Lithofacies uncertainty modeling in a siliciclastic reservoir setting by incorporating geological contacts and seismic information

Abstract

Deterministic modeling lonely provides a unique boundary layout, depending on the geological interpretation or interpolation from the hard available data. Changing the interpreter's attitude or interpolation parameters leads to displacing the location of these borders. In contrary, probabilistic modeling of geological domains such as lithofacies is a critical aspect to providing information to take proper decision in the case of evaluation of oil reservoirs parameters, that is, applicable for quantification of uncertainty along the boundaries. These stochastic modeling manifests itself dramatically beyond this occasion. Conventional approaches of probabilistic modeling (object and pixel-based) mostly suffers from consideration of contact knowledge on the simulated domains. Plurigaussian simulation algorithm, in contrast, allows reproducing the complex transitions among the lithofacies domains and has found wide acceptance for modeling petroleum reservoirs. Stationary assumption for this framework has implications on the homogeneous characterization of the lithofacies. In this case, the proportion is assumed constant and the covariance function as a typical feature of spatial continuity depends only on the Euclidean distances between two points. But, whenever there exists a heterogeneity phenomenon in the region, this assumption does not urge model to generate the desired variability of the underlying proportion of facies over the domain. Geophysical attributes as a secondary variable in this place, plays an important role for generation of the realistic contact relationship between the simulated categories. In this paper, a hierarchical plurigaussian simulation approach is used to construct multiple realizations of lithofacies by incorporating the acoustic impedance as soft data through an oil reservoir in Iran.

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

“…According to the principles of hydraulic flow units, the normal probability logarithm of the flow zone index in each hydraulic flow unit is linearly distributed. This method uses this principle and determines the number of hydraulic flow units (Davis, 2018;Heydari et al, 2012;Hosseini et al, 2023a;Madani et al, 2019;Manshad et al, 2021;Rabbani et al, 2018;Shirneshan et al, 2018;Yasmaniar et al, 2018). In this method, normal probability analysis is performed on the logarithmic data of the flow zone index, and four linear distributions are obtained, representing four hydraulic flow units and, thus, the number of hydraulic flow units obtained from the previous stage (Fig.…”

Estimation of Depth, Physical Parameters, and Location using Nonlinear Inverse Modeling of Magnetic Data by Bhattacharya Method in a field of NW Iran

“…It uses geophysical measurements to optimize the inverse problem by updating the physical property model, preferably in geologically uncertain parts of the studied area during what called uncertainty-guided inversion (Giraud et al, 2019;Liu et al, 2022). An optimized combination of geophysical survey methods was capable by Fu et al (2020) for taking single-point, continuous measurements (e.g., the high-precision ground magnetic survey (HPGMS), the transient electromagnetic (TEM), and the magnetotelluric (MT) methods) can be employed to accurately determine the anomalous planar spatial locations, anomalous pro le morphologies, and burial depths of concealed iron ore bodies such as banded iron formation (BIF)-type (Florio et al, 2022;Fu et al, 2020), and the survey helped to characterize subsurface geology better (Madani et al, 2019). Finally, used magnetic data were used to improve neighboring bodies' resolution and densify the information, pro le, and 2D survey by Sampaio et al (2021).…”

A Statistical Finite Element Method Integrating a Plurigaussian Random Field Generator for Multi-scale Modelling of Solute Transport in Concrete

“…The lithotype is a representation of the spatial relationship between facies (Doligez et al 2011) and can be used to determine the connectivity and relative proportions they exhibit. The method is most commonly applied in attaining field-scale representations of ore deposits (Betzhold and Roth 2000;Yunsel and Ersoy 2011;Renard and Beucher 2012;Talebi et al 2013;Maleki and Emery 2014;Mery et al 2017) and petroleum reservoirs (Chautru et al 2015;Beucher and Renard 2016;Zagayevskiy and Deutsch 2016;Martinius et al 2017;Madani et al 2018), but has also been used at much smaller scales such as in the generation of the micro-structure of solid oxide cells (SOCs). Abdallah et al (2016) applied the truncated plurigaussian model in a set of 3D simulations of three-phase anode layers.…”