Evaluation of Hydrocarbon Producability Below FWL Onshore Abu Dhabi Oil Field Case Study

Objectives / Scope This paper addresses the field development planning challenges of a green onshore South East Abu Dhabi oil field with limited production data. Tectonic movements have created strike slip faults dissecting the structure and uplifting the main body. Tilting of the flanks has resulted in the accumulation to leak some of its initial hydrocarbon and a rebalancing showing a titled FWL. A novel workflow was used to address the challenging reservoir physics including hydrocarbon below FWL. The paper takes a holistic approach in integrating multiple domains data such as Drilling, Petrophysics, Geology and Reservoir / Production Engineering. Methods, Procedures, Process An integrated approach was adopted to address the complexity and challenges of characterizing and modelling the field with hydrocarbon below FWL. Extensive range of data was collected to contribute to better understanding and evaluation of the field. The producibility of hydrocarbon below FWL have a significant impact on field development planning. The used workflow was specifically suitable to drive subsurface team right reservoir characterization: Improve fluid contacts understanding Explain the log responses The discrepancies between dynamic and static responses De-risk the volumetric uncertainties Results Following an extensive multi-disciplinary technical analysis of all available datasets, the most robust, accurate and reliable reservoir characterization, that can be seamlessly integrated into dynamic reservoir modelling phase. A systematic approach was adopted starting from core measurement and lab visits, drilling data such as mud logs, Petrophysical evaluation of multiple complex physics such as hydrocarbon presence below FWL, micro porous intervals, Micritic minerals and imbibition effect, geological regional understanding of faulted reservoirs, and dynamic data such as formation well tests. The study demonstrated that multi-domain integration played a key role in addressing the complex and challenging reservoir dynamics. Novel / Additive Information Large subsurface uncertainty combined with an extensive domain integration required cutting-edge reservoir de-risking and data gathering to provide the optimal reservoir characterization. These unique workflows can be readily used in similar green fields and will be described in full details in the paper.

Frequent Gene Duplication/Loss Shapes Distinct Evolutionary Patterns of NLR Genes in Arecaceae Species

Nucleotide-binding leucine-rich repeat (NLR) genes play a key role in plant immune responses and have co-evolved with pathogens since the origin of green plants. Comparative genomic studies on the evolution of NLR genes have been carried out in several angiosperm lineages. However, most of these lineages come from the dicot clade. In this study, comparative analysis was performed on NLR genes from five Arecaceae species to trace the dynamic evolutionary pattern of the gene family during species speciation in this monocot lineage. The results showed that NLR genes from the genomes of Elaeis guineensis (262), Phoenix dactylifera (85), Daemonorops jenkinsiana (536), Cocos nucifera (135) and Calamus simplicifolius (399) are highly variable. Frequent domain loss and alien domain integration have occurred to shape the NLR protein structures. Phylogenetic analysis revealed that NLR genes from the five genomes were derived from dozens of ancestral genes. D. jenkinsiana and E. guineensis genomes have experienced “consistent expansion” of the ancestral NLR lineages, whereas a pattern of “first expansion and then contraction” of NLR genes was observed for P. dactylifera, C. nucifera and C. simplicifolius. The results suggest that rapid and dynamic gene content and structure variation have shaped the NLR profiles of Arecaceae species.

A genetically linked pair of NLR immune receptors show contrasting patterns of evolution

Throughout their evolution, plant nucleotide-binding leucine-rich-repeat receptors (NLRs) have acquired widely divergent unconventional integrated domains that enhance their ability to detect pathogen effectors. However, the functional dynamics that drive the evolution of NLRs with integrated domains (NLR-IDs) remain poorly understood. Here, we reconstructed the evolutionary history of an NLR locus prone to unconventional domain integration and experimentally tested hypotheses about the evolution of NLR-IDs. We show that the rice (Oryza sativa) NLR Pias recognizes the effector AVR-Pias of the blast fungal pathogen Magnaporthe oryzae. Pias consists of a functionally specialized NLR pair, the helper Pias-1 and the sensor Pias-2, and is allelic to the previously characterized Pia pair of NLRs: the helper RGA4 and the sensor RGA5. Remarkably, Pias-2 carries a C-terminal DUF761 domain at a similar position to the heavy metal–associated (HMA) domain of RGA5. Phylogenomic analysis showed that Pias-2/RGA5 sensor NLRs have undergone recurrent genomic recombination within the genus Oryza, resulting in up to six sequence-divergent domain integrations. Allelic NLRs with divergent functions have been maintained trans-species in different Oryza lineages to detect sequence-divergent pathogen effectors. By contrast, Pias-1 has retained its NLR helper activity throughout evolution and is capable of functioning together with the divergent sensor-NLR RGA5 to recognize AVR-Pia. These results suggest that opposite selective forces have driven the evolution of paired NLRs: highly dynamic domain integration events maintained by balancing selection for sensor NLRs, in sharp contrast to purifying selection and functional conservation of immune signaling for helper NLRs.Significance statementPlants have evolved sophisticated defense mechanisms to fend off pathogens. Plant nucleotide-binding leucine-rich repeat receptor (NLR) proteins play crucial roles in detecting pathogen molecules inside plant cells and mounting defense responses. Here, we identified the Pias gene from rice, which encodes the NLR pair Pias-1 “helper” and Pias-2 “sensor.” These proteins function together to detect the pathogen molecule AVR-Pias of Magnaporthe oryzae and defend against rice blast disease. Pias is allelic to the previously reported Pia gene. A comparison of Pias/Pia alleles among Oryza species showed that Pias/Pia helper is evolutionarily and functionally conserved, whereas Pias/Pia sensor shows highly dynamic evolution, with various host domains integrated into similar positions, allowing it to detect a wide variety of pathogen molecules.

Complex Event Processing Methods for Greenhouse Control

In the greenhouse environment, the application of complex event processing (CEP) technology can effectively tackle the problem of recognition of the complex patterns that appeared in greenhouse conditions. In the existing research, few scholars have proposed a scheme to integrate complicated scenes within the greenhouse environment with high efficiency, convenience, and low coupling. Therefore, in order to solve the problem of hard recognition and fusion of complex patterns in the greenhouse environment, based on the characteristics of the greenhouse, this paper proposes a complex event processing method for greenhouse control. Our method has high applicability and high expansibility, including 13 types of event processing agents and 21 types of typical events involved in greenhouse automatic control. This method has the advantages of low information coupling and multi-domain integration, which can be directly used by agricultural experts and related workers and is of great significance to promote the extensive application of CEP technology in the greenhouse field. Our experiment successfully realized a greenhouse intelligent control system based on CEP technology is successfully realized in our experiment. The experimental statistics shows that the structure of the control system was accessible and effective.

Grand challenges and emergent modes of convergence science

To address complex problems, scholars are increasingly faced with challenges of integrating diverse domains. We analyzed the evolution of this convergence paradigm in the ecosystem of brain science, a research frontier that provides a contemporary testbed for evaluating two modes of cross-domain integration: (a) cross-disciplinary collaboration among experts from academic departments associated with disparate disciplines; and (b) cross-topic knowledge recombination across distinct subject areas. We show that research involving both modes features a 16% citation premium relative to a mono-domain baseline. We further show that the cross-disciplinary mode is essential for integrating across large epistemic distances. Yet we find research utilizing cross-topic exploration alone—a convergence shortcut—to be growing in prevalence at roughly 3% per year, significantly outpacing the more essential cross-disciplinary convergence mode. By measuring shifts in the prevalence and impact of different convergence modes in the 5-year intervals up to and after 2013, we find that shortcut patterns may relate to competitive pressures associated with Human Brain funding initiatives launched that year. Without policy adjustments, flagship funding programs may unintentionally incentivize suboptimal integration patterns, thereby undercutting convergence science’s potential in tackling grand challenges.

The Ape Lottery: Chimpanzees Fail To Consider Spatial Information When Drawing Statistical Inferences

Humans and nonhuman great apes share a sense for intuitive statistics, making intuitive probability judgments based on proportional information. This ability is of tremendous importance, in particular for predicting the outcome of events using prior information and for inferring general regularities from limited numbers of observations. Already in infancy, humans functionally integrate intuitive statistics with other cognitive domains, rendering this type of reasoning a powerful tool to make rational decisions in a variety of contexts. Recent research suggests that chimpanzees are capable of one type of such cross-domain integration: The integration of statistical and social information. Here, we investigated whether apes can also integrate physical information into their statistical inferences. We tested 14 sanctuary-living chimpanzees in a new task setup consisting of two “gumball machine”-apparatuses that were filled with different combinations of preferred and non-preferred food items. In four test conditions, subjects decided which of two apparatuses they wanted to operate to receive a random sample, while we varied both the proportional composition of the food items as well as their spatial configuration above and below a barrier. To receive the more favorable sample, apes needed to integrate proportional and spatial information. Chimpanzees succeeded in conditions in which we provided them either with proportional information or spatial information, but they failed to correctly integrate both types of information when they were in conflict. Whether these limitations in chimpanzees' performance reflect true limits of cognitive competence or merely performance limitations due to accessory task demands is still an open question.

Practical Applications of Diffusive Realization of Fractional Integrator with SoftFrac

Fractional calculus has found multiple applications around the world. It is especially prevalent in the domains of control and electronics. One of the key elements of fractional applications is the fractional integral (or integrator) which is a backbone of famous PIλD controller. It gives advantages of traditional PID with a limited phase lag. The are, however, issues with implementation, which will allow good low-frequency behavior. In this paper, we consider a diffusive realization of a fractional integrator with the use of quadratures. We implemented this method in numerical package SoftFrac, and we illustrate how different quadratures work for this purpose. We show superiority of bounded domain integration with logarithmic transformation and explain issues with behavior for extremely low frequencies.