Oil and gas drilling is a field practice with risks and uncertainties. Uncertainty and ambiguity of formation conditions often cause downhole accidents such as borehole wall instability, stuck drilling, blowout, etc., and also pose a threat to drilling safety.Due to the incorrect understanding of the objective environment and the wrong decision of subjective consciousness; it caused complex underground conditions and serious accidents.

Collapse stuck is the worst kind of accident in stuck stuck. The procedures to deal with this kind of accident are the most complicated, the most time-consuming, the most risky, and even the whole or part of the wellbore may be scrapped, so we should try our best to avoid this accident during the drilling process.Artificial Neural Networks (ANNs for short) is a mathematical model of algorithms that imitate the behavioral characteristics of animal neural networks and perform distributed parallel information processing.

This kind of network depends on the complexity of the system and adjusts the interconnection relationship between a large numbers of internal nodes to achieve the purpose of processing information, and has the ability of self-learning and self-adaptation.

This paper analyzes the causes of collapse stuck, the mechanical mechanism of drilling fluid wettability on the stability of mud shale formation wall.A surface wetting reversal agent added to the drilling fluid system was used to change the wettability of the shale surface.The mechanism analysis and research results of changing the wettability to change the mechanical properties of the shale fracture surface were applied to the actual production of the collapsed drilling rig.Through the change of drilling parameters, the risk of stuck drilling is predicted in advance, the drilling speed is increased, the drilling time loss caused by stuck drilling is reduced, and the drilling cycle and cost are saved.

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