INTEGRATED SUBSURFACE TO SURFACE MODELING TO ASSESS RESERVOIR UNCERTAINTY QUANTIFICATION TO ‘K’ FIELD PRODUCTION

• Main Author: Chaterine, Alysia
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/48976
• Institution: Institut Teknologi Bandung

This study accommodates subsurface uncertainties analysis and quantify the effects on surface production volume as to propose the optimal future field development. The problem of well productivity is sometimes only viewed from the surface components themselves, where in fact the subsurface component often has a significant effect on these production figures. In order to track the relationship between surface and subsurface, a model that integrates both must be created. The methods covered integrated asset modeling, probabilistic forecasting, uncertainty quantification, and sensitivity analysis. Subsurface uncertainties examined were: reservoir closure, regional segmentation, fluid contact, and SCAL properties. As the Integrated Asset Modeling is successfully conducted and several cases of production probability forecasting are obtained, up to ±75% of reservoir uncertainty range variable value conducted yields various of production forecasting with the best probabilistic case obtained for “K” Field is 176.6 - 254.4 BSCF gas production and 273.7 – 329.5 MSTB oil production. The main five reservoir uncertainty that affects gas and oil production of the field are: Corey curvature (Ng), relative permeability of gas (Krg), oil-to-water critical saturation (Sowcr), gas-to-oil critical saturation (Sogcr), and pseudo-fault. As condensate production is highly possible to occur and gives the NPV a significant effect, EOS and rock compressibility modeling must be evaluated in detail. A production scenario for future development with up to 151.58 BSCF gas and 414.4 MSTB oil that yields a NPV of 102.6 MMUSD is proposed. The approach and methods implemented has been proven to acquire the more accurate production forecast and reduce the project cost as the effect of uncertainty reduction.