bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comprehensive Review of Smart/Intelligent Oilfield Technologies and Applications in the Oil and Gas Industry
Authors: Temizel Cenk , Canbaz Celal Hakan , Palabiyik Yildiray , Putra Dike , Asena Ahmet , Ranjith Rahul , Jongkittinarukorn Kittiphong 
Published: March 2019

Abstract
Smart field technologies offer outstanding capabilities that increase the efficiency of the oil and gas fields by means of saving time and energy as far as the technologies employed and workforce concerned given that the technology applied is economic for the field of concern. Despite significant acceptance of smart field concept in the industry, there is still ambiguity not only on the incremental benefits but also the criteria and conditions of applicability technical and economic-wise. This study outlines the past, present and the dynamics of the smart oilfield concept, the techniques and methods it bears and employs, technical challenges in the application while addressing the concerns of the oil and gas industry professionals on the use of such technologies in a comprehensive way.
History of smart/intelligent oilfield development, types of technologies used currently in it and those imbibed from other industries are comprehensively reviewed in this paper. In addition, this review takes into account the robustness, applicability and incremental benefits these technologie bring to different types of oilfields under current economic conditions. Real field applications are illustrated with applications in different parts of the world with challenges, advantages and drawbacks discussed and summarized that lead to conclusions on the criteria of application of smart field technologies in an individual field.
Intelligent or Smart field concept has proven itself as a promising area and found vast amount of application in oil and gas fields throughout the world. The key in smart oilfield applications is the suitability of an individual case for such technology in terms of technical and economic aspects. This study outlines the key criteria in the success of smart oilfield applications in a given field that will serve for the future decisions as a comprehensive and collective review of all the aspects of the employed techniques and their usability in specific cases.
Even though there are publications on certain examples of smart oilfield technologies, a comprehensive review that not only outlines all the key elements in one study but also deducts lessons from the real field applications that will shed light on the utilization of the methods in the future applications has been missing, this study will fill this gap.
Modeling of Unconventional Reservoirs: A Case Study from Bahrain’s Khalij Al-Bahrain Basin
Authors: Jackson Gregory , Almoulani Ghada , Al Ansari Yahya , Viamontes Jorge 
Published: March 2019

Abstract
Bahrain has begun exploring unconventional resources in the Khalij Al-Bahrain Basin for the Tuwaiq Mountain Formation. This work is a case study presenting the workflow for characterizing and modeling the unconventional development in Bahrain all the way from petrophysics through geology, completion modeling, and dynamic simulation.
The work scope consisted of petrophysical modeling 10 key wells including calibration to core data. The petrophysics showed that the lower Tuwaiq Mountain interval with its TOC signature is remarkably consistent across all of Bahrain. The wells modeled in a 3D geological model with reservoir properties distributed throughout the reservoir to confirm resource in-place estimates published in early 2018. Well stimulation treatment on Well 1 was modeled and tied to the production test. A dynamic model was subsequently built to history match the production test. While not unique in its production match, this calibration is an important step for future optimizations in lieu of microseismic data. All of this information was used to form the basis for optimal completions to refine the next appraisal wells with forecasted production rates.
The Tuwaiq Mountain reservoir has commercial potential in Bahrain, particularly in the western area where producibility has been proven. Producibility in the East has not been established as no production tests are available. In addition, future appraisal well locations were identified using the 3D geological model. The best trajectory was chosen such that the wells are estimated to yield EURs more than 500,000 bbls.
The results of this project are important for Bahrain as it highlights the unconventional resource and production potential in the country. For the industry, unconventional development is in its early stages outside of North America and Bahrain’s case study can be utilized to expedite the learning curve in many other basins.
Machine Learning and the Analysis of High-Power Electromagnetic Interaction with Subsurface Matter
Authors: San-Roman-Alerigi Damian , Batatseh Sameeh , Li Weichang , Othman Haitham 
Published: March 2019

Abstract
This work is an ongoing effort to design a numerical platform based on machine learning algorithms to characterize, predict, optimize and guide the interaction of [high power] electromagnetic (HPEM) sources (laser, microwave, RF, etc.) with subsurface matter (e.g. rocks, oils, brines, etc.). Advanced statistical analysis routines are essential to identify key variables and relations in the thermal- mechanical-electromagnetic coupling in heterogeneous and anisotropic materials.
Advanced statistical analysis and machine learning have been recently used to evince relations in complex environments and physical dynamics; e.g. fluid dynamics, P&ID analytics, and drill cuttings classification, to cite a few. The methods make use of sophisticated algorithms to classify and model problems in multiple areas, from image processing to certain optimization problems. In the realm of subsurface photonics, and in particular for high power electromagnetic (HPEM) interaction with subsurface matter, these routines could become essential to identify key variables, assess the environment and process, and evince models to predict the outcome of an inherently multiphysics and multi-dimensional problem.
Numerical models that capture the interaction between HPEM sources and subsurface matter are essential to predict, optimize, adapt, and evaluate the process prior to, and during, deployment in subsurface. These models can come as the solution to a set of coupled partial differential equations that fully describe the physical dynamics, or as the result of supervised-learning algorithms and analysis of experimental and field data. The former is highly sensitive to dynamic material properties, environmental conditions, and source parameters. In addition, it can be challenging to characterize the properties of subsurface materials over the wide range of temperatures and pressures observed in the process. Thus, a machine learning method could provide an ever-improving alternative that learns from the available data to build a numerical platform that can predict, optimize, and guide the process.
Machine learning and advanced statistics provide a compelling alternative to build numerical tools to predict, optimize, and control physical processes. This work introduces a variety of numerical approaches to identify essential variables, predict their impact, and optimize the outcome for subsurface applications. Combined, the methods described in this work can help guide the control of the governing dynamics and parameters for use in multiple applications. This numerical platform can be extended to other applications, enhance experimental prototypes, and advance the design of a comprehensive numerical tool for downhole HPEM operations.
3D Elastic Seismic Modeling of Large Scale Land Survey
Authors: Etienne Vincent , Tonellot Thierry , Janoubi Emad , Shakhs Husain 
Published: March 2019

Abstract
We present a software framework tailored to seismic applications embedding high performance features. Especially, we discuss the spatial cache-blocking algorithm for the Finite-Difference Time-Domain (FDTD) method and the protocol to find optimal parameters. We apply our modeling engine to perform 3D elastic seismic modeling on a large-scale 877 km2 land survey. The velocity and density models are built with geological stratigraphic forward modeling. In total, 162300 shot gathers are computed with a maximum frequency of 20Hz and a maximum offset of 5 km. The main trait of the computed data is the complexity related to the surface-waves, especially their long and dispersive wave-train obscuring other wave arrivals. Computations were performed on the Kaust supercomputer Shaheen II. The modeling campaign took one month to complete using in average 2434 compute nodes in parallel. This achievement represents a workload of 59.6 ExaFLOP on a current PetaFLOP/s machine. This indicates that the next generation of supercomputers targeting the ExaFLOP/s sustained performance, would allow reducing the run-time of our application to one hour or less. With such performance, it is reasonable to predict that 3D elastic imaging will be a routinely used algorithm by seismic exploration in the years to come, while nowadays it still requires leading-edge hardware.
Targeted Interventions to Prevent Chronic Low Back Pain in High Risk Patients: Development and Delivery of a Pragmatic Training Course of Psychologically Informed Physical Therapy for the TARGET Trial
Authors: Beneciuk Jason , George Steven Z. , Greco Carol , Schneider Michael , Wegener Stephen T. , Saper Robert , Delitto Anthony 
Published: March 2019

Abstract
Background: Low back pain (LBP) is a public health concern because it is highly prevalent and the leading cause of disability worldwide. Psychologically Informed Physical Therapy (PIPT) is a secondary prevention approach that first aims to identify individuals at high risk for transitioning to chronicity and then provides tailored treatment to reduce that risk. Training models that are feasible to implement with acceptable training quality are needed to improve scalability for widespread implementation of PIPT. This manuscript describes the PIPT training program that was developed for training physical therapists providing PIPT in the TARGET Trial. Methods: The PIPT training program was developed, tested and modified using an iterative process. Content development consisted of stakeholder engagement, beta testing, modification of training and confirmation of final course objectives. Methods of delivery consisted of a website that included brief online educational modules followed by a live 8-hour workshop that included video-based mock case scenarios and case-based role playing. Attitudes, beliefs and confidence in implementing PIPT principles were assessed before and immediately after training to measure training quality and impact. Results: Early stakeholder engagement and beta testing indicated the need for increased emphasis on experiential learning opportunities and patient-centered communication training. Booster training varied extensively across TARGET sites with involvement of ‘clinician champions’ providing brief follow-up sessions identified as best practice. Favorable post training changes in physical therapist attitudes and beliefs toward biopsychosocial treatment orientation and increased confidence in implementing PIPT principles were observed. Conclusions: PIPT training for provider participation in the TARGET Trial was feasible to deliver. Course content was acceptable to physical therapists and resulted in improved beliefs and confidence in applying PIPT skills during clinical practice. Ongoing consultation and site-based continuing education were methods by which specific TARGET sites maintained or augmented PIPT skill training, however implementing ongoing training was challenging in general. Due to the pragmatic nature of the TARGET trial, it was not possible to directly measure the effect of PIPT training on treatment fidelity which was a limitation of our approach. Trial registration: ClinicalTrials.gov Identifier: NCT02647658
Protocol for a randomised controlled trial to evaluate the effectiveness of improving tuberculosis patients’ treatment adherence via electronic monitors and an app versus usual care in Tibet
Authors: Wei Xiaolin , Hicks Joseph P , Pande Pasang , Zhang Zhitong , Haldane Victoria , Liu Xiaoqiu , Yin Tingting , Wang Lixia , Shi Dachun , Ge Shiliang , Walley John , Upshur Ross , Hu Jun 
Published: March 2019

Abstract
Background: Treatment non-adherence is a serious challenge to effective tuberculosis (TB) control in Tibet. In this study we will pilot and evaluate the effectiveness of using new electronic monitors (e-monitors) and a smartphone app to improve treatment adherence among new pulmonary TB patients in Tibet.
Methods: We will use a multicentre, parallel-group, individually-randomized controlled superiority trial with blinded outcome evaluation and unblinded treatment. We will randomise new pulmonary TB outpatients (aged ≥15 years old and free from communication impairment) from Shigatse, Tibet to either the intervention or control arm in a 1:1 ratio at the time of their diagnosis. All patients will be treated according to the World Health Organisation standard 6-month TB treatment regimen and the China National TB programme guidelines. Intervention arm patients will be given their medication via e-monitors that have automatic voice reminders, and record medication adherence data and share it with health staff via cloud connection. Intervention patients will also be encouraged of receiving smartphone-based video observed treatment if their adherence is problematic. Control arm patients will receive their medication in e-monitors that will collect medication adherence history, but will have their reminder function deactivated and are not linked to the app. The primary outcome is the rate of poor adherence, measured monthly during treatment as a binary indicator where poor adherence means missing ≥20% doses in a month. We will conduct a qualitative process evaluation to explore operational questions regarding acceptability, cultural appropriateness and burden of technology use, as well as a cost-effectiveness analysis and an analysis of the long-term effects of the intervention on TB control.
Discussion: Our study is one of the first trials to evaluate the use of e-monitors and smartphone apps for customised treatment support in low- and middle-income countries (LMICs). All intervention activities are designed to be embedded into routine TB care with strong local ownership. Through the trial we intend to understand the feasibility of our intervention, its effectiveness, its cost-effectiveness and its long-term impacts to inform future scale-up in remote areas of China and other LMICs.
Trial registration: Current Controlled Trials ISRCTN52132803, registered on 9 November 2018.
Insomnia and autonomic dysfunction may identify patients at risk for developing early Parkinson's disease dementia
Authors: Jurcau Anamaria , Nunkoo Vharoon Sharma 
Published: March 2019

Abstract
Background: The prospective study aims at identifying features predictive of early onset of dementia in patients with Parkinson's disease (PD). Methods: 89 non-demented PD patients underwent a complex evaluation (demographic data, UPDRS, Unified Multiple System Atrophy Rating Scale - UMSAR, Insomnia Severity Index - ISI, Neuro-Psychiatric Inventory - NPI, Hamilton Depression Rating Scale - HDRS, Mini Mental State Examination) at baseline and at 3-year follow-up. Results: At 3-year follow-up 43.8% of patients developed dementia. An ordinal regression of MMSE at follow-up showed that dementia developed in patients with autonomic dysfunctions (odds ratio 16.18, 95% CI 3.16 to 82.77, p = 0.001), old age (odds ratio 1.24, 95% CI 1.11 to 1.39, p < 0.001), and insomnia (odds ratio 1.23, 95% CI 1.09 to 1.38, p = 0.001). Conclusion: Patients with signs of autonomic dysfunction and insomnia are at higher risk for developing dementia and deserve closer monitoring of cognitive symptoms.
Validation of the REM sleep behavior disorder screening questionnaire in the Czech population
Authors: Bušková Jitka , Peřinová Pavla , Miletínová Eva , Dušek Petr , Růžička Evžen , Šonka Karel , Kemlink David 
Published: March 2019

Abstract
Background

Idiopathic rapid eye movement sleep behavior disorder (RBD) affects 1 – 2% of people over 60 years of age, and presents a high risk of developing a neurodegenerative disorder from the group of synucleinopathies, such as Parkinson’s disease, dementia with Lewy bodies and multiple system atrophy. Therefore, screening tools are needed. In 2007, the RBD screening questionnaire (RBD-SQ) was developed and has been translated into several languages. Aim of the study: The aim of study was to assess the validity and reliability of the Czech version of the RBD-SQ in a mixed population of sleep clinic patients, supplemented by healthy volunteers and RBD patients.

Methods

Participants included 81 RBD patients, 225 patients with other sleep disorders (obstructive sleep apnea, insomnia, restless legs syndrome and periodic limb movement disorder, other parasomnias, or central hypersomnias including narcolepsy) and 20 healthy volunteers.

Results

The mean RBD-SQ score in the RBD patients was 9.4 ± 2.8 points, and in the non-RBD group it was 4.5 ± 3.0 (P < 0.0001). Receiver -operator analysis yielded an area under the curve of 0.864, suggesting good diagnostic performance of the scale. When using a cut-off value for positivity of 5 points, sensitivity was 0.89 and specificity was 0.62.

Conclusions

The Czech version of the RBD-SQ is a sensitive tool for screening for RBD patients and helps to identify subjects for complete clinical workup.
Outcome prediction in disorders of consciousness: the role of coma recovery scale revised
Authors: Lucca Lucia Francesca , Lofaro Danilo , Pignolo Loris , Leto Elio , Ursino Maria , Cortese Maria Daniela , Conforti Domenico , Tonin Paolo , cerasa antonio 
Published: March 2019

Abstract
Background: To evaluate the utility of the revised coma remission scale (CRS-r), together with other clinical variables, in predicting emergence from disorders of consciousness (DoC) during intensive rehabilitation care.
Method: This is a prospective observational cohort study of consecutive 180 brain-injured patients with prolonged DoC upon admission to neurorehabilitation unit. 123 patients in a vegetative state (VS) and 57 in a minimally conscious state (MCS) were included and followed for a period of 8 weeks in the intensive care rehabilitation unit. Demographical and clinical factors were used as outcome measures. Univariate and multivariate Cox regression models were employed for examining potential predictors for clinical outcome along the time.
Results: VS and MCS groups were matched for demographical and clinical variables (i.e., age, aetiology, tracheostomy and route of feeding). Within 2 months after admission in intensive neurorehabilitation unit, 3.9% were dead, 35.5% had a full recovery of consciousness and 66.7% remained in VS or MCS. Multivariate analysis demonstrated that the best predictor of functional improvement was the CRS-r scores. In particular, patients with values greater than 12 at admission were those with a favorable likelihood of emergence from DoC.
Conclusions: Our study highlights the role of the CRS-r scores for predicting a short-term favorable outcome.
Berberine Alleviates Amyloid-beta Pathogenesis Via Activating LKB1/AMPK Signaling in the Brain of APP/PS1 Transgenic Mice
Authors: Cai Zhi-You , Wang Chuan-Ling , Lu Tao-Tao , Yang Wen-Ming 
Published: March 2019

Background:Liver kinase B1 (LKB1)/5’-adenosine monophosphate-activated protein kinase (AMPK) signaling, a metabolic checkpoint, plays a neuro-protective role in the pathogenesis of Alzheimer’s disease (AD). Amyloid-β (Aβ) acts as a classical biomarker of AD. The aim of the present study was to explore whether berberine (BBR) activates LKB1/AMPK signaling and ameliorates Aβ pathology.Methods:The Aβ levels were detected using enzyme-linked immunosorbent assay and immunohistochemistry. The following biomarkers were measured by Western blotting: phosphorylated (p-) LKB1 (Ser334 and Thr189), p-AMPK (AMPKα and AMPKβ1), synaptophysin, post-synaptic density protein 95 and p-cAMP-response element binding protein (p-CREB). The glial fibrillary acidic protein (GFAP) was determined using Western blotting and immunohistochemistry.Results:BBR inhibited Aβ expression in the brain of APP/PS1 mice. There was a strong up-regulation of both p-LKB1 (Ser334 and Thr189) and p-AMPK (AMPKα and AMPKβ1) in the brains of APP/PS1 transgenic mice after BBR-treatment (P<0.01). BBR promoted the expression of synaptophysin, post-synaptic density protein 95 and p-CREB(Ser133) in the AD brain, compared with the model mice.Conclusion:BBR alleviates Aβ pathogenesis and rescues synapse damage via activating LKB1/AMPK signaling in the brain of APP/PS1 transgenic mice.
Multiplex PCR is a Rapid, Simple and Cheap Method for Direct Diagnosis of M. tuberculosis from Sputum Samples
Authors: Alnour Tarig M.S. , Abuduhier Faisel , Khatatneh Mohammed , Albalawi Fahad , Alfifi Khalid , Silvala Bernard 
Published: March 2019

Introduction:
Rapid diagnosis of M. tuberculosis directly from sputum samples is a
challenging process. This study aimed to design and evaluate a multiplex-PCR method for direct
diagnosis of M. tuberculosis from sputum specimens.

Materials and Methods:
46 suspected tuberculosis patients and 25 apparently healthy individuals
were enrolled in the study. Sputa were collected from the study population and processed
by cold ZN stain. DNA was extracted from each sample and processed by Multiplex
PCR and Genotype Mycobacteria CM.

Results:
Out of the 46 Tuberculosis suspected patients, 22 (47.8%) revealed positive Acid
fast ba- cilli (AFB), while 19 (41.3%) showed positive by both multiplex PCR and Genotype
Mycobacte- ria CM. The overall sensitivity of multiplex PCR and smear microscopy were
100% while the specificity were 100, and 86.3%, respectively.

Conclusion:
Multiplex PCR method using two different sets of primers in combination with
other diagnostic tools such as X-Rays and smear Microscopy are cheap, rapid and reliable
methods for the diagnosis of M. tuberculosis from clinical samples and are able to identify
most of the smear positive cases with valuable accuracy.