Improved gas sand facies classification and enhanced reservoir description based on calibrated rock physics modelling: A case study

Abstract

Rock physics provides a dynamic tool for quantitative analysis by developing the basic relationship between fluid, lithological, and depositional environment of the reservoir. The elastic attributes such as impedance, density, velocity, V p/V s ratio, Mu-rho, and Lambda-rho are crucial parameters to characterize reservoir and non-reservoir facies. Rock physics modelling assists like a bridge to link the elastic properties to petrophysical properties such as porosity, facies distribution, fluid saturation, and clay/shale volume. A robust petro-elastic relationship obtained from rock physics models leads to more precise discrimination of pay and non-pay facies in the sand intervals of the study area. The Paleocene aged Lower Ranikot Formation and Pab sandstone of Cretaceous age are proven reservoirs of the Mehar gas field, Lower Indus Basin. These sands are widely distributed in the southwestern part of the basin and are enormously heterogeneous, which makes it difficult to distinguish facies and fluid content in the reservoir intervals. So, an attempt is made in this paper to separate the reservoir facies from non-reservoir facies by using an integrated approach of the petro-elastic domain in the targeted sand intervals. Furthermore, missing logs (S-sonic and P-sonic) were also synthesized in the wells and missing intervals along with improving the poor quality of the density log by captivating the washouts and other side effects. The calibrated rock physics model shows good consistency between measured and modelled logs. Petro-elastic models were predicted initially using petrophysical properties and incorporated at true reservoir conditions/parameters. Lithofacies were defined based on petrophysical cut-offs. Rock physics modelled elastic properties (Lambda-rho versus Mu-rho, impedance versus V p/V s ratio) were then cross-plotted by keeping lithofacies in the Z-axis. The cross-plots clearly separated and demarcated the litho-fluid classes (wet sand, gas sand, shale, and limestone) with specific orientation/patterns which were randomized in conventional petrophysical analysis.

Supervised machine learning for predicting shear sonic log (DTS) and volumes of petrophysical and elastic attributes, Kadanwari Gas Field, Pakistan

“…Many researchers have successfully used rock physics techniques in the past to predict the DTS log in various fields such as the Middle Indus Basin (Azeem et al, 2015), Barnett Shale Formation (Guo and Li, 2015), North Poland (Wawrzyniak-Guz, 2019), LIB (Durrani et al, 2020), Zamzama Gas Field (Khan et al, 2021), and Mehar Block (Shakir et al, 2021) and further utilized these techniques in improving reservoir characterization based on seismic inversion techniques. Rock physics modeling has provided a decent estimation of the DTS curve, which was evaluated statistically through a QC plot, that is, prediction quality, which assesses the quality of the match between predicted and measured logs ranging from 0 to 1, was equal to 0.78 (Figure 3B).…”

“…Castagna et al (1985) and Han (1987) used empirical models to replicate the real cases, but they also acted as a subset of real cases. Azeem et al (2019), Shakir et al (2021), and Khan et al (2021) have used conventional rock physics modeling approaches to optimize and predict the missing logs without implementing the advanced ML technique in LIB. In this study, the successful execution of a novel ML approach for this basin helped in predicting accurate DTS along with elastic and petrophysical attributes to delineate reservoir potentials.…”

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

“…However, for thin heterogeneous Khadro Formation characterization, the high resolution posterior elastic attributes incorporating PEMs optimized responses proved efficient. The reservoirs' variability through PEMs is assessed by the amalgamation of key reservoir properties, i.e., pores configuration, grain-radii, litho-static and pore pressure variations, geochemistry, and the diagenesis effect (Ukaonu et al, 2017;Shakir et al, 2021). Hence, the integration of the reservoir information into the enhanced elastic attributes differentiates gas-filled sands in the petro-elastic domain and the relationship is extended to the multi-dimensional joint distribution of the litho-prob-ability cube (Boonyasatphan et al, 2019).…”

“…The PEMs optimized elastic logs, p-wave (Vp), s-wave (Vs), and density (ρ) are litho-facies dependent and incorporate effective properties such as dry rocks solid frame modulus (clay, calcite, and sands), fluids modulus within pores (brine and gas saturations), and factors that represent the environment, i.e., temperature and pressure during modeling (Da-Xing, 2017;Grana et al, 2017;Shakir et al, 2021). The improved set of modeled elastic properties adheres to the geological setting and compensates for the deficiencies of the borehole, therefore, discriminates litho-facies more pronouncedly in the petro-elas-Figure 1.…”

Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

“…In petrophysics, conventional methods have been employed to rectify and later remove the noisy data. Similarly, missing sonic logs in the splice zone and density logs due to bad holes cause misleading results. , Rock physics modeling is usually used to predict and optimize bad logs. RPM requires a detailed set of reservoir parameters along with a complex set of equations and models to reproduce reservoir conditions from the mineral level to formation and dry conditions to the saturation level. , Therefore, it requires a very expert-level approach and extra care; otherwise, it may produce erroneous results that later result in failure or misleading information.…”