title: Optimization of Operation Parameters for Helical Flow Cleanout with Supercritical CO2 in Horizontal Wells Using Back-Propagation Artificial Neural Network.
abstract: Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells.
authors.affiliation: State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum-Beijing, Changping, Beijing, China.
authors.forename: Xianzhi
authors.initials: X
authors.lastname: Song
authors.affiliation: State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum-Beijing, Changping, Beijing, China.
authors.forename: Chi
authors.initials: C
authors.lastname: Peng
authors.affiliation: State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum-Beijing, Changping, Beijing, China.
authors.forename: Gensheng
authors.initials: G
authors.lastname: Li
authors.affiliation: College of Petroleum Engineering, China University of Petroleum-Beijing, Beijing, China.
authors.forename: Zhenguo
authors.initials: Z
authors.lastname: He
authors.affiliation: College of Petroleum Engineering, China University of Petroleum-Beijing, Beijing, China.
authors.forename: Haizhu
authors.initials: H
authors.lastname: Wang
mesh_terms: D002245:Carbon Dioxide / Q000032:analysis*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D014867:Water
chemical_list: D014867:Water; D002245:Carbon Dioxide