Logging evaluation of favorable areas of a low porosity and permeability sandy conglomerate reservoir based on machine learning

Jiang, Yanjiao; Zhou, Jian; Song, Yanjie; Song, Lijun; Guo, Zhihua; Shen, Peng

doi:10.1007/s11600-023-01130-4

Artykuł - szczegóły

Tytuł artykułu

Logging evaluation of favorable areas of a low porosity and permeability sandy conglomerate reservoir based on machine learning

Autorzy

Jiang Yanjiao , Zhou Jian , Song Yanjie , Song Lijun , Guo Zhihua , Shen Peng

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-023-01130-4

Warianty tytułu

Języki publikacji

Abstrakty

The sandy conglomerate reservoir in layer Es3 of the Liaohe Eastern Depression has good potential for oil reservoir exploration and has been identified as a key area for future exploration. The low porosity and permeability, complex lithology, and strong heterogeneity of the target layer make it difficult to predict favorable reservoirs. The objective of this study is to analyze and process conventional logging data to extract feature parameters that affect lithology by establishing a decision tree lithology classifier. Principal component analysis is used to reduce data dimensionality, and the elbow method is applied to the clustering algorithm to establish the optimal number of clusters for the automatic classification of reservoir types. Further, support vector machines are used for lithology classification based on features with higher classification capabilities. The results show that the support vector machine lithology recognition method based on feature selection achieved an accuracy of 91.8%. The processing of actual well data has verified the feasibility of the method. Based on the combination of core experiments and oil testing results, the characteristics of three types of reservoirs were presented, and potential reservoir zones were proposed for drilling wells. The comprehensive analysis and the practical application of the developed method reveal that the class I reservoir has high hydrocarbon production and could be the most favorable reservoir in the Es3 sandy conglomerate. The processing data of lithology identification and reservoir classification evaluation are consistent with core data and hydrocarbon production data, verifying the effectiveness and practicability of the method proposed in this paper. The results of this study will serve as a reference for low porosity and permeability sandy conglomerate reservoir evaluation based on machine learning in the target area.

Słowa kluczowe

machine learning feature selection lithology identification favorable reservoir evaluation sandy conglomerate reservoir

uczenie maszynowe wybór cech

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2024

Tom

Vol. 72, no. 2

Strony

711--725

Opis fizyczny

Bibliogr. 78 poz.

Twórcy

autor

Jiang Yanjiao

0000-0003-2872-4118

School of Earth Science, Northeast Petroleum University, Daqing 163318, Heilongjiang, China
Key Laboratory Ministry of Education for Continental Shale Oil and Gas Accumulation and Efficient Development, Northeast Petroleum University, Daqing 163318, Heilongjiang, China

autor

Zhou Jian

zhouj87@163.com

School of Earth Science, Northeast Petroleum University, Daqing 163318, Heilongjiang, China
Key Laboratory Ministry of Education for Continental Shale Oil and Gas Accumulation and Efficient Development, Northeast Petroleum University, Daqing 163318, Heilongjiang, China

autor

Song Yanjie

School of Earth Science, Northeast Petroleum University, Daqing 163318, Heilongjiang, China
Key Laboratory Ministry of Education for Continental Shale Oil and Gas Accumulation and Efficient Development, Northeast Petroleum University, Daqing 163318, Heilongjiang, China

autor

Song Lijun

No.3 Drilling Company of Daqing Drilling Engineering Company, CNPC, Daqing 163411, Heilongjiang, China

autor

Guo Zhihua

School of Earth Science, Northeast Petroleum University, Daqing 163318, Heilongjiang, China
Key Laboratory Ministry of Education for Continental Shale Oil and Gas Accumulation and Efficient Development, Northeast Petroleum University, Daqing 163318, Heilongjiang, China

autor

Shen Peng

International Company, China Petroleum Logging CO. LTD., CNPC, Beijing 102200, China

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Bibliografia

Identyfikator YADDA

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