Microporosity evolution and destruction in the Jurassic Arab D reservoir, Qatar

Core samples were collected from three wells, one onshore and two offshore, from Qatar’s Upper Jurassic Arab D reservoir. The samples were subjected to multiproxy petrographic and chemical analyses to identify their micro- and nanoporosity types and understand their evolution and destruction. Based on the petrographic and petrophysical properties of studied rocks, the Arab D succession was divided into seven rock types. Primary microporosity includes intergranular and interplanar, while secondary types include vuggy, intercrystalline, moldic, dissolution, pyrite displacement, microfracture, and microbial boring. Primary micropores were found mainly between the micrite grains in the lime mudstone facies, between the grains or the plates of clay minerals. Secondary micropores result from open and closed diagenetic systems. The open diagenetic system led to the development of dissolution and moldic micropores, while the closed system created pyrite displacement and boring porosity. Mechanical stress due to crystal growth or displacement generated microfractures. Micropores were destroyed either by cementation, clay minerals growth, dolomitization, or microbial pustular overgrowth. Microporosity was important in quantity and varied in nature in the mud-supported rocks. They are similar to macropores in grain-supported sediments but of less importance. The complex lithology of the studied rocks has significantly influenced the development and destruction of the porosity system of the Arab Formation.The chemical analyses were conducted at the Central Laboratory Unit, Qatar University. We are grateful for their continuous support and professionalism. We thank the management and technicians of the Gas Processing Center (GPC) and the Center for Advanced Materials (CAM), Qatar University for the XRF and XRD analyses. Thanks to Thomas Seers and Ibrahim Almaghrabi (Texas A&M, Doha Campus) for the thin section preparation. David Marioni read the original manuscript and made many valuable amendments. This research is supported by Qatar Foundation through Grant # NPRP11S-0109-180241

Dynamics of mechanical waves in periodic grapheme nanoribbon assemblies

We simulate the natural frequencies and the acoustic wave propagation characteristics of graphene nanoribbons (GNRs) of the type (8,0) and (0,8) using an equivalent atomistic-continuum FE model previously developed by some of the authors, where the C-C bonds thickness and average equilibrium lengths during the dynamic loading are identified through the minimisation of the system Hamiltonian. A molecular mechanics model based on the UFF potential is used to benchmark the hybrid FE models developed. The acoustic wave dispersion characteristics of the GNRs are simulated using a Floquet-based wave technique used to predict the pass-stop bands of periodic mechanical structures. We show that the thickness and equilibrium lengths do depend on the specific vibration and dispersion mode considered, and that they are in general different from the classical constant values used in open literature (0.34 nm for thickness and 0.142 nm for equilibrium length). We also show the dependence of the wave dispersion characteristics versus the aspect ratio and edge configurations of the nanoribbons, with widening band-gaps that depend on the chirality of the configurations. The thickness, average equilibrium length and edge type have to be taken into account when nanoribbons are used to design nano-oscillators and novel types of mass sensors based on periodic arrangements of nanostructures

Structure-Sensitive Mechanism of Nanographene Failure

The response of a nanographene sheet to external stresses is considered in terms of a mechanochemical reaction. The quantum chemical realization of the approach is based on a coordinate-of-reaction concept for the purpose of introducing a mechanochemical internal coordinate (MIC) that specifies a deformational mode. The related force of response is calculated as the energy gradient along the MIC, while the atomic configuration is optimized over all of the other coordinates under the MIC constant-pitch elongation. The approach is applied to the benzene molecule and (5, 5) nanographene. A drastic anisotropy in the microscopic behavior of both objects under elongation along a MIC has been observed when the MIC is oriented either along or normally to the C-C bonds chain. Both the anisotropy and high stiffness of the nanographene originate at the response of the benzenoid unit to stress.Comment: 19 pages, 7 figures 1 tabl

Topological mechanochemistry of graphene

In view of a formal topology, two common terms, namely, connectivity and adjacency, determine the quality of C-C bonds of sp2 nanocarbons. The feature is the most sensitive point of the inherent topology of the species so that such external action as mechanical deformation should obviously change it and result in particular topological effects. The current paper describes the effects caused by uniaxial tension of a graphene molecule in due course of a mechanochemical reaction. Basing on the molecular theory of graphene, the effects are attributed to both mechanical loading and chemical modification of edge atoms of the molecule. The mechanical behavior is shown to be not only highly anisotropic with respect to the direction of the load application, but greatly dependent on the chemical modification of the molecule edge atoms thus revealing topological character of the graphene deformation.Comment: 9 pages, 10 figures, 1 table. arXiv admin note: text overlap with arXiv:1301.094

Applications of single-layered graphene sheets as mass sensors and atomistic dust detectors, Solid State Commun 145

ABSTRACT Molecular structural mechanics is implemented to model vibrational behavior of defect free single-layered graphene sheets (SLGSs) at constant temperature. To mimic these two-dimensional layers, zigzag and armchair models with cantilever and bridge boundary conditions are adopted. Fundamental frequencies of these nano structures are calculated, and it is perceived that they are independent of the chirality and aspect ratio. Effects of point mass and atomistic dust on the fundamental frequencies are also considered in order to investigate the possibility of using SLGSs as sensors. Results of exhibit the principle frequencies are highly sensitive to the added mass in the order of 10 -6 fg

Machine Learning for Capillary Pressure Estimation

Capillary pressure plays an essential role in controlling multiphase flow in porous media and is often difficult to be estimated at subsurface conditions. The Leverett capillary pressure function J provides a convenient tool to address this shortcoming; however, its performance remains poor where there is a large scatter in the scaled data. Our aim, therefore, was to reduce the gaps between J curves and to develop a method that allows accurate scaling of capillary pressure. We developed two mathematical expressions based on permeability and porosity values of 214 rock samples taken from North America and the Middle East. Using the values as grouping features, we used pattern-recognition algorithms in machine learning to cluster the original data into different groups. In each wetting phase saturation, we were able to quantify the gaps between the J curves by determining the ratio of the maximum J to the minimum J. Graphical maps were developed to identify the corresponding group for a new rock sample after which the capillary pressure is estimated using the average J curve of the identified group and the permeability and porosity values of the rock sample. This method also provides better performance than the flow zone indicator (FZI) approach. The proposed technique was validated on six rock types and has successfully generated average capillary pressure curves that capture the trends and values of the experimentally measured data by mercury injection. Moreover, the proposed methodology in this study provides an advanced and a machine-learning-oriented approach for rock typing. In this paper, we provide a reliable and easy-to-use method for capillary pressure estimation in the absence of experimentally measured data by mercury injection. Copyright 2022 Society of Petroleum EngineersWe would like to acknowledge the support of Qatar National Research Fund (a member of Qatar Foundation) through Grant # NPRP11S-1228-170138. The findings achieved herein are solely the responsibility of the authors.Scopu

Predicting carbonate formation permeability using machine learning

It is imperative to characterize the formation permeability to simulate the flow behavior at subsurface conditions. An accurate characterization at the core scale is possible when large samples are available, but often this is not the case, as such samples are hard to recover. Instead, drill cuttings (small pieces) are usually the only source available, especially in real-time conditions. Thus, mercury injection capillary pressure measurements, which are applicable to small pieces, have been used to infer the formation permeability. The challenge is that capillary pressure measurements entail further interpretations, as they can be converted to the pore-throat size distribution but not directly to the permeability. Thus, researchers have proposed different empirical and theoretical relations to predict the permeability. The present study uses machine learning, a data-driven approach, to predict carbonate formation permeability. The data-driven approach does not impose any restriction on the spatial distribution of the pore-throat sizes in the network of connected pores, but rather trains models based on the existing data. The present study is based on 193 carbonate samples whose data (porosity, permeability, and mercury injection capillary pressure measurements) are available in the literature. The permeability values vary from nanodarcies to darcies. We propose two new correlations, with and without grouping analysis, for permeability prediction. The results are promising, as the averaged R2 score obtained with 50 iterations is larger than 0.96. The study provides a valuable tool for permeability prediction based on numerical methods that distinguish the pore structure by taking into account underlying trends in the measurements.The authors would like to acknowledge the support of Qatar National Research Fund (a member of Qatar Foundation ) through Grant # NPRP11S-1228-170138 . The findings achieved herein are solely the responsibility of the authors.Scopu