HOLE CLEANING OPTIMISATION MODEL Osvaldo Tchissola-1009243 Gordon Botterill MSc Drilling and Well Engineering BACKGROUND Hole cleaning represent a major challenge in directional and deviated wells, when improperly managed it can lead to severe hole problems such as mechanical pipe sticking , poor cementing, steering problems, formation fractures, excessive torque and drag. Throughout years, a lot of research has been done to the determine the causes of poor hole cleaning and significant improvement had been achieved. However real time optimisation still needs further improvements to reduce the amount of time spent on wiper trips, or reducing cost by early detection of poor hole cleaning that could lead to NPT. This project attempts to create a real time hole cleaning optimisation model that analysis the predicted downhole conditions, and then determines the required parameters to efficiently clean the hole. Later on during drilling operations the models tracks well data in real time to check the amount of cuttings removed from the hole. Thus allowing in the early stage detecting the lagging amount of cuttings. Aim: The aim of this project is to develop an integrated hole cleaning model that can be used to optimise hole cleaning during drilling operations. It will anticipate the cuttings concentration in the well and estimate the minimum parameters to reduce cuttings in the annulus. Then, during drilling, the model receives real-time data to check if the well is being cleaned properly. Objectives: • Gain a high level of understanding of cuttings transport in deviated wells. • Analyse how several factors impact cuttings transport. • Understand how poor hole cleaning can be detected during drilling operations. • Estimate drilling parameters for optimum hole cleaning. • Develop awareness of good drilling practices for good hole cleaning. CONCLUSION AND FURTHER WORK Conclusion The model was built based on several equations developed through laboratory experiments. Based on the current work the model is capable of producing the desired results, and illustrate them graphically. These can then be used to monitor hole cleaning performance during drilling operations. However there is still the need to validate the application of the model with field data, and benchmark with other industry models. Future Work • Field application to test the accuracy of the model; • Fully integration into computer package to work with rig instrumentation; • Further laboratory experiment to prove the application of all the concepts presented into the project. REFERENCES 1. Cayeux, E., Mesagan, T., Tanripada, S., Zidan, M., & Fjelde, K. K. . Real-Time Evaluation of Hole-Cleaning Conditions With a Transient Cuttings-Transport Model. Society of Petroleum Engineers 2014; (SPE163492): . doi:10.2118/163492-PA (accessed June 2015). 2. K&M, Extended Reach Engineering Design and Implementation Course-Complex Directional Wells.. [Training Material]. K&M Technology Group. October 17, 2011. Figure 2. Forces acting on a suspended cutting.(1) Figure3. Cuttings transport at various angle.(2) METHODOLOGY The model is made up three major modules that are related to the different stages of the hole cleaning. The first stage is the design stage (determination of flow rate, pipe rotation, ECD, SPP, Torque and Drag, and Cuttings volume), the second stage is the real-time data(comparing the real time data with the estimated values), and the final stage is the decision making where actions are taken when the real data and the estimated do not match. The steps to create the model are: • Literature review on the current work of hole cleaning; • Literature review of models of cuttings concentration in the well; • Review of hydraulic optimisation for hole cleaning; • Analysis of the factors that affect hole cleaning; • Review of the indicators of poor hole cleaning during drilling; • Build the concept of the model • Interaction of data from the major areas of the model; • Assessment of the results of the model and conclusion. Figure4. Annular cross sectional view of a high angle well(65°+). (2) Figure 1. Hole cleaning optimisation model