TY - GEN
T1 - Research of Gas Wells Sand-Carrying Prediction and Sand-Control Production in South Sichuan Gas Areas
AU - Wan, Huaxu
AU - Xiao, Fan
AU - Sun, Fengjing
AU - Huang, Jing
AU - Bai, Bofeng
AU - Zhao, Kunpeng
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Shale gas development has become a global energy hotspot. After large-volume fracturing of shale gas reservoirs, sand production in wellbores has become a frequent phenomenon. To address the sand plugging problem of shale gas wellbores, a wellbore gas-liquid sand-carrying experiment was conducted, and the three-phase flow characteristics of gas, liquid, and sand in horizontal wells were investigated using CFD to analyze the influences of the main factors on the gas-liquid sand-carrying capacity. Within the range of the experimental parameters, the sand-carrying capacity of the wellbore increases with the increase of the gas production rate, and with the increase of the pressure gradient of the wellbore, water-producing gas wells have a stronger sand carrying capacity than non-water-producing gas wells. Based on extensive experimental data, a prediction model for the sand-carrying ratio by gas–liquid flow within wellbores was developed using a backpropagation (BP) neural network algorithm, the model takes gas production rate, liquid production rate, and wellhead pressure as inputs, and outputs the sand-carrying ratio (ε), which characterizes the sand-carrying capacity and risk of sand accumulation in wellbore. Based on the sand-carrying ratio prediction model, a production chart and a production optimization method for sand-control is proposed to support shale gas production.
AB - Shale gas development has become a global energy hotspot. After large-volume fracturing of shale gas reservoirs, sand production in wellbores has become a frequent phenomenon. To address the sand plugging problem of shale gas wellbores, a wellbore gas-liquid sand-carrying experiment was conducted, and the three-phase flow characteristics of gas, liquid, and sand in horizontal wells were investigated using CFD to analyze the influences of the main factors on the gas-liquid sand-carrying capacity. Within the range of the experimental parameters, the sand-carrying capacity of the wellbore increases with the increase of the gas production rate, and with the increase of the pressure gradient of the wellbore, water-producing gas wells have a stronger sand carrying capacity than non-water-producing gas wells. Based on extensive experimental data, a prediction model for the sand-carrying ratio by gas–liquid flow within wellbores was developed using a backpropagation (BP) neural network algorithm, the model takes gas production rate, liquid production rate, and wellhead pressure as inputs, and outputs the sand-carrying ratio (ε), which characterizes the sand-carrying capacity and risk of sand accumulation in wellbore. Based on the sand-carrying ratio prediction model, a production chart and a production optimization method for sand-control is proposed to support shale gas production.
KW - (BP) neural network
KW - gas-liquid-solid three-phase flow
KW - sand control production
KW - sand-carrying ratio
KW - Shale gas
UR - https://www.scopus.com/pages/publications/105041724630
U2 - 10.1007/978-3-032-18532-7_75
DO - 10.1007/978-3-032-18532-7_75
M3 - 会议稿件
AN - SCOPUS:105041724630
SN - 9783032185310
T3 - Springer Series in Geomechanics and Geoengineering
SP - 750
EP - 757
BT - Geomechanics in Energy, Geology and Oil-Gas Exploration - Proceedings of the 2025 International Conference on Geology, Energy and Oil and Gas Exploration GEOGE 2025
A2 - Khandelwal, Manoj
A2 - Liu, Chenglin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Geology, Energy and Oil and Gas Exploration, GEOGE 2025
Y2 - 19 September 2025 through 21 September 2025
ER -