Optimized parameters of downhole all-metal PDM based on genetic algorithm
), Yu Wanga,b(), Abstract
Currently, deep drilling operates under extreme conditions of high temperature and high pressure, demanding more from subterranean power motors. The all-metal positive displacement motor, known for its robust performance, is a critical choice for such drilling. The dimensions of the PDM are crucial for its performance output. To enhance this, optimization of the motor's profile using a genetic algorithm has been undertaken. The design process begins with the computation of the initial stator and rotor curves based on the equations for a screw cycloid. These curves are then refined using the least squares method for a precise fit. Following this, the PDM's mathematical model is optimized, and motor friction is assessed. The genetic algorithm process involves encoding variations and managing crossovers to optimize objective functions, including the isometric radius coefficient, eccentricity distance parameter, overflow area, and maximum slip speed. This optimization yields the ideal profile parameters that enhance the motor's output. Comparative analyses of the initial and optimized output characteristics were conducted, focusing on the effects of the isometric radius coefficient and overflow area on the motor's performance. Results indicate that the optimized motor's overflow area increased by 6.9%, while its rotational speed reduced by 6.58%. The torque, as tested by Infocus, saw substantial improvements of 38.8%. This optimization provides a theoretical foundation for improving the output characteristics of all-metal PDMs and supports the ongoing development and research of PDM technology.
Keywords
References
Bani Mustafa, A., Abbas, A.K., Alsaba, M., 2021. Improving drilling performance through optimizing controllable drilling parameters. Journal of Petroleum Exploration and Production 11, 1223–1232. https://doi.org/10.1007/s13202-021-01116-2.
Baskal, T., 2014. Optimization of screw elements by genetic algorithm. Mater. Test. 56, 1049–1053. https://doi.org/https://doi.org/10.3139/120.110664.
Beauquin, J.L., Boireau, C., Lemay, L., 2005. Development status of a metal progressing cavity pump for heavy oil and hot production wells. OnePetro. https://doi.org/10.2118/0506-0059-JPT.
Cao, G., Zhang, J., Guo, Y., et al., 2022. Numerical modeling on friction and wear behaviors of all-metal progressive cavity pump. J. Petrol. Sci. Eng. 213, 110443. https://doi.org/10.1016/j.petrol.2022.110443.
Chaturvedi, E., Acar, P., Sandu, C., 2023. Multi-objective macrogeometry optimization of gears: comparison between sequential quadratic programing and genetic algorithm. Mech. Base. Des. Struct. Mach. 51, 97–112. https://doi.org/10.1080/15397734.2022.2146710.
El-Abd, F.M., Wahba, E.M., Adam, I.G., 2020. Viscous flow simulations through multi-lobe progressive cavity pumps. Petrol. Sci. 17, 768–780. https://doi.org/10.1007/s12182-020-00458-6.
Guo, C., Tang, Y., 2003. Influence of process parameters on screw rotor profiles. Mach. Sci. Technol. 7, 105–118. https://doi.org/10.1081/MST-120018957.
Isaev, A.A., Aliev, M.M.O., Drozdov, A.N., et al., 2022. Improving the efficiency of curved wells' operation by means of progressive cavity pumps. Energies 15 (12), 4259. https://doi.org/10.3390/en15124259.
Lehman, M., 2004. Progressing cavity pumps in oil and gas production. World Pumps 20–22. https://doi.org/10.1016/S0262-1762(04)00356-6.
Li, M., Jiang, D., Guo, H., 2017. Study on clearance optimization of all-metal screw pumps: experiment and simulation. Study on Clearance Optimization of All-metal Screw Pumps Mechanics 23, 735–742. https://doi.org/10.5755/j01.mech.23.5.15387.
Liang, C., Wang, Y., Peng, Z., 2023. Influence of surface roughness of stator on dry sliding behavior against rotor in pneumatic downhole motor. J. Mater. Eng. Perform. 32, 2392–2407. https://doi.org/10.1007/s11665-022-07254-w.
Lu, J., Wang, Y., Kong, L., 2023. Analysis of output performance of all-metal progressive cavity motor. Geoenergy Science and Engineering 222, 211456. https://doi.org/10.1016/j.geoen.2023.211456.
Nguyen, K., Nguyen, T.C., Al-Safran, E., 2021. Modeling the performance of progressive cavity pump under downhole conditions. J. Petrol. Sci. Eng. 198, 108121. https://doi.org/10.1016/j.petrol.2020.108121.
Nguyen, T., Al-Safran, E., Saasen, A., et al., 2014. Modeling the design and performance of progressing cavity pump using 3-d vector approach. J. Petrol. Sci. Eng. 122, 180–186. https://doi.org/10.1016/j.petrol.2014.07.009.
Nguyen, T.C., Al-Safran, E., Nguyen, V., 2018. Theoretical modeling of positive displacement motors performance. J. Petrol. Sci. Eng. 166, 188–197. https://doi.org/10.1016/j.petrol.2018.03.04.9.
Rong, X., Zhu, H., Hu, B., 2021. Performance research and structure optimization of labyrinth screw pump. Micromachines 12 (7), 790. https://doi.org/10.3390/mi12070790.
Samuel, G.R., Saveth, K.J., 2006. Optimal design of progressing cavity pumps (pcp). Journal of Energy Resources Technology-transactions of the Asme 128, 275–279. https://doi.org/10.4115/1.2358142.
Tschirky, J.E., 1978. New developments in down-hole motors for improved drilling performance. J. Petrol. Technol. 30, 993–997. https://doi.org/10.2118/6778-PA.
Wan, B.L., 1993. Single Screw Hydraulic Machinery. University of Petroleum Press (in Chinese).
Wang, J., Pan, S., Dong, L., et al., 2022. Modelling of a novel progressive cavity pump with a special internal compression process used in oil wells. J. Petrol. Sci. Eng. 217, 110889. https://doi.org/10.1016/j.petrol.2022.110889.
Wang, Y., Yu, X., Han, X., et al., 2021. Influences of eccentricity ratio on the internal flow and cavitation characteristics of progressing cavity pump. Proc. IME C J. Mech. Eng. Sci. 235, 6111–6121. https://doi.org/10.1177/0954406221998401.
Whitley, D., 1994. A genetic algorithm tutorial. Stat. Comput. 4, 65-85 https://link.springer.com/article/10.1007/BF00175354.
Zhang, D., Guo, P., Hu, Q., et al., 2022. Parametric study and multi-objective optimization of a ductless archimedes screw hydrokinetic turbine: experimental and numerical investigation. Energy Convers. Manag. 273, 116423. https://doi.org/10.1016/j.enconman.2022.116423.
京公网安备11010802044758号