# Predictive Analytics for Sucker Rod Pump Failures in Kazakhstan Oil Wells Using Machine Learning
## 6 Conclusions and Future Works  
The research presented demonstrates the effectiveness and challenges of applying machine learning techniques to predict SRP failures in oil wells. The main issue with SRP is rare data and their quality; the number of faults creates new issues, such as imbalanced datasets compared with normal drill hole work. We explored ways to improve prediction accuracy by utilizing methods like SMOTE for oversampling and undersampling to address class imbalance. However, despite these efforts, the results revealed that oversampling andundersampling approaches led to model overestimation of the minority class and did not substantially improve the F1 score compared to models trained on original data.  
The study identified several critical parameters that significantly influence SRP failures, such as wellbore curvature, pump set depth, and flow rate. While our models indicated these key factors, the F1-score, particularly in minority classes, remains challenging (F1 = 0.41). To improve predictive performance, future work will need to focus on refining data collection, increasing the frequency of well operation data, and possibly incorporating more advanced resampling techniques.  
According to the results of studies on SRP wells, the authors plan to carry out work on adapting the model for ESP wells in the Kazakhstan field, since ESP wells are equipped with sensors that record the ESP performance indicators in real time and generate a huge array of data. In addition, a possible change of the pilot field with a large amount of data and the failure rate of downhole pumping equipment will be considered to evaluate the constructed model for predicting SRP well failures; when changing the pilot field, the issue of expanding the database on the composition of the extracted liquid will also be considered. The issue of changing the pilot field of SRP wells will be considered based on the results of the failure prediction model for ESP wells.  
Conceptualization, L.U. and T.M.; methodology, T.M.; validation, L.U. and B.B.; formal analysis, B.B. and S.O.; resources, L.U.; data curation, L.U. and [PERSON]; writing--original draft preparation, L.U. and [PERSON]; writing--review and editing, S.O. and B.B.; supervision, B.B.; project administration, L.U. and T.M.; funding acquisition, B.B. and S.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP23483887).  
The data presented in this study are available on request from the corresponding author due to privacy.  
Authors [PERSON] and [PERSON] was employed by the company KMG Engineering LLP. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SRP & Sucker rod pump \\ ESP & Electrical submersible pump \\ SMOTE & Synthetic Minority Oversampling Technique \\ LightGBM & Light Gradient-Boosting Machine \\ ROS & Random Oversampling \\ SHAP & SHapley Additive exPlanations \\ \end{tabular}