Advisory Automated System for Wellbore Cleaning and Evaluation Improves Drilling Efficiency

Abstract

Abstract Ineffective hole cleaning leads to a complicated drilling hole problems such as stuck pipe incidents, and difficult tripping operations. The objective of this paper to introduce a unique Hole Cleaning Advisory System which is showing six real time hole cleaning indicators that can provide an obvious vision on hole cleaning performance during drilling operations and thereby improves drilling efficiency. They are real time drilling fluid density (MWeff), real time equivalent circulating density (ECDeff), real time developed carrying capacity index (HCI), and real time developed cuttings concentration in annulus (CCA), real time developed transport ratio (CTRm) and real time transport index (TIm). Artificial intelligence tool such as artificial neural network (ANN) was applied for validation and confirmation of models parameters and obtaining similar real time profiles of hole cleaning indices. The system was utilizing rig sensors’ real time values, surface and field operations readings by collecting historical and real time readings, selecting the most relevant and important factors. Performing data analytics, preparation and interpretation. Finding the relationships between parameters. Modifying critical parameters. Validating models with referenced tools those are applicable and utilized in drilling operations. Integrating developed models in real time drilling operations to evaluate and monitor drilling performance. The conducted process, validations of real time models were done and compared with other commercial software and advanced drilling tool such as Pressure while drilling (PWD) tool and other sensors tools provided by Services Company for drilling operations. The system were applied in the fields and provided a firm and solid validations of the developed system that empowered the new technology to be applied in drilling rigs fleet. Results, Observations, Conclusions: the results showed that the MWeff and ECDeff were validated with pressure while drilling (PWD) and resulted in 2 % errors .MWeff was validated with other commercial software shows real time mud as output, the developed real time model of mud weight while drilling showed errors 2 %. That means the developed models for mud weight and equivalent circulating density were robust and can be utilize while drilling operation to optimize well drilling performance. regarding other hole cleaning indices such as HCI, CCA, CTRm & TIm were applied in several types of well, it was able to identify the hole cleaning issues and improves the drilling efficiency specifically rate of penetration (ROP) by 65 %. The drilling engineering and operation team was performing immediate intervention to avoid drilling troubles due to hole cleaning. The final results contributed for optimizing drilling efficiency significantly in safety and environmental manners with cost and time effectiveness. In addition, participating in drilling automation and digitalization in compatibility with fourth industrial revolution (4-IR).