A data-driven method for total organic carbon prediction based on random forests

被引:1
|
作者
Gui, Jinyong [1 ]
Gao, Jianhu [1 ]
Li, Shengjun [1 ]
Li, Hailiang [1 ]
Liu, Bingyang [1 ]
Guo, Xin [1 ]
机构
[1] PetroChina, Res Inst Petr Explorat & Dev Northwest, Lanzhou, Peoples R China
关键词
total organic carbon; random forests; extended variables; imbalance data; data-driven; SMOTE;
D O I
10.3389/feart.2023.1238121
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The total organic carbon (TOC) is an important parameter for shale gas reservoir exploration. Currently, predicting TOC using seismic elastic properties is challenging and of great uncertainty. The inverse relationship, which acts as a bridge between TOC and elastic properties, is required to be established correctly. Machine learning especially for Random Forests (RF) provides a new potential. The RF-based supervised method is limited in the prediction of TOC because it requires large amounts of feature variables and is very onerous and experience-dependent to derive effective feature variables from real seismic data. To address this issue, we propose to use the extended elastic impedance to automatically generate 222 extended elastic properties as the feature variables for RF predictor training. In addition, the synthetic minority oversampling technique is used to overcome the problem of RF training with imbalanced samples. With the help of variable importance measures, the feature variables that are important for TOC prediction can be preferentially selected and the redundancy of the input data can be reduced. The RF predictor is finally trained well for TOC prediction. The method is applied to a real dataset acquired over a shale gas study area located in southwest China. Examples illustrate the role of extended variables on improving TOC prediction and increasing the generalization of RF in prediction of other petrophysical properties.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Data-Driven Prognostics Using Random Forests: Prediction of Tool Wear
    Wu, Dazhong
    Jennings, Connor
    Terpenny, Janis
    Gao, Robert
    Kumara, Soundar
    [J]. PROCEEDINGS OF THE ASME 12TH INTERNATIONAL MANUFACTURING SCIENCE AND ENGINEERING CONFERENCE - 2017, VOL 3, 2017,
  • [2] Research and Implementation of a Carbon Emission Prediction Method Based on Electricity Data-Driven Approach
    Xu, Lianjie
    Pan, Xuewen
    Zhang, Guangya
    Zhang, Renbiao
    Chu, Bei
    Yu, Zhilin
    Zhang, Heng
    Wei, Minjun
    [J]. 2024 6TH ASIA ENERGY AND ELECTRICAL ENGINEERING SYMPOSIUM, AEEES 2024, 2024, : 1204 - 1209
  • [3] Casing Damage Prediction Model Based on the Data-Driven Method
    Tan, Chaodong
    Yan, Wei
    Tang, Qing
    Wu, Hua
    Bu, Hongguang
    Kambi, Said Juma
    Liu, Jiankang
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2020, 2020
  • [4] dmPINNs: An Integrated Data-Driven and Mechanism-Based Method for Endpoint Carbon Prediction in BOF
    Xia, Yijie
    Wang, Hongbing
    Xu, Anjun
    [J]. METALS, 2024, 14 (08)
  • [5] Driving Maneuvers Prediction Based on Cognition-driven and Data-driven Method
    Zhou, Dong
    Ma, Huimin
    Dong, Yuhan
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (IEEE VCIP), 2018,
  • [6] An Improved Data-Driven Modeling Method for Aircraft Based on Prediction and Optimization
    Su, Shihong
    Xiao, Bing
    Li, Lingwei
    Luo, Jinfeng
    Zhao, Hui
    [J]. 2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2023, : 2560 - 2565
  • [7] Data-driven Fatigue Life Prediction Method Based on the Influence of Parameters
    Liu, Zhizhuang
    Wu, Hao
    [J]. Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2023, 59 (04): : 71 - 79
  • [8] Data-driven disruption prediction using random forest in KSTAR
    Lee, Jeongwon
    Kim, Jayhyun
    Hahn, Sang-hee
    Han, Hyunsun
    Shin, Giwook
    Kim, Woong-Chae
    Yoon, Si-Woo
    [J]. FUSION ENGINEERING AND DESIGN, 2024, 199
  • [9] A Residual Voltage Data-Driven Prediction Method for Voltage Sag Based on Data Fusion
    Zheng, Chen
    Dai, Shuangyin
    Zhang, Bo
    Li, Qionglin
    Liu, Shuming
    Tang, Yuzheng
    Wang, Yi
    Wu, Yifan
    Zhang, Yi
    [J]. SYMMETRY-BASEL, 2022, 14 (06):
  • [10] Data-driven modelling of the Reynolds stress tensor using random forests with invariance
    Kaandorp, Mikael L. A.
    Dwight, Richard P.
    [J]. COMPUTERS & FLUIDS, 2020, 202 (202)