11/13/2022 0 Comments Hampson russell normalize logs![]() Pore pressure estimation is important for both exploration and drilling projects (Jamali and Sokooti 2008). Sometimes maybe a simple interval velocity model has good results and matches with well data. ![]() The methods used for obtaining the interval velocity model for an area depend on the complexity of subsurface structure in the area (Mavko et al. Generally, the results show the normal trend for pore pressure in the area, except in the left side of the anticline in the 2D seismic section, because of tectonic uplifting. The results of the pore pressure model are validated by pore pressure data obtained by the MDT well test tool. Finally, the velocity model is converted to pore pressure using Bowers (in: IADC/SPE drilling conference proceedings, 1995) relation. The final acoustic impedance model is converted to the velocity model by removing density. Manufactured sonic logs are modified using the check shot interval velocity of Sefid-Zakhor well No. The goal of this study is to estimate pore pressure relation with subsurface velocity in the Sefid-Zakhor gas field. ![]() Predrill pore pressure accurate prediction allows the appropriate mud weight to be selected and allows the casing program to be optimized, thus enabling safety by preventing wellbore collapse and economic drilling by reducing the cost. Mud weight and fracture gradient are essential parameters to have wellbore stability, prevent blowout, lost circulation, kick, sand production and reservoir damages. To optimize drilling decisions and well planning in abnormal pressured areas, it is essential to carry out pore pressure predictions before drilling. During the exploration phase, a prediction of pore pressure can be used to evaluate exploration risk factors including the migration of formation fluids and seal integrity. Additionally,petrophysical properties (clay volume and porosity) are derived from probabilisticneural network approach using well logs and pre-stack inverted attributes (pimpedanceand density) constrained with sample-based seismic attributes(instantaneous, windowed frequency, filters, derivatives, integrated and time).Pore pressure estimation is important for both exploration and drilling projects. Probable litho-facies (tight limestone and shale) are estimated usingwell based litho-facies classification and inverted seismic attributes (p-impedance anddensity) from pre-stack simultaneous inversion in a Bayesian framework. AVOattributes (intercept and gradient) conveniently inspection for amplitude behavior(reflection coefficients) of the possible AVO (class I), fluids and lithologycharacteristics. ![]() Three main litho-facies(hydrocarbon bearing limestone, tight limestone and shale) are classified estimatedbased on the precise analysis of well data using petrophysical properties. Quantitative seismic reservoir characterization approachrelied on well based litho-facies re-classification, Amplitude Variation with Offset (AVO)attributes analysis and Pre-Stack simultaneous inversion attributes constrained withcustomized well-log and seismic data (gathers) conditioning. Abstract : In this study a tight carbonate gas reservoir of early Eocene (S1 formation) is studiedfor litho-facies estimation and probabilistic estimation of reservoir properties predictionusing quantitative geophysical approach from a mature gas field in the Middle IndusBasin, onshore Pakistan. ![]()
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