Geophysical prediction technology based on organic carbon content in source rocks of the Huizhou sag, the South China Sea

• Autorzy: Yang W. , Gong X.-X. , Peng F.-F.
• Języki publikacji: EN

Abstrakty

EN

Due to the high exploration cost, limited number of wells for source rocks drilling and scarce test samples for the Total Organic Carbon Content (TOC) in the Huizhou sag, the TOC prediction of source rocks in this area and the assessment of resource potentials of the basin are faced with great challenges. In the study of TOC prediction, predecessors usually adopted the logging assessment method, since the data is only confined to a “point” and the regional prediction of the source bed in the seismic profile largely depends on the recognition of seismic facies, making it difficult to quantify TOC. In this study, we combined source rock geological characteristics, logging and seismic response and built the mathematical relation between quasi TOC curve and seismic data based on the TOC logging date of a single well and its internal seismic attribute. The result suggested that it was not purely a linear relationship that was adhered to by predecessors, but was shown as a complicated non-linear relationship. Therefore, the neural network algorithm and SVMs were introduced to obtain the optimum relationship between the quasi TOC curve and the seismic attribute. Then the goal of TOC prediction can be realized with the method of seismic inversion

• Wydawca: -
• Czasopismo: Polish Maritime Research
• Rocznik: 2017
• Tom: 24
• Numer: Special Issue S2
• Opis fizyczny: p.4-13,fig.,ref.

Twórcy

autor

Yang W.

• School of Geoscience and Technology, Southwest Petroleum University, Chengdu, China
• Institute of Geophysics, Southwest Petroleum University, Chengdu, China

autor

Gong X.-X.

• College of energy resources ,Chengdu University of Technology ,Chengdu, China
• State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, China

autor

Peng F.-F.

• Exploration and Development Research Institute of Dagang Oilfield, CNPC, Tianjin, China

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Identyfikatory

DOI

10.1515/pomr-2017-0058