@article{scholars10234,
           title = {Integrated reservoir characterization of low resistivity thin beds using three-dimensional modeling for natural gas exploration},
          volume = {65},
            note = {cited By 2},
             doi = {10.7186/bgsm65201810},
       publisher = {Geological Society of Malaysia},
         journal = {Bulletin of the Geological Society of Malaysia},
           pages = {91--99},
            year = {2018},
          author = {Jun, L. Y. and Zung, L. S.},
            issn = {01266187},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060306623&doi=10.7186\%2fbgsm65201810&partnerID=40&md5=dca3aa2337a5d3f5ab55e1be03f8f916},
        abstract = {A natural gas reservoir was discovered at approximately 3 km (TVDSS) through first vertical wildcat. Four subsequent wildcats were drilled in deviated trajectory to assess hydrocarbon distribution with no success. Resistivity log response from hydrocarbon interval appeared as low value low contrast. Seismic acquired onshore with high degree of static variation resulted in low frequency, unable to delineate thin sand interval efficiently. Several hypotheses were formed to explain the failed discovery. First, geological structure is complex due to local tectonic deformities created faults that compartmentalized the reservoir. Second, hydrocarbon charge and migration pathway might be underestimated. Third, presence of high conductive mineral might affect the resistivity log acquisition. An integration of three-dimensional enhanced seismic horizon and fault interpretation, unsupervised machine learning in facies classification, petrophysical data conditioning, rock physics cross validation, and three-dimensional static modeling is used to provide clearer insight on the natural gas play. {\^A}{\copyright} 2018 Geological Society of Malaysia. All rights reserved.}
}