Advances and challenges in shale oil development: A critical review

    Different from the conventional oil reservoirs, the primary storage space of shale is micro/nano pore networks. Moreover, the multiscale and multi-minerals characteristics of shale also attract increasing attentions from researchers. In this work, the advances and challenges in the development of shale oil are summarized from following aspects: phase behavior, flow mechanisms, reservoir numerical simulation and production optimization. The phase behavior of fluids confined in shale nanopores are discussed on the basis of theoretical calculations, experiments, and molecular simulations. The fluid transport mechanisms through shale matrix are analyzed in terms of molecular dynamics, pore scale simulations, and experimental studies. The methods employed in fracture propagation simulation and production optimization of shale oil are also introduced. Clarifying the problems of current research and the need for future studies are conducive to promoting the scientific and effective development of shale oil resources.Cited as: Feng, Q., Xu, S., Xing, X., Zhang, W., Wang, S. Advances and challenges in shale oil development: A critical review. Advances in Geo-Energy Research, 2020, 4(4), 406-418, doi: 10.46690/ager.2020.04.0

Predictive Scale-Bridging Simulations through Active Learning

Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models

In Situ Imaging of Heterogeneous Catalysts from the Micrometer to the Nanometer Scale

The function and efficiency of a catalyst is influenced by its design on several length scales. Therefore, the characterization of catalysts by complementary techniques on all length scales is required to understand the underlying processes and to improve the catalyst function. In this work, the micrometer and nanometer scale of heterogeneous catalysts are probed by spectroscopic and microscopic methods and in situ cells suitable for studying these length scales are presented

Complementary 2D/3D Imaging of Functional Materials using X-ray & Electron Microscopy

Catalysts and other functional materials are generally hierarchically structured materials. Hence, the detailed characterization at different length scales, and especially under reaction conditions, are necessary to unravel their mechanisms and to improve their performance and catalytic activities. Besides, a combination of several techniques is required to acquire complementary information owing to the lack of a single technique able to cover all the length scales. With respect to length, the best way to investigate is by microscopy either in 2D or more preferably in 3D. The work began with an exploration of three different 3D imaging techniques, i.e. ptychographic X-ray computed tomography, electron tomography, and focused ion beam slice-and view. Using nanoporous gold as the model, this study aimed to exhibit the versatility of 3D microscopy as a method beyond imaging as well as to confirm the necessity of complementary nature between them, where electron offers better spatial resolution and X-ray provides larger field of view. The study then continued by utilizing ptychographic X-ray computed tomography for quasi in situ thermal treatment of the same materials under atmospheric pressure. This study highlighted its ease of use of implementing in situ studies, complemented by electron tomography to prove its powerful ability to resolve what ptychographic tomography cannot. The resulting 3D volumes were then used for air permeability and CO2 diffusion simulations, along with material’s electrical and thermal conductivity simulations in order to further expose another excellent benefit from 3D microscopy. Ultimately, the work proceeded into developing two cells in order to perform full in situ investigations under controlled temperatures and atmospheres, where one cell was built for 2D only (X-ray) ptychography experiments with simultaneous X-ray fluorescence mapping, and the other was constructed with an additional capability for 3D limited-angle ptychographic tomography experiments. The feasibility tests were conducted using several functional materials, i.e. nanoporous gold, zeolite, and cobalt-manganese-oxides hollow sphere, as the models for thermal annealing process under specific atmospheres. This work eventually attests the importance of in situ studies in precisely determining the onset annealing temperatures under particular environments, to visualize the morphology online either in 2D or 3D, and to simultaneously map elemental distributions live. Moreover, a complementary technique via transmission electron microscopy was also demonstrated on the same sample, adding up another advantage in using the cells. Despite the preliminary results from in situ limited-angle ptychographic tomography experiments for limitation in data reconstruction, a new tomographic reconstruction technique was recently developed as a solution to acquire 3D images with shortened acquisition times. In conclusions, the work here converges into the ideal case of performing all-around in situ 3D imaging of functional materials for quantitative analysis and simulation

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures

Advance Nanomaterials for Biosensors

The book provides a comprehensive overview of nanostructures and methods used to design biosensors, as well as applications for these biosensor nanotechnologies in the biological, chemical, and environmental monitoring fields. Biological sensing has proven to be an essential tool for understanding living systems, but it also has practical applications in medicine, drug discovery, food safety, environmental monitoring, defense, personal security, etc. In healthcare, advancements in telecommunications, expert systems, and distributed diagnostics are challenging current delivery models, while robust industrial sensors enable new approaches to research and development. Experts from around the world have written five articles on topics including:Diagnosing and treating intraocular cancers such as retinoblastoma; Nanomedicine in cancer management; Engineered nanomaterials in osteosarcoma diagnosis and treatment; Practical design of nanoscale devices; Detect alkaline phosphatase quantitatively in clinical diagnosis; Progress in the area of non-enzymatic sensing of dual/multi biomolecules; Developments in non-enzymatic glucose and H2O2 (NEGH) sensing; Multi-functionalized nanocarrier therapies for targeting retinoblastoma; Galactose functionalized nanocarriers; Sensing performance, electro-catalytic mechanism, and morphology and design of electrode materials; Biosensors along with their applications and the benefits of machine learning; Innovative approaches to improve the NEGH sensitivity, selectivity, and stability in real-time applications; Challenges and solutions in the field of biosensors

giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration

We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations. The library's ability to handle various types of data is rooted in a wide range of preprocessing techniques, and its strong focus on data exploration and interpretability is aided by an intuitive plotting API. Source code, binaries, examples, and documentation can be found at https://github.com/giotto-ai/giotto-tda.Comment: 7 pages, 2 figure

Advances in modeling gas adsorption in porous materials for the characterization applications

The dissertation studies methods for mesoporous materials characterization using adsorption at various levels of scale and complexity. It starts with the topic introduction, necessary notations and definitions, recognized standards, and a literature review. Synthesis of novel materials requires tailoring of the characterization methods and their thorough testing. The second chapter presents a nitrogen adsorption characterization study for silica colloidal crystals (synthetic opals). These materials have cage-like pores in the range of tens of nanometers. The adsorption model can be described within a macroscopic approach, based on the Derjaguin-Broekhoff-de Boer (DBdB) theory of capillary condensation. A kernel of theoretical isotherms is built and applied to the solution of the adsorption integral equation to derive the pore-size distribution from experimental data. The technique is validated with a surface modification of the samples so that it changes the interaction but not the pore size. The second chapter deals with the characterization of three-dimensional ordered mesoporous (3DOm) carbons. Similar to opals, these materials have cage-like mesopores, however, these pores are connected with large windows. These windows affect the adsorption process and calculated pore-size distributions. The grand canonical Monte Carlo simulations with derived solid-fluid potentials, which take into account the 3DOm carbons geometry, confirm the critical role of interconnections, their size, and number, for correct interpretation of adsorption data for the PSD calculations. The fourth chapter discusses a method for the pore size estimation that can serve as an alternative to the adsorption isotherms analysis. It is based on measurements of elastic properties of liquid that can be useful for the pore size estimation. A Vycor glass sample, a disordered mesoporous material with channel-like pores having a characteristic size of ca. 6-8 nm, is considered. The changes in longitudinal and shear moduli from the experimental data and molecular simulations are predicted with a near-quantitative agreement. Then, it follows by their relation of the moduli to the pore size, which is promising for characterization. The last fifth chapter considers a promising Monte Carlo method, the Kinetic Monte Carlo (kMC) algorithm. This method is efficient for the vapor-liquid equilibrium prediction in dense regions. This chapter shows a benchmark with conventional Metropolis et al algorithms as well as a parallelization scheme of the kMC algorithm