| Citation: |
BING Shaoqiang. An Early Warning Method Based on Artificial Intelligence for Wax Deposition in Rod Pumping Wells[J]. Petroleum Drilling Techniques, 2019, 47(4): 97-103. DOI: 10.11911/syztjs.2019093
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The timing of wax clearance is usually determined in the field by observations, which is inaccurate and may cause wells to fail due to wax locking. To solve this problem, a wax deposition early warning method based on artificial intelligence for rod pumping wells has been developed. The Pearson correlation coefficient analysis method is used to conduct correlation analysis between 17 points of data automatically acquired by oil well condition and degree of wax deposition, and seven main control parameters are determined. A wax deposition early warning rule protocol was established on this basis. The wax deposition synthetic characteristics (WPSC) index was obtained by normalizing the merged indexes of seven main control parameters, and the WPSC sample set of wells with paraffin problems was established by using the sample data generated by the wax deposition early warning rule model. Long-term and short-term memory (LSTM) neural networks were selected to train the sample set, and the machine learning model of WPSC is obtained, which could quantitatively predict the degree of wax deposition in the rod pumping wells. The field trial of this method in Block Zhuang 23 of the Shengli Oilfield showed that by using this method, the wax clearance timing as prolonged and the wax locking in failed wells was effectively avoided. Research results showed that the wax deposition early warning method based on artificial intelligence for rod pumping wells achieves quantitative prediction and provides effective early warning on the wax deposition degree in oil wells, and could be used as an effective guide in precise the selection of wax clearance timing.
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Supervisor：SINOPEC GROUP
Sponsor：Sinopec Research Institute of Petroleum Engineering
Serial Number：CN 11-1763/TE, ISSN 1001-0890
Chief Editor： WANG Minsheng
Supported by: Beijing Renhe Information Technology Co., Ltd. 
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