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The 2019–21 drought in southern Madagascar
Get PDFTwo consecutive failed rainy seasons in the southern part of Madagascar in 2019–21 had devastating impacts on the population, including an amplification of the ongoing food insecurity in the area. The drought events were second in severity only to the 1990–92 drought and were estimated in a previous study to have a return period of 135 years. In this study, the physical mechanisms that led to these consecutive drought events are investigated. We found that the anomalously cold sea surface temperatures (SSTs) that persisted to the south of Madagascar between December 2019 and December 2020 led to a decrease in the transport of moist air over land. These cold SST anomalies were the most negative anomalies in the past four decades and intensified the rainfall deficit resulting from a negative Subtropical Indian Ocean Dipole (SIOD) mode during the rainy season of December 2019 to March 2020 and during December 2020. We also found that the rainfall response to the SST anomaly south of Madagascar was three times greater than that of a canonical SIOD. A weak Mozambique Channel Trough and a strong Angola low system, on the other hand, modulated the expected above-normal rainfall from a La Niña event in January–February 2021. Our study demonstrates how local factors can modulate the impacts of large-scale drivers, and that both local and global drivers, and their interactions, should be considered when producing seasonal forecasts and advisories, as well as climate change adaptation and mitigation plans for southern Madagascar.publishedVersio
FishIR: Identifying Pufferfish Individual Based on Deep Learning and Face Recognition
Get PDFPufferfish, globally recognized for its distinctive delicacy, carries high culinary value. However, it is also notorious for the lethal toxicity, and there is a great demand for traceability measures in the commercial trade of pufferfish to assure safety and accountability. This research introduces a novel deep learning approach, utilizing facial recognition techniques, to identify pufferfish individuals. This method specifically leverages distinctive back skin texture patterns as key biological traits. Our initial step involved assembling a collection of annotated and augmented images of Takifugu bimaculatus, a species of pufferfish native to East China Sea, which is accessible upon request. We then extensively investigated fundamental components of Deep Face Recognition (deep FR) systems, focusing on segmentation and extraction models, and assessed their effectiveness in identifying pufferfish. Following this, we developed FishIR (Fish Individual Recognition), a framework to identify pufferfish individuals that consists of four deep FR stages while incorporating enhanced segmentation and feature extraction techniques. Experimental results show that this framework successfully captures unique representations of individual pufferfish, as verified by the high accuracy achieved in recognition tasks.publishedVersio
A General Framework to Describe Drilling Process States
Get PDFAutomation and digitalization of drilling require shared knowledge about the state of the drilling process: Is the bit on-bottom drilling or is the string in-slips; is there an overpull or is there a formation fluid influx? The research question addressed here is whether it is possible to define clear, sharable, and usable definitions of what a drilling process state is, and an agreed method to calculate it. The method to define the drilling process state originates from the fact that a drilling operation can be described by a set of partial differential equations, respecting boundary conditions. Therefore, the set of possible discrete changes of boundary conditions defines the set of all possible drilling process states. The possible state values for each of these boundary conditions can be clearly defined by a set of logical expressions utilizing boundary values at the partial differential equations. Each boundary condition is called a microstate. If the set of microstates is linearly independent and complete, then the overall state of the drilling process is uniquely described by the state of each of the microstates. The boundary values are either measured or estimated using a digital twin of the drilling process. In either case, an uncertainty is associated with the boundary value. It is therefore possible to estimate the probability of being in one state or another for each of the microstates. This is an important property as often the actual state of the drilling process is uncertain. If several digital twins or measurements are available, it is also possible to use sensor fusion to update the uncertainty of the boundary value. A common drilling process interpretation engine and well-defined drilling process states may help with the coordination of multiple advisors participating in the control of the drilling process. An example is given showing how an event-based drill-a-stand procedure involving several external advisors is automatically executed using a common source for the interpretation of the drilling process state. A shared definition and method of calculation of the drilling process state is a fundamental element of an infrastructure to enable interoperability at the rigsite. This work is part of the Drilling and Wells Interoperability Standard (D-WIS) initiative. D-WIS is a cross-industry work group providing the industry with solutions facilitating interoperability of computer systems at the rigsite.A General Framework to Describe Drilling Process StatespublishedVersio
Better stopping through cross validation in an iterative ensemble smoother: A perspective from supervised machine learning
Get PDFIterative ensemble smoothers (IES) are among the popular reservoir data assimilation (RDA) algorithms for reservoir characterization. The actual deployment of an IES algorithm requires implementing certain stopping criteria, normally adopted for runtime control (e.g., by stopping the IES when it reaches the maximum number of iterations) and/or safeguarding the RDA performance (e.g., by preventing the simulated data from overfitting the actual observations). In practice, for various reasons, it is often challenging for existing stopping criteria to simultaneously achieve both purposes. One noticeable issue, as illustrated in this work, is that in many situations, the qualities of the estimated reservoir models may already start to deteriorate before a conventional stopping criterion activates to terminate the iteration process. Following this observation, one practically important question arises: Is it possible to further improve the efficacy of the IES algorithm by designing a different stopping criterion so that the IES can stop earlier, saving computational costs while achieving better RDA performance? As one of the rare attempts in the community, this work aims to investigate the use of a new IES stopping criterion that has the potential to provide an affirmative answer to the above question. In this regard, our main idea is based on the concept of cross validation (CV), routinely adopted in supervised machine learning (SML) problems for early stopping to prevent SML models from overfitting the training data. Despite the noticed similarities between RDA and SML problems, some fundamental differences exist, making it fail to work well if one directly extends a vanilla CV procedure from SML to RDA. To tackle this identified challenge, we design an efficient CV procedure tailored for RDA problems, and inspect the performance of an IES algorithm equipped with this CV procedure (IES-CV) in both synthetic and real field case studies. Our numerical investigation indicates that the IES-CV algorithm achieves promising RDA performance in all case studies, confirming the possibility that with the aid of a proper stopping criterion, an IES algorithm can terminate at an appropriate iteration step with near-optimal RDA performance. Beyond these numerical findings, it is also our hope that the current work may help improve the best practices of applying IES to RDA problems, taking advantage of the effective, CV-based stopping criterion.Better stopping through cross validation in an iterative ensemble smoother: A perspective from supervised machine learningpublishedVersio
Dynamics of HPAM flow and injectivity in sandstone porous media
Get PDFPolymer flooding is a prominent chemical enhanced oil recovery (CEOR) method that involves the injection of polymer solution into the oil reservoirs to improve the sweep efficiency and maximize the ultimate oil recovery. Selecting an appropriate polymer type, molecular weight, and concentration is crucial for success of any polymer flooding project. This paper studies the flow behavior of HPAM-based EOR polymers with different molecular weights through porous media. Dynamic adsorption and injectivity tests were performed through 5 Darcies sandpacks using polymer solutions prepared with low (LM; 8–10 MDa) and high (HM; 20–25 MDa) molecular weight polymers. Polymer solutions with different target viscosity values of 7, 15 and 30 cP were flooded through sandpacks at the reservoir temperature of 80 ºC and pore pressure of 1000 psi. The results showed that HM solutions with different target viscosity have higher polymer retention through sandpacks compared to LM solutions. Furthermore, results of residual resistance factor (RRF) measurements were in line with dynamic adsorption tests results. That is, permeability reduction due to irreversible polymer retention was higher when HM polymer solutions were injected. Furthermore, the results showed that although the resistance factor (RF) and in-situ viscosity of HM and LM polymers are in the same range, however, shear thickening regime becomes pronounced in the case of HM polymer with higher target viscosity. Polymer relaxation time measurements, and consequently, Deborah number calculations were performed to describe the shear thickening behavior by polymers viscoelastic characteristics. Results demonstrate that occurrence of shear thickening regime is controlled by Deborah number which is a function of polymer molecular weight, polymer concentration and injection rate. These results shed light on the importance of the selection of optimal polymer molecular weight and concentration during the design of polymer flooding projects.publishedVersio
PharmaMussels 2024: Assessing the presence of pharmaceutical contaminants in blue mussels
Get PDFThis report presents the analysis conducted under contract UWB no. 107383. NORCE was commissioned to perform chemical analyses on seven samples of blue mussels transplanted near the Reykjavik sewage treatment plant as part of a routine monitoring plan. The mussels were collected, sampled, and stored by the client’s infrastructure. The client then shipped the samples to the NORCE marine station located in Mekjarvik, Randaberg.PharmaMussels 2024: Assessing the presence of pharmaceutical contaminants in blue musselspublishedVersio
Projected changes of rain, sleet, and snowfall in Norway
Get PDFTailored products based on climate projections are in demand for climate adaptation planning in different industries. To meet the needs of the tourism industry, the authors applied available datatsets to calculate projections for the distribution of precipitation as rain, sleet, and snow in Norway, using daily average temperature to classify the precipitation phases. Amounts and number of days with precipitation in the different phases were calculated. The projections were based on bias-adjusted output from 10 EURO-CORDEX models under two emission scenarios. In general, total precipitation, as well as temperature, was projected to increase, while the number of days with precipitation was not projected to change significantly. The proportion of rainfall was projected to increase while that of snow was expected to decrease. Sleet ratio was projected to decrease in low lying coastal areas, and to increase in mountainous and inland areas. The results were presented for several tourist destinations. However, the authors found that the bias adjustment method applied in the input dataset led to a bias towards too much rain and too little snow, which should be considered when interpreting the results. They concluded that projections of rain, sleet and snow days were considered less affected by that flaw.publishedVersio
Regional planstrategi – prosess, vedtak og bruk
Get PDFDenne rapporten er utarbeidet av Holth & Winge AS og NORCE, med grunnlag i arbeidsverkstedene, nasjonale føringer, regionale og lokale behov, og etter tett dialog med oppdragsgiver. De anbefalingene som er gitt i rapporten om ulike temaer, er faglige vurderinger fra Holth & Winge og NORCE.Regional planstrategi – prosess, vedtak og brukpublishedVersio
Rehabiliteringstilbud med innslag av barn og unge: Beboeres erfaringer med å være en mentor for elever i det alternative opplæringstilbudet JegEr Ung på Jegersberg gård
Get PDFJegEr Ung er et alternativt opplæringstilbud etablert på Jegersberg gård for barn og unge med utfordringer i den ordinære skolen i Kristiansand kommune. Elevene er i tilbudet én eller flere dager i uken, og målet er at de skal erfare mestring og inkludering. Jegersberg gård er et rus- og medikamentfritt rehabiliterings- og kompetansesenter hvor beboerne bor og arbeider. Bruk av voksne som er i et rehabiliteringstilbud overfor elever med utfordringer i skolen, er et unikt og innovativt tilbud. Elevene inngår i det praktiske arbeidet og får tildelt en mentor som er beboer på gården. FoU-prosjektet utforsker erfaringene til elevene, foresatte og kontaktlærerne med JegEr Ung-tilbudet, samt hvordan arbeid med elevene påvirke miljøet på gården og beboernes egen rehabiliteringsprosess. Vi finner at både elever og beboere erfarer en helsefremmende utvikling i tråd med recovery-tenkning.Rehabiliteringstilbud med innslag av barn og unge: Beboeres erfaringer med å være en mentor for elever i det alternative opplæringstilbudet JegEr Ung på Jegersberg gårdpublishedVersio