bluuebunny/crossref_metadata_2025_split huggingface dataset:
Vertebral osteomyelitis due to an unusual pathogen: a case report
Authors: Ghimire Rabindra , Hussain Jaffer , Abubaker Ahmed , Henderson Triona , Cook Paul 
Published: April 2019

Background:
Vertebral osteomyelitis most often presents with back pain and is usually a secondary complication of a distant infection with hematogenous seeding. A source of infection is detected in about half of the cases. Burkholderia cepacia has rarely been implicated as an etiology.

Case Presentation:
We present a 50-year old Caucasian male with low back pain for 10 days associated with urinary incontinence without any fever or chills. He had laboratory and imaging evidence of vertebral osteomyelitis. Patient underwent bone biopsy and was diagnosed with vertebral osteomyelitis and discitis caused by B. cepacia. The patient was treated with intravenous meropenem for initial 2 weeks and, thereafter, oral ciprofloxacin and continues to do well several weeks later.

Conclusion:
Vertebral osteomyelitis due to B. cepacia has rarely been reported as a cause and though rare this organism should be considered in the differential diagnosis of vertebral osteomyelitis in the appropriate clinical setting.
The Sensorless Speed Controller of Induction Motor in DFOC Model Based on the Voltage and Current
Authors: Cong Tran Thinh , Brandstetter Pavel , Vo Hau Huu 
Published: April 2019

This paper presents the Sensorless Speed control of the Induction Motor (IM) in Direct Field Oriented Control (DFOC) modeling based on the mathematical model of the voltage and current parameter. The first is DFOC controlled modeling of IM, the second presents sensorless speed controller base on RF-MRAS, the third describes the model of the sensorless speed controller based on measured values directly from the induction motor as the voltage and current. The last is simulation results in the Matlab-Simulink environment. These results indicate that this proposed method can determine accurately, very quickly the speed of the induction motor and can be applied in the practice with high reliability, and low cost.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
The Conversion of Waste CO2 to Intermediated Petrochemical Product
Authors: Piromchart Taradon , D’Elia Valerio 
Published: April 2019

Abstract
Excess anthropogenic CO2 emissions are a global environmental issue and a matter of concern for gas production in the Gulf of Thailand (GoT). The aim of this study is to develop a catalyst that can be used to convert CO2 obtained from a CO2 removal membrane by the cycloaddition reaction of CO2 to epoxides leading to industrially attractive cyclic carbonates. The results show that by using a suitable catalytic system, the reaction can be carried out in high yields under mild condition (Temperature range 25-120 °C and pressure range 4-9 barg). The conversion rate is 98 – 100% under optimized conditions.
Correlative Multiscale Imaging of Mancos Shale
Authors: Goral Jan , Deo Milind , Andrew Matthew 
Published: April 2019

Abstract
Mudstone (shale) reservoir evaluation and efficient development poses a significant challenge due to the heterogeneous nature of these complex formations. Organic-rich shales are characterized by intricate mineralogy, ultra-low nanoporosity, and nano-darcy permeability making these tight source/reservoir rocks challenging in obtaining economically viable hydrocarbons. Consequently, characterizing and quantifying minerology and intra-/interparticle (non)organic-hosted porosity at the pore-scale, and up-scaling it to the core-scale remains a significant focus of evaluating reservoir quality in shale plays.
Current advances in correlative multi-scale and multi-modal 2D/3D imaging of nanoporous geomaterials, such as shales, provide a tremendous opportunity to characterize and represent these rocks over multiple length scales – from core-to pore-scale. Subsequently, these image datasets can be then used for advanced image analysis and digital rock modeling to reconstruct 2D/3D models used to analyze their petrophysical properties.
In this study, the Mancos Shale from the Uinta Basin – one of the most promising shale plays in the United States (Hawkins et al. 2016) – was characterized from centimeter-to nanometer-scale via digital rock analysis using correlative micro 3D X-ray computed tomography (micro-CT), micro 3D X-ray microscopy (micro-XRM), light microscopy (LM) mosaic map, scanning electron microscopy (SEM) mosaic map, and focused ion beam (FIB) – SEM (FIB-SEM) nano-tomography image datasets. These multi-resolution and multi-dimensional image datasets, were then registered with each other, and used to reconstruct digital rock 2D/3D models from which shale petrophysical properties such as lithology, mineralogy, and porosity were extracted. Additionally, the imaged mineralogy and porosity results were compared with traditional laboratory experimental data, such as XRD and helium porosity measurements.
Mature Field Revitalisation: An Integrated, Rapid, Collaborative Approach for Candidate Selection and Reservoir Optimization
Authors: Mazzlan Khairul Akmal , Chia Mabel Pei Chuen , Tamin Muhammad , Tugimin M Azri B A , Azlan Ali Al-Amani , Michael Lester Tugung , Sepulveda Willem , Cortez V. Juan L. , Muhamed Salim Muzahidin , Kalidas Sanggeetha , Chan Nathanael Vui Kit , Biniwale Shripad , Serbini Feroney , Mohd Arifin Azahari , Tan Tina Lee Ting , Tee Karen Ying Chiao 
Published: April 2019

Abstract
‘S’ field is a mature oilfield located offshore Sabah, Malaysia. As part of the redevelopment plan, ‘S’ field was the first field selected for an end-to-end asset management Integrated Operations (IO project) where multiple workflows have been implemented for the asset operation optimization through monitoring and surveillance. One of the exclusive workflow that will be further elaborated in this paper is on Candidate Selection and Reservoir Optimization.
Although field optimization mission was ongoing, proper knowledge capture and standardization of such techniques were not adequate due to the limited data management. Lack of decision-support mechanism and most importantly the challenge was of understanding and analysing the asset performance. A key to the success of field and reservoir optimization is defining a tailored approach, for selection of right candidate and collaborative decision for well/field intervention.
With an objective of full field revitalization, the project was focused on integrated, collaborative 3R approach – Reliability, Reusability and Repeatability. Reliability component was based on capturing knowledge from experienced professionals from various domains and blending that with traditionally proven analytical techniques. Reusability was emphasized by the development of consistent and robust analysis workflows ready to use. Repeatability was aiming at standardizing the process of candidate selection and decision making to assist junior engineers.
Enhancing Velocity Model for Gas Cloud Using First Break Travel Time Tomography Full Waveform Inversion
Authors: Prajapati Srichand , Ghosh Deva 
Published: April 2019

Abstract
The velocity model is of a great importance for geological as well as structural properties of complex structure such as gas cloud. Instead of ray-based techniques, eikonal wavefield tomography can provide a higher resolution velocity model for seismic images. We have implemented first break travel time tomography to enhance the initial velocity model for seismic full waveform inversion (FWI) for better imaging rather than guess initial velocity model for FWI. The First-break travel time concept is based on the eikonal equation, relies on inversion to resolve the complex gas cloud imaging. It allows not only the receivers but the shots to change position along the ray path. Tomography results are useful particularly significant in the presence of noise, scattering in the data. We have implemented this approach on marmousi as well as gas cloud model and output are used as input velocity model for FWI and results of proposed approach is more robust than the traditional with faster convergence.
A New Method to Predict Residual Oil Saturation of Light Oil Reservoir Based on Big Data Analytics
Authors: Luo Xianbo , Li Jinyi , Yang Dongdong , Shi Hongfu 
Published: April 2019

Abstract
The relative permeability test (RPT) plays an important part in production prediction, the law of water cut increasing analysis, the research on recovery factor and the reservoir numerical simulation. The residual oil saturation is one of the most significant parameters of RPT. While literature on the quality control of RPT is limited, the experimentalists make qualitative judgments on the rationality of the key data estimate based on their own experience. A new method is presented to predict residual oil saturation of light oil reservoirs.
Study on Seven-Property Relation of Tight Oil Reservoirs via Well Logs
Authors: Mao Rui , Wang Zhenlin , Zhang Ni 
Published: April 2019

Abstract
Tight oil is more and more important in petroleum industry. However, there is few systematic studies up to now. Based on core data, petrophysical experimental data, well logs and test data, a systematic study on seven-property relation of tight oil reservoirs is conducted in Lucaogou Formation, Permian, Jimusaer Sag, Junggar Basin, China. To be specific, firstly, lithology is identified via cross plot of matrix density and constructed structure index from nuclear magnetic resonance (NMR) log. Secondly, using iteration method, lower limit T2 relaxation time value of effective pore and oil-filled porosity are calculated to be 1.7 ms and 7 ms respectively, which is a new way to predict porosity and oil saturation in tight oil reservoirs. Thirdly, high resolution resistivity (Rt) curve is acquired from micro-resistivity imager log by dimension reduction process and piecewise calibration. Total organic carbon (TOC) content is predicted by curve overlapping method of interval transit time (AC) and the obtained high-resolution resistivity. Fourthly, according to the transformation of dynamic and static rock mechanics parameters, an improved method is proposed for quantitatively predicting brittleness index (BI) of rocks via well logs. Ground stress value and direction are evaluated by various methods comprehensively. This study obtains a good application effect and provides important technical support for evaluating tight oil reservoirs via well logs, which plays an important role in tight oil exploration and development and new oil field discovery.
Effect of Pore Pressure Depletion on Wellbore Stability and Hydraulic Fracturing in Sandstone Reservoir
Authors: Al-Dossary Mohammed , Hamid Osman , Elkatatny Salaheldin 
Published: April 2019

Abstract
Development of stress sensitive reservoirs, especially in challenging environment, is raising awareness that geomechanics is a vital aspect of reservoir management. Understanding reservoir geomechanical behavior becomes more and more important for petroleum industry. A significant changes in formation pressure caused by depletion will induce deformation and stress/strain changes in the reservoir and the surrounding formation, understanding in-situ stresses and how stress changes in and around the reservoir due to depletion is important in a multidisciplinary approach to reservoir characterization and management.
These changes in stresses/strain affect the reservoir as well as the overburden and underburden formation, which directly affect drilling and stimulation operations strategies. Reservoir compaction, shear casing and well damage, cap-rock integrity, fault reactivation and sand production can occur during reservoir depletion.
To address these issues, 3D geomechanical models have been developed (which describe the state of stresses in the reservoir and overburden). 4D geomechanics models (dynamic models, that describe the changes in stress over time with production are required.
Reservoir Geomechanics approaches, this paper address and answer the following questions:
How Geomechanics changes in stresses and strain impact wellbore stability related issues and stimulation operations?
Stress rotation around faulted zones.
How depletion effect hydraulic fracture growth?
Wellbore Failure Modelling Using a Modified Drucker-Prager Criterion
Authors: Younessi Ahmadreza 
Published: April 2019

Abstract
The failure around the wellbore is studied using a modified form of Drucker-Prager failure criterion (MDP). The MDP has a linear shear failure envelope in the meridian plane and a curve-sided triangular failure curve in the deviatoric plane which can be controlled by the yield stress ratio (K). The model is compatible with the rock strength measurements under true-triaxial stress conditions similar to the modified Lade (MLa) failure criterion.
The MDP is used for stress modelling and wellbore stability analyses. The stress modelling is done by calculating the magnitude of maximum horizontal stress (SHmax) from the observed failure in the image log. The stress modelling result is compared with the results from the Mohr-Coulomb (MC), Drucker-Prager (DP), and modified Lade (MLa) criteria. The MDP is also used for wellbore stability analysis using both analytical and numerical (finite element) approaches. The analytical approach is used to conduct a sensitivity analysis to investigate the impact of well trajectory on minimum required mud weight. The finite element analysis is conducted to investigate the dimension of the breakout developed under different mud weights. The results are compared against the other failure criteria.
The SHmax magnitude calculated from the MDP model falls between the MC and DP. The analysis shows that the MDP with K = 0.778 is compatible with the MLa results for a rock with an internal friction angle close to 30° for both SHmax magnitude modelling and minimum required mud weight calculations for the wellbore stability analysis.
The results from the finite element analyses shows that the calculated breakout widths and depths using the MDP model falls between the MC and DP models. The analysis shows that in the presented case, although the calculated breakout width is large, the depth and amount of failed material around the wellbore are relatively small to create any wellbore instability problems. Hence, a mud weight relatively lower than calculated required mud weight from the analytical approach can be used in practice to drill the well. The results show the importance of considering the depth of the breakouts in the mud weight design.
Integrated Study of Shear Wave and Resistivity Anisotropy in an Inclined Well Providing Insight to the Geomechanical Model
Authors: Das Saikat , Zhang Tingting , Vevakanandan Valsan , Abd Rahim Mohd Helmi 
Published: April 2019

Abstract
Understanding in-situ stress orientations and magnitudes is critical in building a geomechanical model which helps in planning and execution of a development and production programme for any hydrocarbon reservoirs. The azimuthal anisotropy analysis from cross-dipole acoustic data is commonly used to derive the direction of maximum-horizontal stress. However, the interpretation of the stress orientation is challenging in inclined wells where anisotropy may also be influenced by the relative angle of bedding plane to the bore hole. Integration of the data becomes of paramount importance to correctly interpret the stress distribution.
Cross dipole wireline acoustic, 3D resistivity and 6 arm calliper data were acquired in a deviated well, offshore, Malaysia. Acoustic data was processed for azimuthal anisotropy, 3D-resistivity data was processed for formation dip, azimuth, horizontal and vertical resistivity and 6 arm calliper data was used to generate borehole shape. Acoustic analysis provided the difference in fast and slow shear wave velocities and the azimuth of fast shear. The resistivity anisotropy, dip and azimuth and bore hole shape information was incorporated to interpret effect of the dipping bed in the scheme of relating acoustic anisotropy to the formation stress.
Meaningful difference in the fast and slow shear velocities (in two orthogonal direction) is observed in this well. The fast shear wave azimuth of NW-SE is consistent with the regional trend. However, the presence of laminated shale interval in the inclined bore hole imparts uncertainty in relating the anisotropy to the stress field. The formation dip and azimuth obtained from the resistivity anisotropy provided the framework of the interpretation by identifying the intervals with higher relative dip and the associated anisotropy perceived by it. Bore hole ovalization also provides the necessary input to the interpretation scheme which is supported by the existing field wide geomechanical model.
Integrating all datasets resolved potentially ambiguous interpretation of the source of azimuthal acoustic anisotropy. This approach determines the cause of the anisotropy (unbalanced stress in formation vs. dipping beds and shale transverse anisotropy). The result provides valuable information to refine the existing geomechanical model which can be used in future well placement and planning, optimum mud weight design, and constraining water injection operating limit during the life of the field.
Improved Estimation and Forecast Through Model Error Estimation – Norne Field Example
Authors: Lu Minjie , Chen Yan 
Published: April 2019

Abstract
The ensemble based methods (especially various forms of iterative ensemble smoothers) have been proven to be effective in calibrating multiple reservoir models, so that they are consistent with historical production data. However, due to the complex nature of hydrocarbon reservoirs, the model calibration is never perfect, it is always a simplified version of reality with coarse representation and unmodeled physical processes. This flaw in the model that causes mismatch between actual observations and simulated data when ‘perfect’ model parameters are used as model input is known as ‘model error’. Assimilation of data without accounting for this model error can result in incorrect adjustment to model parameters, underestimation of prediction uncertainties and bias in forecasts.
In this paper, we investigate the benefit of recognising and accounting for model error when an iterative ensemble smoother is used to assimilate production data. The correlated ‘total error’ (combination of model error and observation error) are estimated from the data residual after a standard history matching using Levenberg-Marquardt form of iterative ensemble smoother (LM-EnRML). This total error is then used in further data assimilations to improve the model prediction and uncertain quantification from the final updated model ensemble. We first illustrate the method using a synthetic 2D five spot case, where some model errors are deliberately introduced, and the results are closely examined against the known ‘true’ model. Then the Norne field case is used to further evaluate the method.
The Norne model has previously been history matched using the LM-EnRML (Chen and Oliver, 2014), where cell-by-cell properties (permeability, porosity, net-to-gross, vertical transmissibility) and parameters related to fault transmissibility, depths of water-oil contacts and relative permeability function are adjusted to honour historical data. In this previous study, the authors highlighted the importance of including large amounts of model parameters, proper use of localization, and adjustment of data noise to account for modelling error. In the current study, we further improve the aspect regarding the quantification of model error. The results showed promising benefit of a systematic procedure of model diagnostics, model improvement and model error quantification during data assimilations.
Combining Dimensional Analysis and Neural Networks to Improve Flow Assurance Simulations
Authors: Bratland Ove 
Published: April 2019

AbstractWhen developing mathematical models for two- and three-phase flow in long pipelines, the most difficult challenge is to model the frictions, volume fractions and flow regimes accurately. This paper combines 3 different methods when confronting that problem: Dimensional analysis, mechanistic models, and Neural Networks (NN). It is shown that those methods supplement each other in important ways. The dimensional analysis is helpful in upscaling laboratory measurements to full-scale flowlines. In case of 3-phase gas oil water flow, the number of dimensionless groups turn out to be 14. NNs offer a way of correlating that many variables, and that allows the model to account for all dimensionless groups for all types of flow. That overcomes the limitations inherent in the more common practice of focusing on only a few parameters or dimensionless groups for each type of flow regime. But introducing NNs creates a new challenge: We need data to train them.The last problem is partly dealt with by building on well-established mechanistic models with various factors inserted. It is those factors which are trained by the NNs, not the dependent dimensionless groups themselves. Using mechanistic models rather than a pure "black box" approach leads to much faster training and more accurate results. That has made it possible to train the NNs based on a more moderate and therefore realistic amount of data than would otherwise be required.The novel approach has been used to develop new software. The FlowRegimeEngine, as it is called, is now incorporated in several steady-state and transient commercially available computer codes. At the end of this presentation results from one of them, FlowlinePro, have been compared to results from the well-established computer code OLGA. The results turned out to be very similar.The presented dimensional analysis also provides an interesting way of testing commercial software by checking whether results are dimensionally consistent. When doing steady-state simulations with two or more different data sets, chosen so that they form the same independent non-dimensional groups, the resulting dependent dimensionless groups should come out identical. If they do not, it is reason to treat the results with suspicion. When applying the test to FlowlinePro and OLGA, they both passed it nicely for the data-sets chosen here.
Improving Drilling Simulation Computation Performance with Smart Logic and Machine Learning
Authors: Mu Chao , Liu Jitang , Chen Rongbing , Bolchover Paul , Suryadi Hendrik , Yu Tao 
Published: April 2019

Abstract
Modeling and simulation play a key role in well construction planning, which can help to evaluate and optimize the engineering designs for a well. Today, many simulations use finite element analysis (FEA) and computational fluid dynamics (CFD) to model complex dynamic downhole conditions and behaviors of drilling tools. However, one challenge is that the complex simulation may take a few hours to run, which limits the usage to only a few well planning jobs. This limitation also poses as barrier in real-time monitoring applications, where under one second computation speed is required. In this paper, two approaches are presented for improving the performance of drilling simulations: smart depth selection logic for BHA tendency calculation, and reduced order model using machine learning for motor optimization modeling.