Integrated Sequence Stratigraphic and Seismic Facies Analysis for Hydrocarbon Prospectivity of the Taranaki Basin, New Zealand

Author:

B. I. Odoh,,C. V. Ahaneku,,O. G. Ozoemena,,M. Ochili,,D.O. Anomneze,,C.F. Ejeke,,K.I. Chima,,S. Nweze,,C.V. Ojinna,,E.K. Boma,

Abstract

This study analyses the hydrocarbon potential and basin characteristics of the Taranaki Basin in New Zealand, the country's primary petroleum-producing region. The research, which uses a robust methodology involving 2D seismic data, well logs, and other geological information, examines the basin's stratigraphy, structural features, and petroleum systems. Key findings include the identification of two genetic sequences with associated system tracts, multiple reservoir and source rock units, and both structural and stratigraphic trapping mechanisms. Seismic facies analysis revealed eight distinct facies types which characterize the depositional environments. Play fairway mapping identified sweet spots where all petroleum system elements overlap. Risk assessment highlighted factors like gas chimneys and fault-compromised seals. The study concludes by presenting the geologic chance of success for three identified plays and one prospect in different stratigraphic intervals. This comprehensive analysis provides new insights into the under-explored portions of the Taranaki Basin and its hydrocarbon potential. By enhancing the understanding of the basin's stratigraphic architecture and depositional history, this study aims to improve reservoir distribution and quality predictability. Moreover, integrating seismic facies analysis with sequence stratigraphy offers a robust tool for delineating potential hydrocarbon-bearing zones, thereby reducing exploration risk and aiding the efficient reassessment of existing prospective zones and future exploration efforts.

Publisher

International Journal of Innovative Science and Research Technology

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Melanoma Detection using Convolutional Neural Networks;International Journal of Innovative Science and Research Technology (IJISRT);2024-09-12

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