Wellbore-heat-transfer-model-based optimization and control for cooling downhole drilling fluid

• Published in: Petroleum science Vol. 21; no. 3; pp. 1955 - 1968
• Main Authors: Wang, Chao, Liu, He, Yu, Guo-Wei, Yu, Chen, Liu, Xian-Ming, Huang, Peng
• Format: Journal Article
• Language: English
• Published: Beijing Elsevier B.V 01.06.2024; KeAi Publishing Communications Ltd
• Subjects: Algorithms; Analysis; Control; Cooling; Drilling; Drilling fluids; Drillstrings; Energy; Fluids; Genetic algorithms; Heat conductivity; Heat exchangers; Heat transfer; High temperature; Influencing factors; Inlet temperature; Inlets (waterways); Intake temperature; Methods; Monte Carlo simulation; Optimization; Parameters; Physical properties; Proportional integral derivative; Quasi-steady states; Real time; Rotating fluids; Sorting algorithms; Specific heat; Technology assessment; Temperature control; Thermal conductivity; Viscosity; Drilling; Control; Cooling; Influencing factors; Analysis; Optimization
• ISSN: 1995-8226; 1672-5107; 1995-8226
• DOI: 10.1016/j.petsci.2023.11.025

To address the two critical issues of evaluating the necessity of implementing cooling techniques and achieving real-time temperature control of drilling fluids underground in the current drilling fluid cooling technology, we first established a temperature and pressure coupled downhole heat transfer model, which can be used in both water-based and oil-based drilling fluid. Then, fourteen factors, which could affect wellbore temperature, were analyzed. Based on the standard deviation of the downhole temperature corresponding to each influencing factor, the influence of each factor was quantified. The influencing factors that can be used to guide the drilling fluid's cooling technology were drilling fluid thermal conductivity, drilling fluid heat capacity, drilling fluid density, drill strings rotation speed, pump rate, viscosity, ROP, and injection temperature. The nondominated sorting genetic algorithm was used to optimize these six parameters, but the optimization process took 182 min. Combining these eight parameters' influence rules with the nondominated sorting genetic algorithm can reduce the optimization time to 108 s. Theoretically, the downhole temperature has been demonstrated to increase with the inlet temperature increasing linearly under quasi-steady states. Combining this law and PID, the downhole temperature can be controlled, which can reduce the energy for cooling the surface drilling fluid and can ensure the downhole temperature reaches the set value as soon as possible.