Estimation of vitrinite reflectance from well log data

Abstract

Vitrinite reflectance (VR) data provide important information for thermal maturity assessment and source rock evaluation. The current study introduces a practical method for vitrinite reflectance determination from sonic and resistivity logs. The main determinant factor of the method is ΔRRS which is defined as the separation between cumulative frequency values of resistivity ratio (RR) and sonic log data. The values of ΔRRS range from-1 at ground level to +1 at bottom hole. The crossing point depth of the DT and RR cumulative frequency curves, where ΔRRS=0, indicates the onset of oil generation window. From the surface (ground level) to the crossing point depth ΔRRS takes negative values indicating organic material diagenesis window. Below the crossing point depth ΔRRS turns into positive values showing thermally-mature organic matter within the catagenesis window. Vitrinite reflectance measurements revealed strong exponential relationships with the calculated ΔRRS data. Accordingly, a new calibration chart was proposed for VR estimation based on ΔRRS data. Finally, an equation is derived for vitrinite reflectance estimation from ΔRRS and geothermal gradient. The proposed equation works well in the event of having limited VR calibration data.

Vitrinite Equivalent Reflectance Estimation from Improved Maturity Indicator and Well Logs Based on Statistical Methods

“…Hence, electrical resistivity logs appear to be very useful Energies 2021, 14, 6182 2 of 16 in improving the formation maturity factor. Kadkhodaiea and Rezaeea [11] used acoustic and electrical resistivity logs in their studies to estimate the parameter and achieved promising results. The main introduced factor of the mentioned method is ∆RRS, which is defined as the separation between cumulative frequency values of resistivity ratio (RR) and sonic log data.…”

“…Currently, shale gas deposits in Poland are under intense consideration because of gas saturation and perspectives in exploitation [12][13][14][15][16][17][18][19]. Hence, estimation of basic parameters, such as R o or TOC content in the shale gas formations based on well logs and laboratory data become a key task for researchers [1,11,[20][21][22][23][24].…”

Prediction of vitrinite reflectance values using machine learning techniques: a new approach

“…Thus, vitrinite reflectance varies based on maturity and is assumed to be the traditional and most robust diagnostic tool for maturity investigation, which can be applicable in a wide range of maturity levels (Mählmann and Le Bayon 2016). Data of this parameter can be applied for source rock evaluation or coal rank assessment (Kadkhodaie and Rezaee 2017;Jiang et al 2019).…”