Visualization Study of Oil Well Fracturing Production Prediction Model Based on Weighted Hybrid Regression Algorithm

DOI: https://doi.org/10.62517/jiem.202403210

Author(s)

Chunwei Deng, Huanqing Shi

Affiliation(s)

Harbin Institute of Petroleum, Harbin, Heilongjiang, China

Abstract

This study aims to explore visualization methods for an oil well fracturing production prediction model based on a weighted hybrid regression algorithm. The objective is to construct a mathematical model capable of accurately predicting oil well fracturing production through theoretical analysis and to enhance the model's interpretability and applicability using visualization techniques. The research primarily employs a weighted hybrid regression algorithm, which combines the advantages of multiple regression and weighted least squares to effectively address heteroscedasticity and multicollinearity issues in the data. Initially, a systematic analysis of factors influencing oil well fracturing production, such as geological conditions, fracturing parameters, and well structure, is conducted. Based on these factors, a weighted hybrid regression model is constructed. Subsequently, mathematical derivation and simulation experiments are used to validate the model's predictive accuracy and stability. Finally, visualization techniques, such as 3D graphics and dynamic simulation, are utilized to present the model’s prediction results and trends, enhancing the model’s intuitiveness and comprehensibility. The study concludes that the weighted hybrid regression algorithm demonstrates high accuracy and robustness in predicting oil well fracturing production, and visualization techniques effectively assist decision-makers and engineers in understanding and managing the oil well production process.

Keywords

Weighted Hybrid Regression Algorithm; Oil Well Fracturing Production Prediction; Visualization Techniques; Mathematical Model; Theoretical Analysis

References

[1] Fan Yu, Huang Haoyong, Wang Xinghao, Chen Juan, Deng Youchao. Research on Shale Gas Horizontal Well Production Prediction Technology Based on Data Mining[C]//31st National Natural Gas Academic Annual Conference (2019).0[2024-07-16]. [2] Song Xuanyi, Liu Yuetian, Ma Jing, et al. Production Capacity Prediction Based on Grey Wolf Algorithm Optimized Support Vector Machine[J]. Lithologic Reservoirs, 2020, 32(2):7. DOI:10.12108/yxyqc.20200215. [3] Liu Xinping, Deng Jie, Yang Penglei. Research on Fracturing Production Capacity Prediction of Tight Reservoir Horizontal Wells Based on K-means and SVR[J]. Computer and Digital Engineering, 2023, 51(9):1949-1953. [4] Li Juhua, Chen Chen, Xiao Jialin. Production Prediction of Multi-stage Fractured Shale Gas Wells Based on Random Forest Algorithm[J]. Journal of Yangtze University: Natural Science Edition, 2020, 17(4):6. [5] Zhang Jinshui, Tian Leng, Huang Shihui, et al. Prediction of Tight Oil Recovery Based on Extreme Gradient Boosting Algorithm and Support Vector Regression Algorithm Variable Weight Combination Model[J]. Science and Technology and Engineering (022-012)[2024-07-16]. [6] Deng Rui. Research on SD Gas Field Reserves and Production Prediction Algorithm Based on Machine Learning[D]. Chengdu University of Technology[2024-07-16]. [7] Wu Da, Liu Yang, Luo Pengfei, et al. Optimization of Gas Production Conditions for Electrolytic Treatment of Fracturing Flowback Fluid[J]. Journal of Xi'an Shiyou University: Natural Science Edition, 2020, 35(1):8. DOI: CNKI:SUN:XASY.0.2020-01-013. [8] Luo Pengfei, Liu Yang, Wu Da, et al. Research on Process Conditions for Gas Production During Electrochemical Treatment of Fracturing Flowback Fluid[J]. Applied Chemistry, 2019. DOI:10.16581/j.cnki.issn1671-3206.20190523.098. [9] Xue Zhao. Single Well Production Prediction of Low Permeability Oil Fields Based on IWOA-RVM Model[J]. World Petroleum Industry, 2022(002):029. [10] Ma Xianlin, Fan Yilong. Fracturing Vertical Well Production Capacity Prediction Model Based on Machine Learning[J]. Practice and Cognition of Mathematics, 2021, 51(19):11. [11] Min Chao, Zhang Xinhui, Yang Zhaozhong, et al. Identification of Main Controlling Factors of Fracturing Effect in Coalbed Methane Wells Based on CBFS-CV Algorithm[J]. Oil and Gas Geology and Recovery, 2022, 29(1):7. [12] Pan Yuan, Wang Yonghui, Che Mingguang, et al. Prediction of Post-fracturing Production Capacity of Horizontal Wells and Optimization of Fracturing Parameters Based on Grey Relational Projection Random Forest Algorithm[J]. Journal of Xi'an Shiyou University: Natural Science Edition, 2021, 36(5):6. DOI:10.3969/j.issn.1673-064X.2021.05.009. [13] Wang Jian. Research on Seismic Source Location Accuracy and Geophone Network Optimization Design Based on Microseismic Monitoring of Well Fracturing[D]. Jilin University, 2012. [14] She Gang, Ye Zhihong, Zhang Chengen, et al. Evaluation of Key Parameters of Gas-bearing Mud Shale Reservoirs in Badaoshan Basin, Qinghai East Kunlun Plateau[J]. Journal of Yangtze University (Natural Science Edition), 2024, 21(1):38-48.