Estimation of porosity and facies distribution through seismic inversion in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos basin

Abstract

Precision porosity and facies determinations are critical in reducing drilling uncertainty and increasing hydrocarbon recoveries from heterogeneous sources. The porosity and facies distribution of the Taiyuan-Shanxi Formations (T9c-T9d), and Shihezi-1 Formation (T9d-T9e) within the Hangjinqi area are uncertain and no studies have covered the spatial distribution on a regional scale. The heterogeneous nature of coal, mudstone, and sandstone makes it challenging to comprehend the distribution of porosity and lithofacies. Also, the seismic resolution is not able to resolve the reservoir heterogeneity. Therefore, we have employed regional 3D seismic and well logs by utilizing the advanced acoustic impedance inversion to accomplish our study. Results of petrophysical analysis conducted on the well J32 showed that Shihezi-1 and Shanxi-1 Formations have potential gas-saturated zones. Crossplot analysis distinguished the lowest impedance coal from the highest impedance tight sandstone facies. The outcomes of the constrained sparse spike inversion (CSSI) reliably distinguished the coal facies from the channel-tight sandstone facies. The tight sandstone facies showed the highest impedance values as compared to coal and mudstone facies on the absolute acoustic impedance section. Impedance and porosity maps of T9d and T9e suggested the presence of a maximum porosity (8%–12% for T9d, and 5%–10% for T9e), and maximum distribution of tight sandstone facies, while T9c shows the lowest porosity (0%–6%) and lowest impedance values due to the presence of coal facies. Thick braided fluvial channels are evident on the T9d impedance and porosity maps, making it the most favorable horizon to produce the maximum gas. Whereas, T9c shows the least distribution of sandstone facies making it the least favorable. We propose that the zones of maximum porosity on the T9c, T9d, and T9e horizons can be exploited for future gas explorations.

Geochemical Characteristics and Origin of Sinian-Cambrian Natural Gas in Penglai Gas Area, Sichuan Basin

“…In the Penglai gas area, because there are many sets of caprocks such as tight dolomite in the Dengying Formation, shale in the Cambrian Qiongzhusi Formation, and Permian Longtan Formation above the Sinian gas reservoir (Figure ), the natural gas is well preserved. Based on the rock physical characteristics reflected by logging data, , combined with the burial history analysis of the caprock, the history of the breakthrough pressure evolution can be obtained. According to the results, several sets of caprocks had possessed sealing ability since the early stage of crude oil cracking (Figure ), and thus present the current geochemical characteristics.…”

Carbonate Reservoir Quality Variations in Basins with a Variable Sediment Influx: A Case Study from the Balkassar Oil Field, Potwar, Pakistan

“…Integrating geology, petrophysical, and geophysical data through seismic inversion has improved the reservoir assessment . Seismic porosities are estimated with the help of the PNN technique on the whole seismic cube. , Results indicate that the formation bears extremely low porosities throughout the study area (Figures and ). These low porosity values justify the high porosity reduction rates determined through the geohistory analysis.…”

Appraisal of reservoir porosity using a machine learning approach: A study from the Eocene‐Miocene interval of Upper Assam Basin, NE, India

“…It acts as a crucial tool for accurately describing the characteristics of a reservoir (e.g. porosity, saturation and lithofacies) (Abdolahi et al, 2022; Ashraf et al, 2022; Avadhani et al, 2006; Mahgoub et al, 2017; Xu et al, 2009). Hence, usage of this attribute, in particular, becomes more reliable for defining the reservoir property than using a set of multiple attributes such as instantaneous phase, frequency, amplitude, energy, variance and bandwidth.…”

“…Recently, an increase in the usage of ML algorithms has delivered impressive performance in predicting reservoir properties from seismic data (AlBinHassan & Wang, 2011;Ashraf et al, 2022;Cao & Roy, 2017;Das and Mukerji, 2020;Dixit et al, 2023;Gholami & Ansari, 2017;Zhou et al, 2021). AlBinHassan and Wang (2011) applied the data handling technique to generate porosity models from seismic properties automatically and achieved a better resolution than artificial neural networks.…”