Fluid phase behavior of tight and shale reservoirs: Monte Carlo simulations

Tight and shale reservoirs are forming important components of the global hydrocarbon landscape, which impede the free thermal movement of fluid molecules, with numerous nanoscale pores. The confined hydrocarbons in the nanopores cannot be industrially produced from conventional exploration and development methods, with deviated fluid phase behavior under nano-confinement effects. Most commonly important fluid phase behavior in nanopores has been simulated and compared with the bulk cases previously, including phase coexistence, critical properties, and density distribution of confined fluids. This paper focuses on the deviated fluid phase behavior under nano-confinement effects by Monte Carlo modeling. The Monte Carlo simulation is still limited to modeling the macroscopic pore-related behavior like capillarity and complex fluid and solid materials. Moreover, the Monte Carlo simulation is usually scale-restricted and the pore-size range where the nano-confinement effect fails to work needs to be quantitatively determined. Overall, for the tight and shale fluid phase behavior, a functional Monte Carlo model, coupled with the long-range correction and configuration bias techniques, is suggested to include both the multi-component fluids and skeleton.Cited as: Chu, W., Zhang, K. Fluid phase behavior of tight and shale reservoirs: Monte Carlo simulations. Advances in Geo-Energy Research, 2023, 7(2): 132-135. https://doi.org/10.46690/ager.2023.02.0

The Saccharomyces cerevisiae acetyltransferase Gcn5 exerts antagonistic pleiotropic effects on chronological ageing.

ACKNOWLEDGMENTS We would like to thank Dr Juan Mata for his help with the transcriptome analysis. The Flow cytometry facility of the School of Biological Sciences is thanked for their excellent help in FACS analysis FUNDING The metabolomics study was funded by the MRC Programme in Lipid Profiling and Signalling (MC_UP_A090_1006) to JLG. NZ is grateful to the Wellcome Trust and the University of Cambridge for support and facilities.Peer reviewedPublisher PD

Indium-Containing Visible-Light-Driven (VLD) Photocatalysts for Solar Energy Conversion and Environment Remediation

Indium-containing visible-light-driven (VLD) photocatalysts including indium-containing oxides, indium-containing sulfides, indium-containing hydroxides, and other categories have attracted more attention due to their high catalytic activities for oxidation and reduction ability under visible light irradiation. This chapter will therefore concentrate on indium-containing nano-structured materials that demonstrate useful activity under solar excitation in fields concerned with the elimination of pollutants, partial oxidation and the vaporization of chemical compounds, water splitting, and CO2 reduction processes. The indium-containing photocatalysts can extend the light absorption range and improve the photocatalytic activity by doping, heterogeneous structures, load promoter, and morphology regulation. A number of synthetic and modification techniques for adjusting the band structure to harvest visible light and improve the charge separation in photocatalysis are discussed. In this chapter, preparation, properties, and potential applications of indium-containing nano-structured materials used as photocatalysis will be systematically summarized, which is beneficial for understanding the mechanism and developing the potential applications

Unsupervised Multi-document Summarization with Holistic Inference

Multi-document summarization aims to obtain core information from a collection of documents written on the same topic. This paper proposes a new holistic framework for unsupervised multi-document extractive summarization. Our method incorporates the holistic beam search inference method associated with the holistic measurements, named Subset Representative Index (SRI). SRI balances the importance and diversity of a subset of sentences from the source documents and can be calculated in unsupervised and adaptive manners. To demonstrate the effectiveness of our method, we conduct extensive experiments on both small and large-scale multi-document summarization datasets under both unsupervised and adaptive settings. The proposed method outperforms strong baselines by a significant margin, as indicated by the resulting ROUGE scores and diversity measures. Our findings also suggest that diversity is essential for improving multi-document summary performance.Comment: Findings of IJCNLP-AACL 202

PIVOINE: Instruction Tuning for Open-world Information Extraction

We consider the problem of Open-world Information Extraction (Open-world IE), which extracts comprehensive entity profiles from unstructured texts. Different from the conventional closed-world setting of Information Extraction (IE), Open-world IE considers a more general situation where entities and relations could be beyond a predefined ontology. More importantly, we seek to develop a large language model (LLM) that is able to perform Open-world IE to extract desirable entity profiles characterized by (possibly fine-grained) natural language instructions. We achieve this by finetuning LLMs using instruction tuning. In particular, we construct INSTRUCTOPENWIKI, a substantial instruction tuning dataset for Open-world IE enriched with a comprehensive corpus, extensive annotations, and diverse instructions. We finetune the pretrained BLOOM models on INSTRUCTOPENWIKI and obtain PIVOINE, an LLM for Open-world IE with strong instruction-following capabilities. Our experiments demonstrate that PIVOINE significantly outperforms traditional closed-world methods and other LLM baselines, displaying impressive generalization capabilities on both unseen instructions and out-of-ontology cases. Consequently, PIVOINE emerges as a promising solution to tackle the open-world challenge in IE effectively