Special Oil & Gas Reservoirs >

2024 , Vol. 31 >Issue 5: 11 - 19

DOI: https://doi.org/10.3969/j.issn.1006-6535.2024.05.002

Geologic Exploration

Identification of Natural Gas Hydrates and Natural Gas Reservoirs Based on SMOTE and XGBoost

  • Du Ruishan ,
  • Huang Yupeng ,
  • Fu Xiaofei ,
  • Meng Lingdong ,
  • Zhang Yi'nan ,
  • Jin Mingyang ,
  • Cai Hongbo
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  • 1. Northeast Petroleum University,Daqing,Heilongjiang 163318,China;
    2. Key Laboratory for Evaluation of Oil and Gas Reservoir and Underground Storage Integrity in Heilongjiang Province,Daqing,Heilongjiang 163318,China;
    3. PetroChina Liaohe Oilfield Company,Panjin,Liaoning 124010,China

Received date: 2023-09-12

  Revised date: 2024-07-15

  Online published: 2024-12-24

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Abstract

Natural gas hydrates identification and characterization are the key tasks throughout the exploration and development phase of marine energy resources.However,due to the complex nonlinear relationship between logging data and reservoirs,as well as the imbalance of logging data, traditional reservoir identification methods often show low accuracy,which severely limited the progress of energy exploration in the study area.To address the above issues,a composite method for reservoir identification is proposed.The improved SMOTE algorithm is used to increase the number of minority class reservoir samples and denoise the data,which effectively solves the issues of data imbalance.The XGBoost algorithm is then used to identify reservoirs.The results show that compared with traditional machine learning method,the RLSMOTE-XGB method has higher effectiveness and accuracy in reservoir identification.This method addresses the limitations of traditional machine learning methods in the case of imbalanced sample classes,increasing the reservoir identification accuracy from 66.7% to 86.4% and significantly improving the algorithm′s performance.This study can effectively improve the identification effect of natural gas hydrates and natural gas reservoirs,which is of great significance for achieving intelligent reservoir identification.

Key words： reservoir identification; SMOTE; machine learning; RLSMOTE-XGB; outlier detection algorithm

Cite this article

Du Ruishan , Huang Yupeng , Fu Xiaofei , Meng Lingdong , Zhang Yi'nan , Jin Mingyang , Cai Hongbo . Identification of Natural Gas Hydrates and Natural Gas Reservoirs Based on SMOTE and XGBoost[J]. Special Oil & Gas Reservoirs, 2024 , 31(5) : 11 -19 . DOI: 10.3969/j.issn.1006-6535.2024.05.002

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