Investigations into the coiled tubing partial underbalanced drilling (CT-PUBD)technique for drilling hard formations

To improve the rate of penetration(ROP) in drilling deep and hard formations, this paper proposes a new drilling method called coiled tubing partial underbalanced drilling(CT-PUBD). As a preliminary investigation into the new drilling method, this paper presents predictions of hole cleaning efficiency, drilling speed, cuttings migration and pressure loss in the drilling process with CT-PUBD. Based on numerical simulation and full-scale experimental studies, we conclude that using CT-PUBD, an underbalanced drilling condition can be achieved near the bit while maintaining wellbore safety at the same time. This condition can be achieved using a cuttings discharge device, a rotary packer and a backflow controller.According to the numerical simulations performed in this study, CT-PUBD can achieve high efficiency of hole cleaning.Along the cuttings migration process, the fluid velocities can reach the maximum values in the backflow holes. A full-scale laboratory experimental system was used to test the hydraulic characteristics and obtain the drilling performance of the new technology. The result shows that CT-PUBD significantly improves the ROP compared to the conventional drilling method.

Method for predicting cuttings transport using artificial neural networks in foam drilling

Foam is used widely in underbalanced drilling for oil and gas exploration to improve well perfor-mance.Accurate prediction of the cutting transport and pressure loss in the foam drilling is an important way to prevent stuck pipe,lost circulation and to increase the rate of penetration(ROP).In foam drilling,the cuttings transport quality may be defined in terms of cuttings consistency and downhole pressure loss,which are controlled by many factors.Therefore,it is very difficult to establish the mathematical equation that reflects nonlinear relationship among various factors.The field and experimental measurements of these parameters are time consuming and costly.In this study,the authors suggest a cuttings transport mathematical modeling using BPN(back propagation network),RBFN(radial basis function network)and GRNN(general regression neural network)based on various experiment data of cuttings transport of previous researchers and compared the result with experiment data.Results of this study show that the GRNN has a correlation coefficient of 0.99962 and an average error of 0.15 in training datasets,and a correlation coefficient of 0.99881 and an average error of 0.612 in testing datasets,which has higher accuracy and faster training velocity than the BP network or RBFN network.GRNN can be used in many mathematical problems for accurate estimation of cuttings consistency and downhole pressure loss instead of field and experimental measurements for hydraulic design in foam drilling operation.