Identification and characterization of tuffaceous sandstone based on petrophysical model:A case study of Paleogene sandstone reservoir in Huizhou Sag of the Pearl River Mouth Basin
In Huizhou H5 Oilfield in the Huizhou Sag of the Pearl River Mouth Basin,the sandstone reservoir in the lower section of the Paleogene Enping Formation is the major oil reservoir.Tight tuffaceous sandstone is developed in the reservoir due to volca-nism.Such sandstone has led to the degradation of reservoir performance.The prediction of high-quality reservoirs is challenging.Accurate identification of tuffaceous sandstone and characterization of its distribution are crucial to the prediction of high-quality reservoirs in Huizhou H5 Oilfield.In this paper,petrophysical modeling of tuffaceous sandstone was conducted using available drilling and seismic data,and sensitive petrophysical elastic parameters for identifying tuffaceous sandstone were obtained.Then,quantitative reservoir prediction was performed through seismic data prestack inversion and artificial intelligence deep learning.Furthermore,tight tuffaceous sandstone and its distribution were identified and characterized,highlighting high-quality sandstone reservoirs.Finally,the technology and process of artificial intelligence reservoir prediction driven by petrophysical modeling of tuf-faceous rocks and petrophysical models were proposed.This technology was applied to the reservoir evaluation of Huizhou H5 Oil-field,contributing to the successful identification of the tuffaceous sandstone in the H5-3d well area and the lower section of the Enping Formation in the H5-5d well area before drilling,and to the accurate characterization of the tuffaceous sandstone distribu-tion,which provide an important basis for the subsequent drilling of appraisal wells and reserve declaration.
physical model of tuffaceous sandstoneprestack inversionartificial intelligence deep learningquantitative reservoir prediction
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