Optimization of Tracer Injection Schemes for Improved History Matching

2021 ◽  
Author(s):  
Hsieh Chen ◽  
Hooisweng Ow ◽  
Martin E Poitzsch
Keyword(s):  
History Matching ◽  
Large Scale ◽  
Reservoir Characterization ◽  
Statistical Significance ◽  
Scale Up ◽  
Production Optimization ◽  
Tracer Injection ◽  
Full Field ◽  
Multiple Data ◽  
Mean Square Errors

Abstract Interwell tracers are powerful reservoir surveillance tools that provide direct reservoir flow paths and dynamics, which, when integrated with near real-time production optimization, can greatly improve recovery factor, and return on investment, the so-called "Advanced Tracers System" (ATS). Applying full field ATS is attractive for resource-holders, especially for those with large waterflood operations. However, to scale up ATS to cover large fields with potentially tens to hundreds of injectors and producers, the required unique tracer variations ("barcodes") and materials and associated analysis may increase rapidly. Here, we explore different tracer injection schemes that can acquire the most information while using reduced numbers of tracers, thereby controlling costs in field operations. We tested the designs of various modified tracer injection schemes with reservoir simulations. Numerical experiments were performed on synthetic fields with multiple injector and producer wells in waterflooding patterns. Two tracer injection schemes were considered: In Scheme 1, all injectors were injected with unique tracers representing the most information-rich case. In Scheme 2, some injectors were injected with the same tracers ("recycling" the same barcodes), and some injectors received no tracer injection ("null" barcodes). Production and tracer breakthrough data was collected for history matching after waterflooding simulations on the synthetic fields. The ensemble smoother with multiple data assimilation with tracers algorithm was used for history matching. We calculated the root-mean-square errors (RMSE) between the reference data and the history matched production simulation data. To improve the statistics, 20 independent testing reference synthetic fields were constructed by randomizing the number and locations of high permeability zones crossing different injectors and producers. In all cases, the history matching algorithms largely reduced the RMSE thereby enhancing reservoir characterization. Analyzing the statistical significance with p-values among testing cases, first, as expected, the data mismatch is highly significantly lower after history matching than before history matching (p < 0.001). Second, the data mismatch is even lower when history matching with tracers (both in Scheme 1 and 2) than without tracers (p < 0.05), demonstrating clearly that tracers can provide extra information for the reservoir dynamics. Finally, and most importantly, history matching with tracers in Scheme 1 or in Scheme 2 result in statistically the same data mismatch (p > 0.05), indicating the cost-saving "recycling" and "null" tracer barcodes can provide equally competent reservoir information. To the best of our knowledge, this is the first study that evaluated the history matching qualities deriving from different tracer injection schemes. We showed that through optimal designs of the tracer injections, we can acquire very similar information with reduced tracer materials and barcodes, thus reducing costs and field operational complexities. We believe this study facilitates the deployment of large-scale reservoir monitoring and optimization campaigns using tracers such as ATS.

Solid Lipid Nanoparticles: A Promising Drug Delivery Technology

2009 ◽  
Vol 2 (2) ◽  
pp. 509-516
Author(s):  
S. Pragati ◽  
S. Kuldeep ◽  
S. Ashok ◽  
M. Satheesh
Keyword(s):  
Drug Delivery ◽  
Solid Lipid Nanoparticles ◽  
Large Scale ◽  
Colloidal Particles ◽  
Scale Up ◽  
Lipid Nanoparticles ◽  
Scale Production ◽  
Research Activity ◽  
New Approach ◽  
Solid Lipid

One of the situations in the treatment of disease is the delivery of efficacious medication of appropriate concentration to the site of action in a controlled and continual manner. Nanoparticle represents an important particulate carrier system, developed accordingly. Nanoparticles are solid colloidal particles ranging in size from 1 to 1000 nm and composed of macromolecular material. Nanoparticles could be polymeric or lipidic (SLNs). Industry estimates suggest that approximately 40% of lipophilic drug candidates fail due to solubility and formulation stability issues, prompting significant research activity in advanced lipophile delivery technologies. Solid lipid nanoparticle technology represents a promising new approach to lipophile drug delivery. Solid lipid nanoparticles (SLNs) are important advancement in this area. The bioacceptable and biodegradable nature of SLNs makes them less toxic as compared to polymeric nanoparticles. Supplemented with small size which prolongs the circulation time in blood, feasible scale up for large scale production and absence of burst effect makes them interesting candidates for study. In this present review this new approach is discussed in terms of their preparation, advantages, characterization and special features.

Recombinant Protein Production in Microalgae: Emerging Trends

2020 ◽  
Vol 27 (2) ◽  
pp. 105-110 ◽  
Author(s):  
Niaz Ahmad ◽  
Muhammad Aamer Mehmood ◽  
Sana Malik
Keyword(s):  
Chloroplast Genome ◽  
Recombinant Proteins ◽  
Large Scale ◽  
Higher Plants ◽  
Scale Up ◽  
Chloroplast Transformation ◽  
Point Of View ◽  
Nuclear Transformation ◽  
Scale Production ◽  
Algal Chloroplast

: In recent years, microalgae have emerged as an alternative platform for large-scale production of recombinant proteins for different commercial applications. As a production platform, it has several advantages, including rapid growth, easily scale up and ability to grow with or without the external carbon source. Genetic transformation of several species has been established. Of these, Chlamydomonas reinhardtii has become significantly attractive for its potential to express foreign proteins inexpensively. All its three genomes – nuclear, mitochondrial and chloroplastic – have been sequenced. As a result, a wealth of information about its genetic machinery, protein expression mechanism (transcription, translation and post-translational modifications) is available. Over the years, various molecular tools have been developed for the manipulation of all these genomes. Various studies show that the transformation of the chloroplast genome has several advantages over nuclear transformation from the biopharming point of view. According to a recent survey, over 100 recombinant proteins have been expressed in algal chloroplasts. However, the expression levels achieved in the algal chloroplast genome are generally lower compared to the chloroplasts of higher plants. Work is therefore needed to make the algal chloroplast transformation commercially competitive. In this review, we discuss some examples from the algal research, which could play their role in making algal chloroplast commercially successful.

Dietary Patterns: A New Therapeutic Approach for Depression?

2019 ◽  
Vol 20 (2) ◽  
pp. 123-129 ◽  
Author(s):  
Mariana Jesus ◽  
Tânia Silva ◽  
César Cagigal ◽  
Vera Martins ◽  
Carla Silva
Keyword(s):  
Depressive Symptoms ◽  
Dietary Patterns ◽  
Large Scale ◽  
Randomized Clinical Trials ◽  
Dietary Intervention ◽  
Common Mental Disorder ◽  
Controlled Trial ◽  
Statistical Significance ◽  
Inverse Association ◽  
The Impact

Introduction: The field of nutritional psychiatry is a fast-growing one. Although initially, it focused on the effects of vitamins and micronutrients in mental health, in the last decade, its focus also extended to the dietary patterns. The possibility of a dietary cost-effective intervention in the most common mental disorder, depression, cannot be overlooked due to its potential large-scale impact. Method: A classic review of the literature was conducted, and studies published between 2010 and 2018 focusing on the impact of dietary patterns in depression and depressive symptoms were included. Results: We found 10 studies that matched our criteria. Most studies showed an inverse association between healthy dietary patterns, rich in fruits, vegetables, lean meats, nuts and whole grains, and with low intake of processed and sugary foods, and depression and depressive symptoms throughout an array of age groups, although some authors reported statistical significance only in women. While most studies were of cross-sectional design, making it difficult to infer causality, a randomized controlled trial presented similar results. Discussion: he association between dietary patterns and depression is now well-established, although the exact etiological pathways are still unknown. Dietary intervention, with the implementation of healthier dietary patterns, closer to the traditional ones, can play an important role in the prevention and adjunctive therapy of depression and depressive symptoms. Conclusion: More large-scale randomized clinical trials need to be conducted, in order to confirm the association between high-quality dietary patterns and lower risk of depression and depressive symptoms.

scholarly journals COVID-19 and Its Symptoms’ Panoply: A Case-Control Study of 919 Suspected Cases in Locked-Down Ovar, Portugal

2021 ◽  
pp. 1-8
Author(s):  
Regina Sá ◽  
Tiago Pinho-Bandeira ◽  
Guilherme Queiroz ◽  
Joana Matos ◽  
João Duarte Ferreira ◽  
...  
Keyword(s):  
Large Scale ◽  
Case Control Study ◽  
Statistical Significance ◽  
Case Control ◽  
Case Definition ◽  
Suspected Case ◽  
Risk Characteristics ◽  
Increased Risk ◽  
Wide Range ◽  
Control Study

<b><i>Background:</i></b> Ovar was the first Portuguese municipality to declare active community transmission of SARS-CoV-2, with total lockdown decreed on March 17, 2020. This context provided conditions for a large-scale testing strategy, allowing a referral system considering other symptoms besides the ones that were part of the case definition (fever, cough, and dyspnea). This study aims to identify other symptoms associated with COVID-19 since it may clarify the pre-test probability of the occurrence of the disease. <b><i>Methods:</i></b> This case-control study uses primary care registers between March 29 and May 10, 2020 in Ovar municipality. Pre-test clinical and exposure-risk characteristics, reported by physicians, were collected through a form, and linked with their laboratory result. <b><i>Results:</i></b> The study population included a total of 919 patients, of whom 226 (24.6%) were COVID-19 cases and 693 were negative for SARS-CoV-2. Only 27.1% of the patients reporting contact with a confirmed or suspected case tested positive. In the multivariate analysis, statistical significance was obtained for headaches (OR 0.558), odynophagia (OR 0.273), anosmia (OR 2.360), and other symptoms (OR 2.157). The interaction of anosmia and odynophagia appeared as possibly relevant with a borderline statistically significant OR of 3.375. <b><i>Conclusion:</i></b> COVID-19 has a wide range of symptoms. Of the myriad described, the present study highlights anosmia itself and calls for additional studies on the interaction between anosmia and odynophagia. Headaches and odynophagia by themselves are not associated with an increased risk for the disease. These findings may help clinicians in deciding when to test, especially when other diseases with similar symptoms are more prevalent, namely in winter.

Expanding the boundaries of learning

2021 ◽  
Vol 102 (8) ◽  
pp. 8-13
Author(s):  
Thomas Hatch
Keyword(s):  
Instructional Practices ◽  
New Hampshire ◽  
Large Scale ◽  
Educational Experiences ◽  
Scale Up ◽  
Project Based Learning

Taking advantage of the possibilities for learning outside of school requires us to build on what we know about why it is so hard to sustain and scale up unconventional educational experiences within conventional schools. To illustrate the opportunities and challenges, Thomas Hatch describes a large-scale approach to project-based learning developed in a camp in New Hampshire and incorporated in a Brooklyn school, a trip-based program in Detroit, and Singapore’s systemic embrace of learning outside school. By understanding the conditions that can sustain alternative instructional practices, educators can find places to challenge the boundaries of schooling and create visions of the possible that exceed current constraints.

scholarly journals Efficient Dimensionality Reduction Methods in Reservoir History Matching

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3137
Author(s):  
Amine Tadjer ◽  
Reider B. Bratvold ◽  
Remus G. Hanea
Keyword(s):  
Data Assimilation ◽  
Dimensionality Reduction ◽  
Gaussian Process ◽  
Latent Variable ◽  
History Matching ◽  
Production Performance ◽  
Latent Variable Model ◽  
Variable Model ◽  
Multiple Data ◽  
Ensemble Smoother

Production forecasting is the basis for decision making in the oil and gas industry, and can be quite challenging, especially in terms of complex geological modeling of the subsurface. To help solve this problem, assisted history matching built on ensemble-based analysis such as the ensemble smoother and ensemble Kalman filter is useful in estimating models that preserve geological realism and have predictive capabilities. These methods tend, however, to be computationally demanding, as they require a large ensemble size for stable convergence. In this paper, we propose a novel method of uncertainty quantification and reservoir model calibration with much-reduced computation time. This approach is based on a sequential combination of nonlinear dimensionality reduction techniques: t-distributed stochastic neighbor embedding or the Gaussian process latent variable model and clustering K-means, along with the data assimilation method ensemble smoother with multiple data assimilation. The cluster analysis with t-distributed stochastic neighbor embedding and Gaussian process latent variable model is used to reduce the number of initial geostatistical realizations and select a set of optimal reservoir models that have similar production performance to the reference model. We then apply ensemble smoother with multiple data assimilation for providing reliable assimilation results. Experimental results based on the Brugge field case data verify the efficiency of the proposed approach.

scholarly journals Scaling Up Delivery of Biofortified Staple Food Crops Globally: Paths to Nourishing Millions

2021 ◽  
pp. 037957212098250
Author(s):  
Jennifer K. Foley ◽  
Kristina D. Michaux ◽  
Bho Mudyahoto ◽  
Laira Kyazike ◽  
Binu Cherian ◽  
...  
Keyword(s):  
Pearl Millet ◽  
Large Scale ◽  
Scale Up ◽  
Lessons Learned ◽  
Raw Material ◽  
Micronutrient Deficiencies ◽  
Seed Systems ◽  
Public And Private ◽  
Context Specific ◽  
Public And Private Sector

Background: Micronutrient deficiencies affect over one quarter of the world’s population. Biofortification is an evidence-based nutrition strategy that addresses some of the most common and preventable global micronutrient gaps and can help improve the health of millions of people. Since 2013, HarvestPlus and a consortium of collaborators have made impressive progress in the enrichment of staple crops with essential micronutrients through conventional plant breeding. Objective: To review and highlight lessons learned from multiple large-scale delivery strategies used by HarvestPlus to scale up biofortification across different country and crop contexts. Results: India has strong public and private sector pearl millet breeding programs and a robust commercial seed sector. To scale-up pearl millet, HarvestPlus established partnerships with public and private seed companies, which facilitated the rapid commercialization of products and engagement of farmers in delivery activities. In Nigeria, HarvestPlus stimulated the initial acceptance and popularization of vitamin A cassava using a host of creative approaches, including “crowding in” delivery partners, innovative promotional programs, and development of intermediate raw material for industry and novel food products. In Uganda, orange sweet potato (OSP) is a traditional subsistence crop. Due to this, and the lack of formal seed systems and markets, HarvestPlus established a network of partnerships with community-based nongovernmental organizations and vine multipliers to popularize and scale-up delivery of OSP. Conclusions: Impact of biofortification ultimately depends on the development of sustainable markets for biofortified seeds and products. Results illustrate the need for context-specific, innovative solutions to promote widespread adoption.

scholarly journals Injection-production optimization of carbonate reservoir based on an inter-well connectivity model

2021 ◽  
pp. 014459872199465
Author(s):  
Yuhui Zhou ◽  
Sheng Lei ◽  
Xuebiao Du ◽  
Shichang Ju ◽  
Wei Li
Keyword(s):  
History Matching ◽  
Water Injection ◽  
Field Application ◽  
Carbonate Reservoir ◽  
Production Optimization ◽  
Oil Field ◽  
Carbonate Reservoirs ◽  
Utilization Rate ◽  
Production Volume ◽  
Connectivity Model

Carbonate reservoirs are highly heterogeneous. During waterflooding stage, the channeling phenomenon of displacing fluid in high-permeability layers easily leads to early water breakthrough and high water-cut with low recovery rate. To quantitatively characterize the inter-well connectivity parameters (including conductivity and connected volume), we developed an inter-well connectivity model based on the principle of inter-well connectivity and the geological data and development performance of carbonate reservoirs. Thus, the planar water injection allocation factors and water injection utilization rate of different layers can be obtained. In addition, when the proposed model is integrated with automatic history matching method and production optimization algorithm, the real-time oil and water production can be optimized and predicted. Field application demonstrates that adjusting injection parameters based on the model outputs results in a 1.5% increase in annual oil production, which offers significant guidance for the efficient development of similar oil reservoirs. In this study, the connectivity method was applied to multi-layer real reservoirs for the first time, and the injection and production volume of injection-production wells were repeatedly updated based on multiple iterations of water injection efficiency. The correctness of the method was verified by conceptual calculations and then applied to real reservoirs. So that the oil field can increase production in a short time, and has good application value.

scholarly journals Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM)

2021 ◽  
Vol 13 (7) ◽  
pp. 1367
Author(s):  
Yuanzhi Cai ◽  
Hong Huang ◽  
Kaiyang Wang ◽  
Cheng Zhang ◽  
Lei Fan ◽  
...  
Keyword(s):  
Large Scale ◽  
Semantic Segmentation ◽  
Optimal Combination ◽  
Reconstruction Technique ◽  
Process Data ◽  
Redundant Data ◽  
Multiple Data ◽  
Information Models ◽  
Efficient Data

Over the last decade, a 3D reconstruction technique has been developed to present the latest as-is information for various objects and build the city information models. Meanwhile, deep learning based approaches are employed to add semantic information to the models. Studies have proved that the accuracy of the model could be improved by combining multiple data channels (e.g., XYZ, Intensity, D, and RGB). Nevertheless, the redundant data channels in large-scale datasets may cause high computation cost and time during data processing. Few researchers have addressed the question of which combination of channels is optimal in terms of overall accuracy (OA) and mean intersection over union (mIoU). Therefore, a framework is proposed to explore an efficient data fusion approach for semantic segmentation by selecting an optimal combination of data channels. In the framework, a total of 13 channel combinations are investigated to pre-process data and the encoder-to-decoder structure is utilized for network permutations. A case study is carried out to investigate the efficiency of the proposed approach by adopting a city-level benchmark dataset and applying nine networks. It is found that the combination of IRGB channels provide the best OA performance, while IRGBD channels provide the best mIoU performance.

scholarly journals Synchronization in 5G networks: a hybrid Bayesian approach toward clock offset/skew estimation and its impact on localization

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Meysam Goodarzi ◽  
Darko Cvetkovski ◽  
Nebojsa Maletic ◽  
Jesús Gutiérrez ◽  
Eckhard Grass
Keyword(s):  
Bayesian Approach ◽  
Large Scale ◽  
Hybrid Approach ◽  
Position Estimation ◽  
5G Networks ◽  
Clock Skew ◽  
Depth Analysis ◽  
The Impact ◽  
Mean Square Errors ◽  
Clock Offset

AbstractClock synchronization has always been a major challenge when designing wireless networks. This work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Furthermore, we provide an in-depth analysis of the impact of the proposed approach on a synchronization-sensitive service, i.e., localization. Specifically, we expose the substantial benefit of belief propagation (BP) running on factor graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian recursive filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into local synchronization domains and applying the most suitable synchronization algorithm (BP- or BRF-based) on each domain. The performance of the hybrid approach is then evaluated in terms of the root mean square errors (RMSEs) of the clock offset, clock skew, and the position estimation. According to the simulations, in spite of the simplifications in the hybrid approach, RMSEs of clock offset, clock skew, and position estimation remain below 10 ns, 1 ppm, and 1.5 m, respectively.

Sign in / Sign up

Export Citation Format

Share Document