Data-driven wireline sticking risk assessment and control factor analysis

Abstract

The risk of wireline sticking cannot be accurately predicted at wireline logging site. Once the wireline is sticked on the wellbore, it seriously affects the logging process. A data-driven risk assessment method has been proposed to quantitatively predict and qualitatively evaluate the probability of wireline sticking accidents. Firstly, based on the data collection capability and expert experience, the risk characterization parameters of wireline sticking were determined. Secondly, according to the operating conditions of wireline logging, the variable length window data acquisition method and risk calibration were established, and the data feature extraction and feature selection methods were established. Thirdly, 149 logging operation records were collected to establish training data, and synthetic minority over-sampling technique (SMOTE) algorithm were used to expand the number of training data. Finally, a probability prediction model based on co-training-style random forest (Co-Forest) was established to quantitatively predict the probability of cable jamming. And the critical risk probability value is observed by fuzzy clustering to define the risk level. The performance of the proposed method was verified by logging operation records from an oilfield from 2019 to 2022, and the prediction accuracy was more than 90%. The results show that the model has good guiding significance and practicability for realizing intelligent wireline logging operation safety warning. The proposed method can be used to determine the depth of wireline logging operations and avoid wireline sticking.