Fluid Loss Control in Smart Bentonite Drilling Mud Modified with Nanoclay and Quantified with Vipulanandan Fluid Loss Model

Abstract In order to enhance the performance and sensing properties of the water based bentonite drilling mud hydrophilic bentonite based nanoclay was used. The bentonite content in the drilling muds were varied from 2% to 8% by the weight of water and temperature was varied from 25°C to 85°C. The nanoclay (particle size in range of 12 nm to 20 nm) content was varied up to 1% by the weight of the drilling mud to modify the rheological properties, enhance the sensing electrical resistivity and reducing the fluid loss of the drilling mud. The fluid loss experiments were performed on bentonite drilling muds modified with nanoclay at 100 psi up to about 420 minutes till the end of the fluid loss. Based on the experimental and analytical study the electrical resistivity was identified as the sensing property of the smart drilling mud so that the changes in the properties can be monitored in real-time during construction. Addition of 1% nanoclay to 8% bentonite drilling mud reduced a fluid loss to zero at 25°C. The results also showed that 1% nanoclay decreased the electrical resistivity of the drilling mud from 15% to 36% based on the bentonite content in the drilling mud and temperature. Compared to the Vocadlo model, Vipulanandan rheological model better predicted the shear stress- shear strain rate relationships all the drilling muds investigated in this study. In all cases, except for the 8% bentonite drilling mud at 25°C, adding 1% nanoclay more than doubled the yield stresses and the maximum shear stress tolerances. The rheological properties of the drilling muds have been correlated to the electrical resistivity of the drilling mud using the Vipulanandan correlation model. The API fluid loss model was compared to the new kinetic Vipulanandan fluid loss model in predicting the experimental results for fluid loss. Compared to the API fluid loss model, Vipulanandan fluid loss model was effective, based on the lower value of root mean square error value, in predicting the short-term and longterm fluid loss with time, nanoclay content and temperature. The new model predicted the short-term and long-term fluid losses very well. This model also has a limit on the total fluid loss but the API model doesn't have a limit.