The growing demand for hydrocarbons has driven the exploration of riskier prospects in depths, pressures, and temperatures. Substantial volumes of hydrocarbons lie within deep formations, classified as high pressure, high temperature (HPHT) zone. This study aims to delineate hydrocarbon potential in the HPHT zone of the Malay Basin through the integrated application of rock physics analysis, pre-stack seismic inversion, and artificial neural network (ANN). The zones of interest lie within Sepat Field, located offshore Peninsular Malaysia, focusing on the HPHT area in Group H. The rock physics technique involves the cross-plotting of rock properties, which helps to differentiate the lithology of sand and shale and discriminates the fluid into water and hydrocarbon. The P-impedance, S-impedance, Vp/Vs ratio, density, scaled inverse quality factor of P (SQp), and scaled inverse quality factor of S (SQs) volumes are generated from pre-stack seismic inversion of 3D seismic data. The obtained volumes demonstrate spatial variations of values within the zone of interest, indicating hydrocarbon accumulation. Furthermore, the ANN model is successfully trained, tested, and validated using 3D elastic properties as input, to predict porosity volume. Finally, the trained neural network is applied to the entire reservoir volume to attain a 3D porosity model. The results reveal that rock physics study, pre-stack seismic inversion, and ANN approach helps to recognize reservoir rock and fluids in the HPHT zone.
The Artificial Neural Network (ANN) is widely used to map and estimate reservoir properties. Since ANN has the ability of non-linear computing and self-error correction, it serves as an alternative method to enhance reservoir characterization by improving reservoir properties' prediction. This study employed an integrated approach where seismic, and well log data are used alongside ANN to evaluate petrophysical properties in Field X, South Caspian Basin. The study field is situated in the South Caspian Basin, which developed during Tertiary-Quaternary. The South Caspian Basin covers the southern part of the Caspian Basin, coastal regions of eastern Azerbaijan, northern Iran, and western Turkmenistan. The field is geologically situated on an elongated and multi-crest, anticlinal feature, better known as Apsheron Sill. Available data set consists of log data from four wells: X1, X2, X3, X4, and 3D pre-stack seismic data. Firstly, the reservoir properties were calculated from the well log data. The simultaneous inversion was carried out to obtain elastic rock properties like the P-impedance, S-impedance, density. After that, elastic properties were used to obtain 3D dimensional SQp and SQs attributes. Furthermore, the Radial basis function Neural Network (RBFN) model was created with optional inputs: volume of P-impedance, S-impedance, density, SQp, SQs and property logs and outputs: porosity. Moreover, this research work demonstrates an application of SQp and SQs attributes for properties prediction using neural network. The neural network's primary purpose is to determine a relationship between obtained volumes and reservoir parameters, porosity at well locations. The RBFN model is successfully trained and validated on the field data. The results demonstrated an excellent correlation between actual property logs. They predicted properties, which gives confidence for spatial prediction in areas where well logs are not available.
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