Research on Oil and Gas-Bearing Zone Prediction and Identification Based on the SVD–K-Means Algorithm—A Case Study of the WZ6-1 Oil-Bearing Structure in the Beibu Gulf Basin, South China Sea

Zhilong Chen, Renyi Wang (), Biao Xu and Jianghang Zhu
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Zhilong Chen: School of Petrochemical Engineering and Environment, Zhejiang Ocean University, Zhoushan 316022, China
Renyi Wang: School of Petrochemical Engineering and Environment, Zhejiang Ocean University, Zhoushan 316022, China
Biao Xu: School of Petrochemical Engineering and Environment, Zhejiang Ocean University, Zhoushan 316022, China
Jianghang Zhu: School of Petrochemical Engineering and Environment, Zhejiang Ocean University, Zhoushan 316022, China

Energies, 2024, vol. 17, issue 22, 1-12

Abstract: The WZ6-1 oil-bearing structure in the Beibu Gulf Basin of the South China Sea has well-developed faults with significant variations in fault sealing capacity, resulting in a complex and highly variable distribution of oil, gas, and water, and limited understanding of hydrocarbon accumulation patterns. Traditional methods, such as single seismic attributes and linear fusion of multiple seismic attributes, have proven ineffective in identifying and predicting oil and gas-bearing areas in this region, leading to five unsuccessful wells. Through comprehensive analysis of drilled wells and seismic data, six types of horizon seismic attributes were selected. A novel approach for predicting oil-bearing zones was proposed, employing SVD–K-means nonlinear clustering for multiple seismic attribute fusion. The application results indicate: ① Singular value decomposition (SVD) technology not only reduces the correlation redundancy among seismic attribute data variables, but enables data dimensionality reduction and noise suppression, decreasing ambiguity in prediction results and enhancing reliability. ② The K-means nonlinear clustering method facilitates the nonlinear fusion of multiple seismic attribute parameters, effectively uncovering the nonlinear features of the underlying relationship between seismic attributes and reservoir oil-bearing characteristics, thereby amplifying the hydrocarbon information within the seismic attribute variables. ③ Compared to K-means, SVD–K-means demonstrates superior performance across all metrics, with an 18.4% increase in the SC coefficient, a 57.8% increase in the CH index, and a 24.7% improvement in the DB index. ④ The results of oil-bearing zone prediction using the SVD–K-means algorithm align well with the drilling outcomes in the study area and correspond to the geological patterns of hydrocarbon enrichment in this region. This has been confirmed by the high-yield industrial oil flow obtained from the newly drilled WZ6-1-A3 well. The SVD–K-means algorithm for predicting oil and gas-bearing zones provides a new approach for predicting hydrocarbon-rich areas in complex fault block structures with limited drilling and poor-quality seismic data.

Keywords: seismic attributes; K-means algorithm; singular value decomposition; petroliferous area prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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