Hydrocarbon Potential Sweet Spot Evaluation with Artificial Intelligent in Mature and Complex Sandstone Reservoirs

Abstract The hydrocarbon potential is important to be defined in development reservoir and field. The complex reservoir conditions with overpressure, multi fluid contacts, multi-reservoir subunits, structural and stratigraphic sand discontinuities are the challenges on the complexity and time spending. An Artificial Intelligent (AI) with the integrated reservoir characterization, new high seismic data and model are used for AI reservoir model in mature and complex reservoirs. The suitable AI algorithm has been evaluated for reliable sweet spot reservoir characterization in the effective time. The reservoir characterization with updated geophysic, geological, petrophysical, reservoir and production data have been used in the AI workflow for sweet spot model and evaluation. The available wells formation pressures, PVT, image log, core-logs saturation height model and production data have been used in the compartment and reservoir discontinuity evaluation. The special comprehensive wells data and high resolution of seismic have been selected for the hydrocarbon sweet spot evaluation. The suitable AI algorithm has been arranged in the evaluation with involvement domain subsurface engineers in validation of key wells parameters data. The AI reservoir model is intended to provide the oil and condensate sweet spots for further potential wells of development and appraisal in the matured oil field area. The integrated reservoir data with the latest seismic data have been arranged to provide reliable and good of reservoir properties and results for updated sweet spot AI reservoir model. The high resolution seismic has been integrated with open and cased-hole logs data such as image resistivity/density, chromatography, pulse-neutron capture and production logs have been used to verify fault, fluid contacts, contribution, water saturation changes and production optimization. And for every reservoir subunit, the formation pressure has been used to identify an initial oil water contact and reservoir compartment/sand discontinuity evaluation, thus it has provided high resolution and reliability results for complex reservoir modeling. Validation of hydrocarbon sweet spot with criteria's high oil saturation, high-moderate reservoir pressure, high-moderate permeability, porosity and oil volume in key reservoirs/wells have been used in the AI evaluation, for the development of multi-layers and complex sandstone reservoirs. The potential oil sweet spots for further appraisal and development can be evaluated in the area. The integrated approach reservoir characterization and AI in this paper shows the value of advanced reservoir characterization with the latest data and effective evaluation time in the complex reservoirs. It has utilized the integration updated high seismic resolution and reservoir fluid-rock data. The evaluation also has been expected the reliable and efficient time for recent oil sweat spot data of field appraisal and development purposes.