bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and Practical Application of an Analytical Well Failure Model that Improves Well Safety and Production Efficiency
Authors: Girling Stuart , McIntosh John , Lumbye Peter , Nielsen Hans-Henrik , Patterson Stephen , Lutzen Peter 
Published: October 2020

Abstract
A well failure model is constructed as a matrix that identifies the most common modes of well failure seen by the Well Operator. For specific well types, well anomalies are assessed before they happen, in a what-if risk assessment.
A traditional well failure model (WFM) includes a subset of possible anomalies and is based on subjective judgement. Total E&P Denmark's (TEPDK) new WFM uses quantitative analysis with an objective approach to risk assessment. This gives detail and clarity of actual risk (as a product of probability and severity) from well integrity anomalies not previously available.
This Analytical WFM assesses well integrity barrier compliance and risk. It uses individual component failure mode probabilities and severities and analyses thousands of potential leak paths throughout wells. It includes potential flow from multiple pressure sources and to multiple pressure sinks, providing a comprehensive understanding of all possible failure cases, and their associated impact on risk.
Quantitative Risk Assessment (QRA) was used to calculate maximum Safe Service Life for wells following component failure. Outputs from the analysis were used to build a detailed WFM. Immediate and long-term actions were developed in workshops and suitable response times agreed based on the Safe Service Life calculation.
Engineers now spend 85% less time in well integrity risk assessments and failure management when anomalies arise because this work has been done in advance. This means engineers' time is freed for other valuable tasks. The company also saves management time, onshore and offshore, cutting down the need for management challenge and action justification. All personnel involved have a more consistent understanding of well issues, with routine anomalies dealt with much more efficiently. This means decisions are made at a lower level, including whether to continue to operate a well or shut it in.
TEPDK has reduced the probability of process safety incidents from wells. This is because it has a far more sophisticated, clearer understanding of well risks, and actions and timeframes for anomaly response. It has also improved production efficiency, safeguarding on average 2,700 boepd, through a reduction in well shut-in time. This has an equivalent value of approximately $50 million / year.
Optimal Well Placement Using Machine Learning Methods: Multiple Reservoir Scenarios
Authors: Mousavi Seyed Mahdi , Jabbari Hadi , Darab Mahdi , Nourani Meysam , Sadeghnejad Saeid 
Published: October 2020

Abstract
Determining the distribution of optimal injection and production wells along with their operating conditions is a complex problem. The objective of this study is to compare the effectiveness of an experimental based approach (central composite design, CCD) with a machine learning method (xGBoost) in a well-placement optimization application.
In this study, the well-placement problem consists of the joint optimization of drainage radius, well-operating conditions, and the number of injection and production wells. Our objective function is the net-present-value (NPV) for a specified operational life of the reservoir under study. Both algorithms (CCD and xGBoost) are applied to three different optimization scenarios, i.e., production from (I) homogeneous reservoir, (II) heterogeneous reservoir, and (III) waterflooding into a heterogeneous reservoir. Like all machine-learning algorithms, our methods need a training dataset. The fast predictor module (i.e., trained model) is obtained by running several numerical simulations by a commercial simulator internally called in an own developed Python code. Moreover, R-squared is chosen as the statistical quality measure in this study.
In the first scenario, both algorithms show satisfying predictions (R-squared of 0.943 and 0.999 for CCD and xGBoost, respectively). In scenario II, the CCD and xGBoost show a similar response again (0.948 and 0.997, respectively). As a sub-result, the optimum distance between the two producers was found approximately five simulation blocks in the first case, and three blocks in the second one. The CCD method reveals unreliable results (0.840) in scenario III, while the NPV predictions of the xGBoost algorithm are still acceptable (0.986). Moreover, unlike CCD, xGBoost could find the optimum solution. In other words, the CCD method does not always converge to an optimum solution. However, the number of required simulation runs for CCD is equal to that for the xGBoost model.
In this study, a machine learning approach and an experimental design method were compared together in detail. Their efficiency in predicting the NPV of a well-placement problem through multiple homogeneous and heterogeneous reservoir scenarios differ significantly.
Deep Reinforcement Learning Applied to Managed Pressure Drilling
Authors: ArnØ Mikkel , Godhavn John-Morten , Aamo Ole Morten 
Published: October 2020

Abstract
During drilling operations, maintaining a desired downhole pressure between pressure margins is crucial to avoid damage to the formation and the well. The process is highly nonlinear, changing with depth, and every section in every well is different. Standard solutions with PID controllers are widely accepted for this purpose, although methods such as deep reinforcement learning (DRL) could be investigated as an alternative approach.
A smooth update deep Q learning algorithm is used to train an agent embedded in a managed pressure drilling system. The aim is to control downhole pressure during pipe connections by use of a topside choke valve with nonlinear characteristics. The agent is trained on previously gathered data, from situations featuring step changes in pressure setpoint and changing mud flows, all at various well depths. After training, the agent is tasked with controlling BHP during connection, herein demonstrated by use of a numerically simulated low-order hydraulics model.
Through episodic training, it becomes clear that the agent purely through interaction with the environment, and without any prerequisite knowledge of system dynamics and reward design, converges to an optimal control policy. The trained agent is then tested on pipe connections with well depths in the lower and upper bounds of the training data. The pipe connection scenario presents changes in operating conditions in terms of changing mud flows with changing conditions like increased frictional pressure losses due to increased depth. Still, the results presented show the agent's ability to track a pressure setpoint at various depths in the changing conditions present during connection, while seamlessly incorporating controller constraints.
There are several advantages associated with this approach, among them eliminating the need for development of a complex dynamic model for the process. Also, the approach is applicable to both linear and nonlinear systems, deterministic and stochastic systems, and lower- and higher-level decision-making. These methods could possibly also be applied to other key challenges in drilling such as ROP optimization or autonomous directional drilling.
When Can a Stock-Tank Oil be a Condensate?
Authors: Meisingset Knut Kristian , Cely Alexandra , Bergo Cathrine , Yang Tao 
Published: October 2020

Abstract
Database plots of the distribution of C7+ to C40+ fractions, as well as plus fraction density and molecular weight, show that there is a clear statistical difference - with minor overlap - between stabilized hydrocarbon liquids from reservoir gas (condensate) and reservoir oil. In most cases, the origin of a stock-tank oil sample can be interpreted on basis of composition, density, molecular weight and reservoir pressure.
Typically, hydrocarbon liquid with density higher than 0.85 g/cm3 and C7+ molecular weight &gt; 250 g/mol origins from reservoir oil, while hydrocarbon liquid with density lower than 0.8 g/cm3 and C7+ molecular weight &lt; 170 g/mol origins from reservoir gas.
A main purpose of the present paper is to identify cases where composition, molecular weight and density show that a hydrocarbon liquid sample cannot origin from a reservoir gas at a given reservoir pressure. In addition to statistics from a large PVT database, a PVT simulation procedure is proposed, giving additional proof for such cases.
Accounting for Model Errors of Rock Physics Models in 4D Seismic History Matching Problems: A Perspective of Machine Learning
Authors: Luo Xiaodong , Lorentzen Rolf , Bhakta Tuhin 
Published: October 2020

Abstract
Model errors are ubiquitous in practical history matching problems. A common approach in the literature to accounting for model errors is to treat them as random variables following certain presumed distributions. While such a treatment renders algorithmic convenience, its underpinning assumptions are often invalid. In this work, we adopt an alternative approach, and treat model-error characterization as a functional approximation problem, which can be solved using a generic machine learning method. We then integrate the proposed model- error characterization approach into an ensemble-based history matching framework, and show that, with very minor modifications, existing ensemble-based history matching algorithms can be readily deployed to solve the history matching problem in the presence of model errors.
To demonstrate the efficacy of the integrated history matching framework, we apply it to account for potential model errors of a rock physics model in 4D seismic history matching applied to the full Norne benchmark case. Our experiment results indicate that the proposed model-error characterization approach helps improve the qualities of estimated reservoir models, and leads to more accurate forecasts of production data. This suggests that accounting for model errors from a perspective of machine learning may serve as a novel and viable way to deal with model imperfection in practical history matching problems.
Hierarchical Characterization and Modelling of Deep-Water Slope Channel Reservoirs
Authors: Soni Kishan , Manzocchi Tom , Haughton Peter , Carneiro Marcus 
Published: October 2020

Abstract
Oil reservoirs hosted in deep-water slope channel deposits are a challenge to manage and model. A six-level hierarchical arrangement of depositional elements within slope channel deposits has been widely recognized, and dimensional (width and thickness) and stacking (amalgamation ratio and volume fraction) data have been acquired from published studies to establish parameters for a representative slope channel system. A new static modelling workflow has been developed for building models of channel complexes based on a simplified hierarchical scheme using industry-standard object-based modelling methods and a new plugin applying the compression algorithm. Object-based modelling using the compression algorithm allows for independent input of volume fractions and amalgamation ratios for channel and sheet objects within a hierarchical modelling workflow. A base-case channel complex model is built at the resolution of individual sandstone beds, conditioned to representative dimensional and stacking characteristics of natural systems. Inclusion of explicit channel axis and margin regions within the channels governs bed placement and controls inter-channel connectivity where channels are amalgamated. The distribution of porosity and permeability within these beds mimics grain-size trends of fining in the vertical and lateral directions. The influence of various geological parameters and modelling choices on reservoir performance have been assessed through water-flood flow simulation modelling. Omission of the compression method in the modelling workflow results in a three-fold increase in oil recovery at water-breakthrough, because the resultant unnaturally high amalgamation ratios result in overly-connected flow units at all hierarchical levels. Omission in the modelling of either the bed-scale hierarchical level, or of the axial and marginal constraints on the bed placement in models that do include this level, results in a two-fold increase in oil recovery at water-breakthrough relative to the base-case, because in these cases the channel-channel connections are too permissive.
Abundance tracking by long-read nanopore sequencing of complex microbial communities in samples from 20 different biogas/wastewater plants
Authors: Brandt Christian , Bongcam-Rudloff Erik , Müller Bettina 
Published: October 2020

Abstract
Background: Anaerobic digestion (AD) has long been critical technology for green energy, but the majority of the microorganisms involved are unknown and are currently not cultivable, which makes abundance tracking difficult. Developments in nanopore long-read sequencing make it a promising approach for monitoring microbial communities via metagenomic sequencing. For reliable monitoring of AD via long reads, a robust protocol for obtaining less fragmented, high-quality DNA, while preserving bacteria and archaea composition, was established. Results: Samples from 20 different biogas/wastewater reactors were investigated, and a median of 20.5 Gb sequencing data per nanopore flow cell was retrieved for each reactor using the developed DNA isolation protocol. The nanopore sequencing data was compared against Illumina sequencing data while using different taxonomic indices for read classifications. The Genome Taxonomy Database (GTDB) index allowed sufficient characterisation of the abundance of bacteria and archaea in biogas reactors with a dramatic improvement (1.8- to 13-fold increase) in taxonomic classification compared to the RefSeq index. Both technologies performed similarly in taxonomic read classification with a slight advantage for Illumina in regards to the total proportion of classified reads. However, nanopore sequencing data revealed a higher genus richness after classification. Conclusion: Metagenomic read classification via nanopore provides a promising approach to monitor the abundance of taxa present in a microbial AD community, as an alternative to 16S rRNA studies or Illumina Sequencing.
High-precision digital terahertz phase manipulation within a multichannel field perturbation coding 2DEG meta-chip
Authors: Zeng Hongxin , Liang Huajie , Zhang Yaxin , Yang Ziqiang , Lan Feng , Liang Shixiong , Li Zheng , Wang Lan , Zhang Xilin , Gong Sen , Gong Yubin , Yang Ziqiang 
Published: October 2020

Abstract
Terahertz phase manipulation has always been based on direct coupling of the resonance of quasi-optical terahertz waves with metamaterials, which is accompanied by unnecessary amplitude modulation, thus limiting the accuracy of phase manipulation and its application in monolithic integrated systems. Here, we propose a coding meta-chip composed of transmission lines and two-dimensional electron gas (2DEG) meta-atoms, wherein local perturbation resonances are induced to manipulate the phase of terahertz waves. By controlling the electronic transport characteristics of the 2DEG with external voltages, the intensity of the perturbation can be manipulated, which affects the transmission phase of the waves. More importantly, the perturbation resonances induced by different meta-atoms can be coupled so that through digital coding of the perturbation state of 2DEG meta-atoms, the terahertz wave transmission phase can be manipulated with high precision. As a result, phase manipulation with different precisions from 2° to 5° is observed from 0.26 to 0.27 THz, where the average phase error is only 0.36°, and the maximum root mean square of the transmittance is 0.36 dB. This high-precision phase manipulation via field coding has great application potential in the fields of beamforming, wireless communication, and high-resolution imaging.
Finite Element Modeling and Vibration Analysis of Sprag Clutch-Flexible Rotor System
Authors: huang chuang , zhao yongqiang , jin guanghu 
Published: October 2020

Abstract
To study the overall vibration characteristics of the sprag clutch-flexible rotor system (SC-FRS) under high-speed operating conditions, a finite element model of SC-FRS considering rotor flexibility and bearing support stiffness is established based on the proposed calculation method of the stiffness matrix. According to this model, the natural frequency and mode shape of the system are calculated, and the correctness of the model is verified by comparing it with the calculation results of ANSYS software. Under the action of unbalance, the bending-torsion coupled vibration and the dynamic load of the inter-shaft bearings are analyzed, and it is found that the resonant peak in the torsional direction has the same resonance frequency as that in the bending direction. A test rig for the sprag clutch-rotor system is built, and the axis trajectory and critical speed are tested. The test results show that the finite element model of SC-FRS can accurately describe the vibration characteristics of the system.
Understanding the costs of urban sanitation: towards a standard costing model
Authors: Sainati Tristano , Zakaria Fiona , Locatelli Giorgio , Sleigh P. Andrew , Evans Barbara 
Published: October 2020

Abstract
There is a dearth of reliable cost data for urban sanitation. In the absence of high-quality global data, the full cost of sustainable implementation of urban sanitation remains uncertain. This paper proposes an approach for developing bespoke parametric cost estimation models for easy and reliable estimation of the costs of alternative sanitation technologies in a range of geographical contexts. A key requirement for the development of these models is the establishment of a large database of empirical information on the current costs of sanitation systems. Such a database does not currently exist. Two foundational tools are proposed. Firstly, a standard metric for reporting the costs of urban sanitation systems, total annualised cost per household. Secondly, a standardised approach to the collection of empirical cost data, the Novel Ball-Park Reporting Approach (NBPRA). Data from the NBPRA are presented for 87 individual sanitation components from 25 cities in 10 countries. Broad cost ranges for different archetypal systems have been estimated; these currently have high levels of uncertainty. Further work is proposed to collect additional data, build up the global database, and develop parametric cost estimation models with higher reliability.
Comparative life cycle assessment of four commonly used point-of-use water treatment technologies
Authors: Walsh Tara , Mellor Jonathan 
Published: October 2020

Abstract
Across the globe, billions of people lack access to safe drinking water. Many different point-of-use (POU) technologies have been developed that significantly reduce the disease-causing pathogens found in untreated water. With many different technologies available, it can be difficult to choose which technology to implement in specific areas. Beyond the cost of each technology, the environmental impacts could bring additional harm to a community. Life cycle assessments (LCAs) are used to make comparisons across different technologies. This study uses an LCA to compare boiling water, ceramic water filters, BioSand filters and POU chlorination as treatment options in the rural community of Thohoyandou, Limpopo Province, South Africa utilizing previously published, open-access data. Global warming potential, water use, energy use, smog formation, particulate matter and land use are the studied environmental impacts. Results found that boiling had the most impact on energy use, global warming potential, smog and land use; chlorination had the greatest impact on particulate matter and water use. A cost comparison found boiling water to be most expensive at 0.053 USD per liter and chlorination to be least expensive at 0.0005 USD per liter.
Water quality variation and its conditioning factors in the Three Gorges Reservoir, China
Authors: Wang Xiaoxiao , Bing Haijian , Wu Yanhong , Zhou Jun , Zhu He , Wu Yong , Sun Hongyang 
Published: October 2020

Abstract
The variation of water quality has been an issue of concern since the impoundment of the Three Gorges Reservoir (TGR). In this study, water samples from the TGR were collected in July and November 2015 and in May and July 2016 to reveal the variations of water quality and its key conditioning factors. The results showed that the concentrations of major ions, nutrients and heavy metals in the TGR's water body exhibited heterogeneous variations. Especially, the hotspot for major ions and heavy metals emerged at Chongqing downtown, and a decreasing trend from Chongqing toward the TGR dam was spatially observed. The heterogeneous variation of hydrochemistry in the TGR revealed the integrated influences of natural processes and human activities. Especially, the intense rainfall in the spring and early summer of 2016 promoted the transport of pollutants to the TGR, and further magnified the influence of large cities on the spatial variations of the hydrochemistry in the TGR. This study will give more insights into the change of water quality in large reservoirs in the context of the intense rainfall and the human activities.
Plant Derived Silver Nanoparticles and their Therapeutic Applications
Authors: Roy Arpita 
Published: October 2020

Background:
Nanotechnology involves the synthesis of materials that are in nanometre sizes
(1 to 100 nm). Nanotechnology has appeared as a new area of fundamental science and is getting
worldwide consideration due to its extensive applications. They have exceptional physicochemical
properties due to their unique size, shape and size distribution.

Methods:
Conventionally, nanoparticles were synthesized using physical and chemical methods. These
methods have various disadvantages; therefore, biological methods possess great interest. Biological
synthesis uses bacteria, fungi, algae and plants.

Results:
Biosynthesis of nanoparticles using plants emerges as nanofactories as they offer nontoxic, clean
and eco-friendly methods with various physicochemical properties. Out of all the nanoparticles, silver nanoparticles
gain special attention due to their various therapeutical and environmental applications.

Conclusion:
In this review, a summary of various reports within the last ten years where plants were
utilized for silver nanoparticle synthesis has been discussed. Further mechanisms involving synthesis,
factors affecting the process of synthesis and therapeutical applications have been discussed.