An Analytical Model Coupled With Data Analytics to Estimate PDC Bit Wear

Abstract

Abstract The increasing complexities of wellbore geometry imply an increasing potential of damage resulting from downhole bit wear. Although the locations of critical bit wear can be difficult to predict, the quantification of the actual bit teeth/cutter wear is important to achieve reduced cost per foot and predictable bit failure. There is no acceptable universal mathematical model that describes bit wear accurately because of the complex nature of downhole conditions. Usually, either analytical models or real-time data analytics are used separately to estimate and predict bit wear. Combining both methods and using them simultaneously is an efficient way to address this limitation. This paper presents a new simple analytical bit wear model coupled with data analytics using real-time gamma ray data to suppress the uncertainties of the interacting formation properties and other intervening variables. The fractional bit wear of polycrystalline diamond compact (PDC) bit cutters is obtained from the geometric correlation between height loss and the cutter volume loss. The volume loss of cutters is assumed to be proportional to weight on bit (WOB), cutter sliding distance, rock strength, and rock quartz content. The paper presents a field example to predict and estimate the bit wear using actual data. Gamma ray and rate of penetration (ROP) data of the initial drilling section are used to train the model to quantify the influence of formation strength interaction with the analytical model. Then estimation of ROP using the new bit wear model was carried out using actual field drilling parameters. The calculated ROP profile closely matched with the actual data within reasonable accuracy of less than 5%. Specific procedures are proposed for effective prediction of ROP and bit life.