bluuebunny/crossref_metadata_2025_split huggingface dataset:
Technology Focus: Completions (April 2023)
Authors: Pedroso Carlos Alberto 
Published: May 2023

_
A few years ago, exaggerating a figure of speech, I coined the expression, “We work in a water industry; oil is just a supporting actor.” In fact, producing water is the final destination of every oil field, but obviously the objective was to highlight the importance of water management in the hydrocarbon-production process. Since the beginning, well completion and stimulation strategies have always sought to optimize water management and, extending the concept, management of all fluids produced.
Selective completions in vertical and multilateral wells (combined with sand control or not) and segmentation of horizontal wells are good examples of this effort. These technologies, combined with intelligent completion, have brought very positive results. Intelligent completion in open hole, currently used in the Brazilian presalt, has allowed the reduction of the carbon footprint, so necessary nowadays. The optimal application of intelligent completion is the subject of paper SPE 209965, an excellent compendium of best practices on the topic.
In recent years, driven by technological advances, good results, and the need to increase operational efficiency, we have experienced a major increase in the use of devices for flow equalization and selective fluid production [e.g., inflow control devices, autonomous inflow control devices (AICDs), and autonomous inflow control valves]. A significant reduction in water and gas production has been reported in several technical papers. In fact, this is the most published completion topic in the past 3 years, and many good articles are available.
Apart from new and exciting trends, conventional solutions continue to play their important role. Basic themes long discussed continue to evolve and bring new insights. This is the case with paper SPE 204185, which deals with perforating for fracturing.
Finally, paper SPE 210417 discusses the use of AICD technology in the Singue Field in Ecuador, which resulted in an increase in cumulative oil rate and a decrease in cumulative water.

Recommended additional reading
SPE 210205 Successful Autonomous Inflow-Control Device Application To Reduce Water Production in a Heavy Oil Greenfield by Carlos A. Pedroso, Enauta, et al.
SPE 206213 Automated Well Production and Gas Lift System Diagnostics and Optimization Using an Augmented Artificial Intelligence Approach in a Complex Offshore Field by Mahmoud Elwan, Dragon Oil, et al.
OTC 31483 Case Study: How the Newest Generation of Autonomous Inflow Control Devices Helps To Control Excessive Well-Water Production Within a Major Sultanate of Oman Oilfield by Salim Buwauqi, Petroleum Development Oman, et al.
Technology Focus: Heavy Oil (April 2023)
Authors: Izadi Mehdi 
Published: May 2023

_
Heavy oils are characterized by high-density, high-viscosity, and high-heavy-fraction components. One of the more common characteristics of heavy oil reservoirs is a low primary recovery factor, which is mainly because of unfavorable mobility ratios between oil and water, negligible solution drives, and faster decline of reservoir pressures because of relatively low oil compressibility. Most of the technologies that apply to heavy oil reservoirs need to address the mobility ratio or viscous forces before any flooding. Given the complex nature of heavy oil reservoirs, paper SPE 209328 present a comprehensive review of the latest technologies and work flows developed for heavy oil reservoir management.
Although secondary oil production methods such as waterflooding, water-alternating-gas injection, and gas drive might not be efficient in recovery from heavy oil reservoirs, the applicability and success of these methods can be analyzed on a reservoir basis.
The offtake and throughput management philosophy for heavy oil waterflooding is not the same as for classical light oil, and it could play a major role in recovery efficiency. The key point is to understand the nature of heavy oil and reservoir heterogeneity regarding their fluid and rock properties. Recent technology advances in hardware and software have enabled the industry with tools that allow achieving full-field high-resolution simulation modeling; therefore, performing a realistic reservoir simulation is achievable. This might decrease the loss of millions of dollars before field applications. Paper SPE 210185 presents an example of taking advantage of recent simulation technologies and presents a method and work flow under subsurface uncertainty for a large heavy oil field with complex geological characteristics, a long production history, and many wells. The work-flow design is applicable for other fields with similar characteristics and delivery objectives.
Paper SPE 203783 discusses the results of a recovery-optimization study conducted in a heavy oil field in south Oman aimed at improving the understanding of deep bottom-up water injection.

Recommended additional reading
SPE 205449 First Successful Controlled Dumpflood in Deepwater Gulf of Mexico Results in Promising Incremental Rate and Recovery by Bilal A. Hakim, Talos Energy, et al.
URTeC 2021-5077 Heavy Oil Polymer Enhanced Oil Recovery in the Challenging Alaskan Arctic—It Works! by A. Dandekar, University of Alaska Fairbanks, et al.
SPE 212387 Evaluation of the Oil Recovery and Economic Benefit of the First-Ever Polymer flood Field Pilot To Enhance the Recovery of Heavy Oils on Alaska’s North Slope Using Machine-Assisted Reservoir Simulation by Cody Douglas Keith, University of Alaska Fairbanks
Technology Focus: History Matching and Forecasting (April 2023)
Authors: Nalla Gopi 
Published: May 2023

_
This year’s history matching and forecasting selections cover topics that include performance prediction of a polymerflood pilot in a heavy oil reservoir, multivariate characterization and modeling of a paleo zone, and multiwell pressure history matching in an unconventional reservoir.
The authors of paper SPE 206247 developed a history-matched reservoir simulation model for a polymerflood pilot to enhance heavy oil recovery on the Alaska North Slope. According to the authors, the viscous fingering effect in the reservoir during waterflooding and the restoration of injection conformance during polymerflooding were effectively represented. Using the history-matched model, studies were conducted to investigate the oil-recovery performance under different development strategies, with consideration for sensitivity to polymer parameter uncertainties.
In paper IPTC 22034, the authors present an approach that incorporates characterization of paleo zones, parameterization of paleo-zone conductivity, and application of flow profiles as a guide in the history-matching study of a conceptual reservoir simulation model under reservoir uncertainty. The authors conclude that the modeled paleo zone acts as a baffle inducing a partial confinement of the injectors.
Solving a global optimization problem to minimize the mismatch between the instantaneous shut-in pressure and treatment pressures measured in the field and simulated by the model, for all stages and all wells, is the subject of paper SPE 204162. According to the authors, the technique reduces overfitting by minimizing the number of variables used for history matching, by increasing the number of wells and stages that are simultaneously matched, and by reproducing the dominant behavior of all stages instead of capturing the detailed behavior of specific stages.

Recommended additional reading
SPE 210224 Rate-Transient-Analysis-Assisted Numerical History Matching and Co-Optimization of CO2 Storage and Huff ’n’ Puff Performance for a Near-Critical Gas Condensate Shale Well by Hamidreza Hamdi, University of Calgary, et al.
URTeC 3724391 Fractional Dimension Rate-Transient Analysis in Unconventional Wells: Application in Multiphase Analysis, History Matching, Forecasting, and Interference Evaluation by Behnam Zanganeh, Chevron, et al.
SPE 200908 Application of an Integrated Ensemble-Based History-Matching Approach—An Offshore Field Case Study by Usman Aslam, Emerson, et al.
Identification of Key Ischemic Stroke Genes by Computational Systems Biology
Authors: Yue Rongting , Dutta Abhishek 
Published: May 2023

Abstract
Stroke is one of the leading causes of death in humans. Even if patients survive from stroke, they may suffer sequelae such as disability. Treatment for strokes remains unsatisfactory due to an incomplete understanding of its mechanisms. This study investigates Ischemic Stroke (IS), a primary subtype of stroke, through gene differential expressions (Limma) and Weighted Gene Co-expression Network Analysis (WGCNA) based on RNA-seq data from GSE137482 (GEO database). A protein-protein interaction network was constructed to identify 112 hub genes among 1,117 differentially expressed genes (DEGs). Gene co-expression patterns were obtained to cluster genes, and the most disease-related gene cluster with 179 hub genes was identified. The intersection of hub gene sets based on DEGs and co-expressions results in a total of 7 common hub genes, including Actb, Itgb1, Stat3, Fn1, Pecam1, Kdr and Csf1r. Based on our analyses, we hypothesize that these hubs might play a key role in the onset and progression of IS. The result suggests the potential of identifying the unexplored key regulators by the systemic method used in this work.
Finite element and regression analysis for improving Terzaghi’s, Meyerhof’s, Hansen’s and Vesic’s bearing capacity methods
Authors: Pantelidis Lysandros 
Published: May 2023

Abstract

The bearing capacity of shallow foundations is one of the major fields in soil mechanics and foundation engineering. Since 1943, when Terzaghi suggested his famous equation with the three N-factors, several dozens of bearing capacity methods for centrically-vertically loaded strip footings have been proposed in the literature, mainly focusing on refining the Nγ factor. However, the methods proposed by Terzaghi, Meyerhof, Hansen and Vesic remain by far the most popular ones, with the last one to be component of many contemporary design codes. In this paper, the author put these four methods into extensive comparison against the finite element method. A common characteristic of the results is the great scatter of values, while none of these methods seems to be superior to the others. The regression analysis carried out by the author, however, clearly showed that it is not Nγ that prevents us from having a reliable analytical prediction for the bearing capacity of (strip) footings. Instead, all methods need a) to get rid of all original depth factors and b) calibration against the Df⁄B parameter (use of the same parameter for all three N-factors). In this respect, the author calibrated these methods applying (both non-linear and linear) regression analysis. Generally, the performance of all these methods was significantly improved after using a suitably calibrated term of the form (const_1+ const_2∙Df⁄B), as multiplier in front of the original, but naked from the depth factors, bearing capacity formula. Finally, the author offered corrected expressions for the four methods mentioned above, not only for the case of rigid footings but also for the case of flexible footings, as regression analysis was also carried out against the respective finite element results. In addition to the above it is mentioned that, in the calibration procedure against finite element analysis, the author considered the non-associated flow rule (ψ=0°). As known, an analysis with a non-associated flow rule is more representative of the actual soil behavior, while it is on the safe side.
Expression and biogenesis analysis of 21-nt and 24-nt phased small interfering RNAs in maize tissues
Authors: Yu Shuai , Dang Dongdong , Yin Yanzhe , Dai Yuxin , Zhang Xiaoyu , Sun Yixuan , Dong Xiaomei , Ruan Yanye 
Published: May 2023

Abstract
Phased secondary small interfering RNAs (phasiRNAs) are generated by double-stranded RNA (dsRNAs), which are cleaved by Dicer as a phase set. So far, hundreds of 21-nt and 24-nt phasiRNAs have been identified in male reproductive organs of rice and maize. Whereas, the expression profile of phasiRNAs among maize tissues is still exclusive. In this study, to identify 21-nt and 24-nt phasiRNAs among tissues, about 600 million signatures from nine tissues were got including root, seedling, embryo, pollen, anther, immature tassel, immature ear, premature ear and silk. As a result, 269 and 135 21-nt and 24-nt PHAS (phasiRNA precursors) loci were identified, respectively. Interestingly, except male tissues, 21-nt and 24-nt phasiRNAs also were identified in immature ear and silk, respectively, which highly overlapped with those characterized in male reproductive organs. But few phasiRNAs were identified in root, seedling and embryo. 93.4% and 81.3% of 21-nt and 24-nt PHAS loci contained 22-nt motif which matched well with miR2118 and miR2275, respectively. The expression levels of miR2118 and miR2275 in tissues accorded well with that of phasiRNAs. Finally, we found that DCL1 might be the direct Dicer nuclease to promote the maturation of 22-nt miR2118 and miR2275 in maize, because the levels of miR2118 and miR2275 were reduced in seedling and tassel primordia of dcl1 mutants. We provided profiling information of 21-nt and 24-nt phasiRNAs among tissues in maize. It could be helpful to understand the biogenesis of phasiRNAs in maize.
Aspirin attenuates hepatocellular carcinoma progression by inhibiting PD-L1 expression
Authors: Wang Yi-kai , Wang Mu-qi , Liu Chen-rui , Hao Miao , Wang Wen-jun , Li Ya-ping , Shi Juan-juan , Zhang Xin , Dang Shuang-suo 
Published: May 2023

Abstract
Background and aims:
Aspirin, as a widely used anti-inflammatory drug, has been shown to exert anti-cancer effects in a variety of cancers. PD-L1 is widely expressed in tumor cells and inhibits anti-tumor immunity. This study aims to clarify whether aspirin exerts its anti-HCC effect by inhibiting PD-L1 expression.
Methods
Thirty male SD rats were randomly divided into the DEN, DEN + ASA, ASA, and control groups. The rats in the DEN and DEN + ASA groups were fed with 0.01% diethylnitrosamine freely to establish a liver cancer model. Rats in the DEN + ASA and ASA groups were treated with aspirin by gavage. The expression of PD-L1 in the liver was detected by Western Blot.
Results
The tumor number and liver weight ratio in the DEN + ASA group were significantly lower than those in the DEN group (P = 0.006, P = 0.046). Biochemical indexes showed that there were significant differences in all indexes between the DEN and control group (P < 0.05). The levels of DBIL, ALP, and TT in the DEN + ASA group were significantly lower than those in the DEN group (P = 0.038, P = 0.042, P = 0.031). In the DEN group, there was an obvious fibrous capsule around the tumor, and the portal vein was dilated. The pathological changes were mild in the DEN + ASA group. The expression of PD-L1 in the DEN group was significantly higher than that in the other three groups (P < 0.05); Aspirin could significantly inhibit the expression of PD-L1 in liver cancer tissues (P = 0.0495).
Conclusions
Aspirin can inhibit the progression of hepatocellular carcinoma and reduce tumor burden by targeting PD-L1.
Sensation-seeking traits influence risk adjustment through reward sensitivity during dynamic risky decisions
Authors: Yin Qianlan , Dong Wei , Hou Tianya , Cai Wenpeng , Deng Guanghui 
Published: May 2023

Abstract
Objective
A prominent question has emerged about how individual differences shape the way decision results drive subsequent risk-taking behavior. Sensation seeking (SS) and reward sensitivity (RS) are important pathological personalities for behavioral disorders such as gamble and material addictive disorders. However, previous studies have shown behavioral heterogeneity is a pervasive feature of risk-taking and decision-making, yet a neural trait approach can at least partially explain the heterogeneity in behavior by stable brain-based characteristics of individuals. Hence, to study their impacts on reward-driven risk-taking behaviors, we combined the Reinforcement Learning (RL) model and the neural measure of a dynamic risky decision task to explore the relationship between SS, RS, and risk adjustment (RA) to rewards.
Methods
A task characterized by the unknown but ordered risk was designed to quantify the RA with the RL model and adapted from the Balloon Analog Risk Task. In Study 1, 43 young participants completed the task; in Study 2, 37 young participants finished the task while wearing an electroencephalography device. The recorded behavioral data and EEG signal were analyzed using the computational model, event-related potentials and spectral perturbations analysis, and bayesian multi-model linear regression.
Results
Results of Study 1 showed the choice deviations were larger in the higher SS participants with a lower level of RS. Meanwhile, results from Event-related potential and Time-frequency analysis of Study 2 showed higher SS participants were less sensitive to the reward feedback. Based on Study 1 and Study 2, Bayesian multi-model linear regression showed the saliently direct effect of RS on RA and the moderating effect of SS.
Conclusions
SS might indirectly relate to RA through RS. In conclusion, RS impacts the entire process of reward prediction and learning and is vital for intervening in risky behaviors, especially in individuals with high SS.
Effect of Natural Gas Usage as Fuel in Gasoline- Based Vehicle Using 100% Substitution Method
Authors: Syafrinaldy Ade , Saputro Frendy Rian , Muharto Bambang , Anggoro Trisno , Masfuri Imron , - Siswanto , Rosyadi Erlan , Nolandy Henry , Sugeng Dhani Avianto 
Published: May 2023

Abstract
This study performed a comparative experimental analysis of vehicle performance, fueled with domestic gasoline and compressed natural gas (CNG). A passenger vehicle, with 1.5 L, 4-cylinder gasoline engine was used as tool through a road test, dyno test and idling emission measurement. The 100% substitution method was applied in all experiments. The road test covered the distance of Serpong-Bandung (Route I, 237 km) and Bandung-Jakarta (Route II, 142.4 km). In general, this study revealed that all engine performance parameters decreased by CNG usage, but CNG has advantages over gasoline, including considerable emission and cost reductions. In term of fuel economy, gasoline was more efficient by covering longer distance per liter fuel, but CNG gave almost 50% fuel cost cutting per kilometer due to price difference. CNG came 13–20% short in term of engine output torque and horsepower, most likely because incomplete gas combustion in the engine cylinder. CNG emitted lower carbon oxides, but higher nitrogen oxides and hydrocarbons. The slow CNG flame speed seemingly caused relatively high proportion of unburnt methane. Interesting fact was that CNG performed better on rougher Route I than on Route II, demonstrated the suitability of CNG to heavy-duty vehicles.
Targeted Next-Generation Sequencing with MultiPrime A Reliable and Efficient Tool for Global Pathogen Detection
Authors: Yang Junbo 
Published: May 2023

Abstract
Targeted next-generation sequencing (tNGS) offers a cost-effective option for pathogen diagnostics and therapeutics. Nevertheless, the design of multiplex primers constrains its wide-spectrum target coverage. Herein, we present multiPrime, which enables a more extensive coverage of pathogen spectra while maintaining high multiplexing compatibility. Our study demonstrates that multiPrime outperforms DegePrime with fewer primers and greater coverage on 8 viruses. Furthermore, multiPrime-designed primers successfully amplified all target amplicons across these viruses. We utilized multiPrime to design primers for a tNGS panel that covers 30 viruses, validating the panel's performance in 80 clinical specimens, achieving 94% sensitivity and 89% specificity.
Optimizing Interfacial Interaction between Cu and Metal Oxides Boosts Methanol Yield in CO 2 Hydrogenation
Authors: Li Xiaojun , Xia Yudong , Xu Yangzhou , Li Haitao , Huang Jiangchao , Yao Jie , Zhao Heng , Gao Xinhua , Yu Jinghao , Guo Lisheng , Li Jie 
Published: May 2023

Abstract
Thermocatalytic conversion of redundant CO2 to useful methanol is an attractive route to address both energy and environmental crises simultaneously. However, existing copper/oxide catalysts widely used in these thermocatalytic processes still suffer from low methanol yield under mild reaction conditions. In this work, we design inverse oxide/Cu catalysts to achieve superior thermal catalytic performance for CO2 hydrogenation. The optimized ZnO/Cu-1.0 catalyst exhibits maximum CH3OH selectivity of 83.4% and space-time yield (STY) of 170.9 gCH3OH·kgcat−1·h− 1 in CO2 hydrogenation at 210°C, nearly 2-fold higher STY than the previous optimal inverse ZnO/Cu catalysts (89.6 gCH3OH·kgcat−1·h− 1 at 250°C). Importantly, ZnO/Cu-1.0 catalyst displayed not only a satisfactory catalytic stability but also a superior CH3OH STY with a time-on-stream of 24 h. Such inverse configuration of catalysts will pave the way for new strategies to design high-performance thermocatalytic catalysts and promote their commercialization.
Long and accurate PCR (LA-PCR) detection of azole-resistant Trichophyton indotineae
Authors: Yamada Tsuyoshi , Kano Rui 
Published: May 2023

Abstract
Trichophyton indotineae, a species newly designated in 2020 independent of T. interdigitale, comprises highly terbinafine (TRF)-resistant dermatophytosis that is epidemic in North India and spreading worldwide. Some clinical isolates of T. indotineae have shown resistance to both TRF and azoles that might have been related to treatment failure. To detect the azole-resistant strains, we developed a long and accurate polymerase chain reaction (LA-PCR) method for detecting the tandem repeat of CYP51B (encoding sterol 14α-demethylase gene) in T. indotineae. Comparing the drug susceptibility test results with the LA-PCR results confirmed a trend toward low susceptibility to azole antifungal agents in strains with amplifications of 9.5 kbp or greater (3 or more copies of CYP51B). Our results suggest that the method could rapidly detect strains with low susceptibility to azole antifungal agents.
Differential abundance of lipids and metabolites related to SARS- CoV-2 infection and susceptibility
Authors: Albóniga Oihane E. , Olmo Elena Moreno del , Martínez-Sanz Javier , Vizcarra Pilar , Ron Raquel , Diaz Jorge , Rosas Marta , Sánchez-Conde Matilde , Galán Juan Carlos , Angulo Santiago , Moreno Santiago , Barbas Coral , Serrano-Villar Sergio 
Published: May 2023

Abstract
Background: The mechanisms driving SARS-CoV-2 susceptibility remain poorly understood, especially the factors determining why unvaccinated individuals remain uninfected despite high-risk exposures.
Objective: to understand lipid and metabolite profiles related with COVID-19 susceptibility and disease progression.
Methods: we collected samples from an exceptional group of unvaccinated healthcare workers heavily exposed to SARS-CoV-2 but not infected (‘non-susceptible’) and subjects who became infected during the follow-up (‘susceptible’), including non-hospitalized and hospitalized patients with different disease severity providing samples at early disease stages. Then, we analyzed their plasma metabolomic profiles using mass spectrometry coupled with liquid and gas chromatography.
Results:we show specific lipids profiles and metabolites that could explain SARS-CoV-2 susceptibility and COVID-19 severity. More importantly, non-susceptible individuals show a unique lipidomic pattern characterized by the upregulation of most lipids, especially ceramides and sphingomyelin, which could be interpreted as markers of low susceptibility to SARS-CoV-2 infection.
Conclusion: this study strengthens the findings of other researchers about the importance of studying lipid profiles as relevant markers of SARS-CoV-2 pathogenesis.