3D geo-cellular modeling for Oligocene reservoirs: a marginal field in offshore Vietnam

Abstract

This study focuses on constructing a 3D geo-cellular model by using well-log data and other geological information to enable a deep investigation of the reservoir characteristics and estimation of the hydrocarbon potential in the clastic reservoir of the marginal field in offshore Vietnam. In this study, Petrel software was adopted for geostatistical modeling. First, a sequential indicator simulation (SIS) was adopted for facies modeling. Next, sequential Gaussian simulation (SGS) and co-kriging approaches were utilized for petrophysical modeling. Furthermore, the results of the petrophysical models were verified by a quality control process before determining the in-place oil for each reservoir in the field. Multiple geological realizations were generated to reduce the geological uncertainty of the model assessment for the facies and porosity model. The most consistent one would then be the best candidate for further evaluation. The porosity distribution ranged from 9 to 22%. The original oil place of clastic reservoirs in the marginal field was 50.28 MMbbl. Ultimately, this research found that the marginal field could be considered a potential candidate for future oil and gas development in offshore Vietnam.

3D Geomechanical Model Construction for Wellbore Stability Analysis in Algerian Southeastern Petroleum Field

“…Once the grids were created, they were filled with the mechanical properties that characterized the rock; namely, the density, the elastic properties (Young's modulus, Poisson ratio, and Biot coefficient), and the resistance properties (UCS, the resistance of the rock to tension, and the friction angle) [10]. These properties were calculated using the same method as in the 1D model constructed earlier in this study and with the same mathematical relationships [17].…”

“…In order to determine the volumes for the acoustic impedance, the Vp/Vs ratio, and the density, correlation values between the inverted model and the logging data were obtained. The correlation values ranged from 72% to 85% [10]. The second part of the paper is dedicated to the construction of the 3D geomechanical model.…”

“…They also identified [3] the favourable gas zones through fault distribution, extracting them from the 3D seismic inversion model. Most recently, Hung and Lee have modelled [10] the geo-cellular data for an Oligocene reservoir based on a 3D inversion model, but this approach was utilized only for a marginal offshore field and has not been studied for onshore petroleum fields.…”

Sand-ratio distribution in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos Basin: Acoustic impedance inversion-based reservoir quality prediction

“…Tight sandstone is generally defined as a reservoir with porosity of around 12% and less than 1mD (Dai et al, 2012). Henceforth, reservoir characterization and reservoir quality prediction are thus being studied by a large number of geologists, geophysicists, petrophysicist and petroleum engineers (Ehsan et al, 2018;Abdulaziz et al, 2019;Ashraf et al, 2019;Ehsan et al, 2019;Qiang et al, 2020;Radwan, 2020;Vo Thanh et al, 2020;Ashraf et al, 2021;Kassem et al, 2021;Radwan, 2021;Radwan et al, 2021;Dar et al, 2022;Jiang et al, 2022;Ullah et al, 2022;Vo Thanh and Lee, 2022). The Ordos Basin has China's biggest yearly gas production (Duan et al, 2008;Yang et al, 2015).…”

Fluid inclusion characteristics of the Jurassic reservoir and hydrocarbon accumulation process in the eastern Kuqa Depression, Tarim Basin

“…When petroleum is expelled into the reservoir, some fluid inclusions of the rock-forming and ore-forming components are captured by the cement and micro-fractures during the process of mineral crystallization, and these inclusions record information on fluid temperature, pressure, and composition during oil and gas migration (Chen et al 2000;Parnell et al 2010). The geological properties of hydrocarbon inclusions and brine inclusions contain important information on migration and accumulation and help in the study of hydrocarbon accumulation chronology (Horsfield et al 1984;Goldstein 2001;Liu et al 2013;Thanh et al 2020;Thanh et al 2022). Investigations of fluid evolution in petroliferous basins are critical for understanding petroleum migration and accumulation and have significance for predicting the distribution of hydrocarbon resources (Guo et al 2016b;Thanh et al 2019).…”