Real Time Automation of Cutting Carrying Capacity Index to Predict Hole Cleaning Efficiency and Thereby Improve Well Drilling Performance

Abstract

Abstract Hole cleaning efficiency is one of the major factors that affects well drilling performance. Rate of penetration (ROP) is highly dependent on hole cleaning efficiency. Hole cleaning performance can be monitored in real-time in order to make sure drilled cuttings generated are efficiently transported to surface. The objective of this paper to present a real time automated model to obtain hole cleaning efficiency and thus effectively adjust parameters as required to improve drilling performance. The process adopts a modified real time carrying capacity indicator. There are many hole cleaning models, methodologies, chemicals and correlations, but majority of these models do not simulate drilling operations sequences and are not dependent on practicality of drilling operations. The developed real time hole cleaning indicator can ensure continuous monitoring and evaluation of hole cleaning performance during drilling operations. The methodology of real time model development is by selecting offset mechanical drilling parameters and drilling fluid parameters where collected, analyzed, tested and validated to model strong hole cleaning efficiency indicator that can extremely participate and facilitate a position in drilling automations and fourth industry revolution. The automated hole cleaning model is utilizing real time sensors of drilling and validate the strongest relationships among the variables. The study, analysis, test and validation of the relationships will reveal the significant parameters that will contribute massively for model development procedures. The model can be run as well by using the real time sensors readings and their inputs to be fed into the developed automated model. The developed model of real time carrying capacity indicator profile will be shown as function of depth, drilling fluid density, flow rate of mud pump or mud pump output, and other important factors will be illustrated by details. The model has been developed and validated in the field of drilling operations to empower the drilling teams for better and understandable monitoring and evaluation of hole cleaning efficiency while performing drilling operations. The real time model can provide a vision for better control of mud additives and that will contribute to mud cost effectiveness. The automated model of hole cleaning efficiency optimized the rate of penetration (ROP) by 50% in well drilling performance as a noticeable and valuable improvement. This optimum improvement saved cost and time of rig and drilling of wells and contributed to accelerate wells’ delivery. The innovative real time model was developed to optimize drilling and operations efficiency by using the surface rig sensors and interpret the downhole measurements and that can lead innovatively to other important hole cleaning indicators and other tactics for better development of downhole measurements models that can participate for optimized drilling efficiency.