INTEGRATED LAMINATED SHALY SAND ANALYSIS (LSSA) AND MONTE CARLO UNCERTAINTY PROBABILISTIC APPLIED FOR THE THIN LAMINATION RESERVOIR TOWARDS SIGNIFICANT NET PAY ADDITION

Abstract

In recent years, formation evaluation and well logging analysis have been evolving from its measurements, accuracy and applicability to determine tough downhole environments. Thin bed Reservoir is one of the most challenging well logging analysis due to the complexity of the high content of clay minerals in the shaly-sand lamination layer (thin bed) which inherently affects the log data response in the form of high gamma ray values and low resistivity. This causes the thin lamination zone is identified as a non-reservoir zone. Meanwhile, worldwide 30 to 40% of the oil in-place resources are confined within thin beds. Seeing the problem in the prediction of thin bed reservoirs, this research is focusing on Enhancing and developing thin bed potential by reevaluating petrophysical analysis, applying Laminated Shally Sand Analysis and Monte Carlo Uncertainty Probabilistic for calculating petrophysical parameter distribution and validating the parameter with Log Image. Laminated Shally Sand Analysis implements Thomas Steiber Plot in order to give laminated shale distribution and porosity, whereas the water saturation is calculated using Waxman Smith’s Equation. From this research, well SSK has thin bed potential at 2521.45 - 2543.45 ft. Laminated Shally Sand Analysis also capable to improve thickness of netpay with the range of 40.35% based on probabilistic delivered from Monte Carlo.