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Effectively preventing pipe sticking in oil and gas wells is a key aspect of safety and productivity in the oil and gas industry. This paper presents a new torque and drag model developed using machine learning techniques to improve accuracy and predictive capabilities. The model is based on a comprehensive analysis of many factors, including geological characteristics of the well, drilling parameters, fluid parameters, hydraulic conditions, and production equipment parameters. This research explores the applicati...

This study addresses the significant challenge of hole cleaning in drilling operations, which is essential for preventing stuck pipe incidents-a major cause of non-productive time and additional costs in drilling. This research aims to develop and validate machine learning models that enhance the prediction and optimization of cuttings removal during drilling. Utilizing a dataset derived from historical drilling operations, we employed regression analysis and neural network models to forecast the presence and heig...

The drilling muds parameters are playing a major role in drilling technology and particularly in oil and gas drilling technology. At present those parameters are measured manually at long and irregular time intervals. In Satpajev university a general method of the existing manual measurements automatization has been worked. A number of patents on appliances for automatic continuous measuring the drilling mud funnel viscosity, density, jell stress and other parameters was obtained. Engineering development on creati...