wellbore uncleanness is significant for safe drilling, while existing qualitative prediction methods result in inaccuracy and lateness. To cover the shortages, a novel quantitative pipe sticking prediction method is established to achieve pipe sticking prediction early in drilling preparatory stage. First, the additional axial forces and torques on the drill string exerted by accumulated cuttings are calculated in tripping out, tripping in, back reaming and reaming processes based on granular mechanics. Second, the torque and drag model is modified considering additional axial forces and torques to calculate the hook load and the top drive torque. Finally, the pipe sticking prediction is conducted according to the critical hook load and the critical top drive torque in a specific drilling process. A case study including comparison with the classical model and sensitivity analysis is conducted. Under the given condition, pipe sticking risk is predicted with the new mothed by anticipating hook loads (5524.67 and -667.71 kN) and top drive torques (60.87 and 66.36 kN m) exceeding the critical values. The hook load and the top drive torque change exponentially with the travelling distance. In tripping out and back reaming processes from 4350 to 4250 m, the hook load and the top drive torque respectively increase for 1894.36 kN and 53.99 kN m. In tripping in and reaming processes from 3450 to 3550 m, the hook load decreases for 29925.66 kN and the top drive torque increases for 157.3 kN m. According to the sensitivity analysis, low frictional coefficients, initial cutting volume fractions, torque and drag frictional factors and high junk slot ratios are required to reduce the risk of pipe sticking. This study provides a timely and quantitative prediction method for pipe sticking accidents due to wellbore uncleanness in long horizontal drilling.
NSTL
Elsevier
