Influence of silica nanoparticles on the surface properties of carbonate reservoirs

In the last decade, nanofluids-based techniques have shown tremendous promise in oil and gas industry. Nanofluids, dispersions of nanoparticles (NPs) in a base-fluid, have shown promises potentials in enhanced oil recovery (EOR), and carbon geosequestration projects. Based on the qualitative analysis, the study reveals that NPs, as a new EOR approach, offer applicable potentials and opportunities that can be implemented successfully at reservoir conditions and show better profits over conventional EOR techniques

Modelling Inductive Charging of Battery Electric Vehicles using an Agent-Based Approach

The introduction of battery electric vehicles (BEVs) could help to reduce dependence on fossil fuels and emissions from transportation and as such increase energy security and foster sustainable use of energy resources. However a major barrier to the introduction of BEVs is their limited battery capacity and long charging durations. To address these issues of BEVs several solutions are proposed such as battery swapping and fast charging stations. However apart from these stationary modes of charging, recently a new mode of charging has been introduced which is called inductive charging. This allows charging of BEVs as they drive along roads without the need of plugs, using induction. But it is unclear, if and how such technology could be utilized best. In order to investigate the possible impact of the introduction of such inductive charging infrastructure, its potential and its optimal placement, a framework for simulating BEVs using a multi-agent transport simulation was used. This framework was extended by an inductive charging module and initial test runs were performed. In this paper we present the simulation results of these preliminary tests together with analysis which suggests that battery sizes of BEVs could be reduced even if inductive charging technology is implemented only at a small number of high traffic volume links. The paper also demonstrates that our model can effectively support policy and decision making for deploying inductive charging infrastructure

Coupled Thermal-Hydraulic-Mechanical (THM) modelling of underground gas storage – A case study from the Molasse Basin, South Germany

Thermal-hydraulic-mechanical (THM) models of gas storage in porous media provide valuable information for various applications. The range of these applications varies from prediction of ground surface displacements, determination of stress path changes, and maximum reservoir pressure to storage capacity for maintaining fault stability and overburden integrity. The study, conducted in collaboration with research institutes and storage companies in Germany, addresses the numerical modelling of geomechanical effects caused by the storage of methane in a depleted gas field. The geomechanical assessment focuses on a former gas reservoir in the Bavarian Molasse Basin east of Munich, for which a hypothetical conversion into underground gas storage (UGS) is considered. The target reservoir is of Late Oligocene age, i.e., the Chattian Hauptsand with three gas bearing layers having a total thickness of 85 m. The reservoir formation is highly porous with an average porosity of 23% and permeability is in the range between 20 mD and 80 mD. The reservoir has produced natural gas from 1958 till 1978 and has been in a shut-in phase ever since. The storage operations require precise understanding of reservoir mechanics and stresses; therefore, the selected methodology helps to analyze these issues in detail. The geomechanical analysis is performed with the help of a state-of-the-art THM model with the following objectives: (1) analyze the variation of principal stress field induced by the field activities (2) analyze the effective stress changes with changing pore pressure in short-term as well as long-term using hypothetical injection-production schedule cases (3) prediction of ground surface displacements over the field, (4) analyze the possible reactivation of faults and fractures as well as the safe storage capacity of the reservoir; and (5) thermal stress changes with injection of colder foreign gas in underground reservoir. The methodology comprises 1D mechanical earth modelling (MEM) to calculate elastic properties as well as a first estimate for the vertical and horizontal stresses at well locations by using log data. This modelling phase provide complete analyses of log, core and laboratory data which leads to detailed 1D MEM of all the wells available for case study reservoir. This information is then used to populate a 3D finite element MEM) which has been built from seismic data and comprises not only the reservoir but the entire overburden up to the earth’s surface as well as part of the underburden. The size of this model is 30 × 24 × 5 km3 and 3D property modelling has been done by applying geostatistical approach for property inter-/extrapolation. The behavior of pore pressure in the field has been derived from dynamic fluid flow simulation through history matching for the production and subsequent shut-down phases of the field. Subsequently, changes in the pore pressure field during injection-production and subsequent shut-down phases are analyzed for weekly and seasonal loading and unloading scenario cases. The resulting pore pressure changes are coupled with 3D geomechanical model in order to have complete understanding of stress changes during these operations. In two scenario cases, the surplus electricity in Germany from renewable energy sources such as solar and wind from the year 2017 is considered. It results that the German surplus electricity can be stored in underground gas storage facilities with a Power-to-Gas (PtG) concept and that the stored gas can be reused again. Additionally, fault reactivation and thermal stress analyses are also performed on THM model in order to evaluate maximum threshold (injection) pressure as well as safe storage capacity of the reservoir. The fault reactivation already occurs at 1.25 times the initial reservoir pressure which provides a safe storage rate of 100,000-150,000 m3/day in the case study reservoir. The validated THM model is ready to be used for analyzing new wells for future field development and testing further arbitrary injection-production schedules, among others. The methodology can be applied on to any UGS facility not only in German Molasse Basin but anywhere in the world

Achieving Secure and Efficient Cloud Search Services: Cross-Lingual Multi-Keyword Rank Search over Encrypted Cloud Data

Multi-user multi-keyword ranked search scheme in arbitrary language is a novel multi-keyword rank searchable encryption (MRSE) framework based on Paillier Cryptosystem with Threshold Decryption (PCTD). Compared to previous MRSE schemes constructed based on the k-nearest neighbor searcha-ble encryption (KNN-SE) algorithm, it can mitigate some draw-backs and achieve better performance in terms of functionality and efficiency. Additionally, it does not require a predefined keyword set and support keywords in arbitrary languages. However, due to the pattern of exact matching of keywords in the new MRSE scheme, multilingual search is limited to each language and cannot be searched across languages. In this pa-per, we propose a cross-lingual multi-keyword rank search (CLRSE) scheme which eliminates the barrier of languages and achieves semantic extension with using the Open Multilingual Wordnet. Our CLRSE scheme also realizes intelligent and per-sonalized search through flexible keyword and language prefer-ence settings. We evaluate the performance of our scheme in terms of security, functionality, precision and efficiency, via extensive experiments

Synergistic effect of hydrophilic nanoparticles and anionic surfactant on the stability and viscoelastic properties of oil in water (o/w) emulations; Application for enhanced oil recovery (EOR)

With the rapidly increased global energy demand, great attention has been focused on utilizing nanotechnology and particularly nanofluids in enhanced oil recovery (EOR) to produce more oil from low-productivity oil reservoirs. Nanofluid flooding has introduced as one of the promising methods for enhanced oil recovery using environment-friendly nanoparticles (NPs) to be as an innovative-alternative for chemical methods of EOR. This work investigates the synergistic effects of anionic surfactant and hydrophilic silica nanoparticles on the stability and the mechanical behavior of oil in water (O/W) emulsions for their application in EOR. To achieve this, an extensive series of experiments were conducted at a wide range of temperatures (23 – 70 °C) and ambient pressure to systematically evaluate the stability and the viscoelastic properties of the oil in water (O/W) emulsion with the presence of hydrophilic silica nanoparticles and an anionic surfactant. In this context, the initial oil to water volume ratio was 25:75. Sodium dodecylsulfate (SDS) was used as the anionic surfactant and n-decane was used as model oil. A wide concentration ranges of NPs (0.01 – 0.2 wt%) and surfactant (0.1 – 0.3 wt%) were used to formulate different emulsions. For stability measurements, a dynamic light scattering and zetasizer were used to measure the particle size distribution and zeta potential respectively. Creaming and phase behaviors were also investigated. The viscoelastic measurements were conducted using Discovery Hybrid Rheometer. Results show that in the presence of surfactant, and NPs mitigates the coalescence of dispersed oil droplets giving high promises in EOR applications. Further, over the tested range of temperatures, the viscosity of O/W emulsion remains stable which indicates thermal stability. Despite studies examining the use of nanoparticle-surfactant combination in sub-surface applications, no reported data is currently available, to the best of our knowledge, about the potential synergistic effect of this combination on the stability and viscoelastic properties of O/W emulsion. This study gives the first insight on nanoparticle-surfactant synergistic effect on oil in water (O/W) emulsion for EOR applications

Reversible and irreversible adsorption of bare and hybrid silica nanoparticles onto carbonate surface at reservoir condition

© 2020 Southwest Petroleum University Realistic implementation of nanofluids in subsurface projects including carbon geosequestration and enhanced oil recovery requires full understanding of nanoparticles (NPs) adsorption behaviour in the porous media. The physicochemical interactions between NPs and between the NP and the porous media grain surface control the adsorption behavior of NPs. This study investigates the reversible and irreversible adsorption of silica NPs onto oil-wet and water-wet carbonate surfaces at reservoir conditions. Each carbonate sample was treated with different concentrations of silica nanofluid to investigate NP adsorption in terms of nanoparticles initial size and hydrophobicity at different temperatures, and pressures. Aggregation behaviour and the reversibility of NP adsorption onto carbonate surfaces was measured using dynamic light scattering (DLS), scanning electron microscope (SEM) images, energy dispersive X-ray spectroscope (EDS), and atomic force microscope (AFM) measurement. Results show that the initial hydrophilicity of the NP and the carbonate rock surface can influence the NPs adsorption onto the rock surfaces. Typically, oppositely charged NP and rock surface are attracted to each other, forming a mono or multilayers of NPs on the rock. Operation conditions including pressure and temperature have shown minor influence on nano-treatment efficiency. Moreover, DLS measurement proved the impact of hydrophilicity on the stability and adsorption trend of NPs. This was also confirmed by SEM images. Further, AFM results indicated that a wide-ranging adsorption scenario of NPs on the carbonate surface exists. Similar results were obtained from the EDS measurements. This study thus gives the first insight into NPs adsorption onto carbonate surfaces at reservoirs conditions

Biopsy Proven Renal Morphology Cognizance into its Four-Year Evolving Pattern; A Pakistani Perspective

Objective: To determine the pattern of Biopsy Proven Renal Diseases (BPRD) in a single tertiary care centre in Islamabad, Pakistan. Study Design: Cross-sectional study. Place and Duration of Study: Department of Nephrology, KRL Hospital, Islamabad Pakistan, from Mar 2016 to Nov 2020. Methodology: The archival records of all native renal biopsies performed in adults (>18 years) were retrospectively analyzed.The biopsies were performed according to standard indications and evaluated by light microscopy and immunofluorescence. Results: A total of 134 renal biopsies were studied. Among these, 85(61.1 %) were males, and 49(36.5 %) were females. The mean age was 44.70±14.63 years. Primary glomerulonephritis’s were the predominant group of diseases found in 93(69.4%) cases. Membranous nephropathy (MN) was the most common lesion in 52(38.8%), followed by focal segmental Glomerulosclerosis (FSGS) in 22(16.4%) cases. Chronic tubulointerstitial nephritis (Ch. TIN) 12(9.0%) was the third most common lesion among all biopsies. Other diagnoses included lupus nephritis (LN) 10(7.5%) and IgA nephropathy (IgAN) 9(6.7%). One sample one-sided t-test was used to estimate the minimum proportion of occurrence of different biopsies in our concerned population. The estimated minimum proportion of membranous nephropathy (MN) was 0.31, with a p-value of 0.034. Conclusion: We concluded that primary Glomerulonephritis (PGN) is the most common renal disease, and membranous nephropathy is the most common biopsy-proven Glomerulopathy in our concerned population

DRPO : A Deep Learning Technique for Drug Response Prediction in Oncology Cell Lines

With the invention of high-throughput screening technologies, innumerable drug sensitivity data for thousands of cancer cell lines and hundreds of compounds have been produced. Computational analysis of these data has opened a new horizon in the development of novel anti-cancer drugs. Previous deep-learning approaches to predict drug sensitivity showed drawbacks due to the casual integration of genomic features of cell lines and compound chemical features. The challenges addressed include the intricate interplay of diverse molecular features, interpretability of complex deep learning models, and the optimization of drug combinations for synergistic effects. Through the utilization of normalized discounted cumulative gain (NDCG) and root mean squared error (RMSE) as evaluation metrics, the models aim to concurrently assess the ranking quality of recommended drugs and the accuracy of predicted drug responses. The integration of the DRPO model into cancer drug response prediction not only tackles these challenges but also holds promise in facilitating more effective, personalized, and targeted cancer therapies. This paper proposes a new deep learning model, DRPO, for efficient integration of genomic and compound features in predicting the half maximal inhibitory concentrations (IC50). First, matrix factorization is used to map the drug and cell line into latent ’pharmacogenomic’ space with cell line-specific predicted drug responses. Using these drug responses, we next obtained the essential drugs using a Normalized Discounted Cumulative Gain (NDCG) score. Finally, the essential drugs and genomic features are integrated to predict drug sensitivity using a deep model. Experimental results with RMSE 0.39 and NDCG 0.98 scores on Genomics of drug sensitivity in cancer (GDSC1) datasets show that our proposed approach has outperformed the previous approaches, including DeepDSC, CaDRRes, and KMBF. These good results show great potential to use our new model to discover novel anti-cancer drugs for precision medicine

A methodology to design multimodal public transit networks : procedures and applications

This thesis deals with public transit network design problem, one of the core issues facing the existing planning and operation of public transit systems. More specifically, this problem involves designing an efficient/optimal set of routes and their associated frequencies for public transit networks, given certain constraints. A novel route overlap-based approach is developed for this design undertake considering the perspectives of passengers and agencies. The concept of route overlap entails the ratio of common segments/arcs that each route shares with the remaining set of the routes of the transit network. Varying this ratio results with a variety of transit-network configurations such as a sparse network with lower ratios and dense network with higher ratios. The main part of the developed methodology consists of four major components. It starts with transit route creation and network construction where, at first, feasible routes are created by using the traditional k-shortest path algorithm, and then route overlap concept is used to design a public transit network. The second component is the analysis of assigning transit demand including headway derivation; this is used to assign demand onto the transit network and to generate travel times and suitable transit modes. The third component is the holistic evaluation of the transit network; this is done by using performance metrics to reflect the viewpoints of passengers, agencies and authorities. The last component is a metaheuristic search engine employed to explore feasible search space for attaining improved transit network configurations from both passengers and agencies perspectives. The new methodology undergone testing of different networks including benchmark and real-life networks. The outcomes of the experiments are used for the evaluation and validation of the proposed methodology including a comparison with other research studies. The results show that instead of using many overlapping redundant routes, it is more efficient and productive to have lesser number of routes with a reduced number of overlapping route segments; this will be more suitable and efficient for both passengers and agencies. Finally, the viability of the developed methodology is examined for the Singapore’s public transit network; the results suggest that this network can be optimised further with even lesser number of routes.Doctor of Philosoph