bluuebunny/crossref_metadata_2025_split huggingface dataset:
Is Digital Nomadism the Answer to Work Life Balance?
Authors: Gustave Nicole , Alarfaj Abdullah 
Published: December 2021

Abstract
The world is currently experiencing a rude awakening because of the COVID-19 pandemic and in a matter of months businesses averse to trust the benefits of remote working have been compelled to adapt. This advantage has enabled many Human Resource (HR) Professionals to revisit the dreaded topic of flexible working, as the new normal has shown that it is not where you work but the work you produce that matters.
Ironically, the age-old question of work-life balance surfaces as individuals search for the purpose of life as the pandemic brings everyone to their knees and philosophically people question what exactly is this balance. For HR Professionals this question is not personal but a matter of their profession in providing companies with a wider lens to understand that in order to remain competitive they need to adapt to change. One of the ways is to develop an open mindset and flexibility to revise their policies on types of flexible working, which offers work-life balance and positively impacts their ability to retain and attract highly skilled talent. This article examines the concept of Digital Nomadism as one of the radical yet realistic ways to achieve work-life balance.
Digital Nomadism puts a new spin on work arrangements and is a movement of highly mobile workers who dictate where they work, how they adapt to the demands of work to suit their lifestyle and find balance; with digital technologies. The concept has been around since 2014, the history of nomadism even longer but what is new, and why this subject adds value is the ingenuity of technology, how it makes this way of working a reality and the increasing numbers of digital nomads. The research suggests that approximately several hundred thousand of digital nomads exist throughout the world and numbers continues to rise due to globalization and the need for talent to be flexible with their lifestyles and work.
Interestingly, while many companies are convinced of the technological disruptors and how it changes the face of work from a technical perspective, the flexibility of work patterns remains a hard sell in some cases. Consequently, recruiting for talent, employment contracts and the way work is organized, remains the same and lacks flexibility. This limits the opportunity to remain competitive, retain or attract top talent and drive innovation at all angles of the business. This paper will confirm whether the solution to work-life balance is the notion of digital nomadism, detailing how it works, its benefits and issues, with the intention to offer an option to forward thinking companies, reasons to adapt their flexible working policies.
Energy Harvesting Under Harsh Conditions for the Oil & Gas Upstream Industry
Authors: Correia José , Rodrigues Cátia , Esteves Ricardo , Bezerra de Melo Ricardo Cesar , Gutiérrez José , Pereira André , Ventura João 
Published: December 2021

Abstract
Environmental and safety sensing is becoming of high importance in the oil and gas upstream industry. However, present solutions to feed theses sensors are expensive and dangerous and there is so far no technology able to generate electrical energy in the operational conditions of oil and gas extraction wells. In this paper it is presented, for the first time in a relevant environment, a pioneering energy harvesting technology based on nanomaterials that takes advantage of fluid movement in oil extraction wells. A device was tested to power monitoring systems with locally harvested energy in harsh conditions environment (pressures up to 50 bar and temperatures of 50ºC). Even though this technology is in an early development stage this work opens a wide range of possible applications in deep underwater environments and in Oil and Gas extraction wells where continuous flow conditions are present.
High Performance Friction Reducer for Slickwater Fracturing Applications: Laboratory Study and Field Implementation
Authors: Al-Hulail Ibrahim , Arauji Oscar , AlZaki Ali , Zeghouani Mohamed 
Published: December 2021

Abstract
Proppant placement in a tight formation is extremely challenging. Therefore, using a high viscous friction reducer (HVFR) as a fracturing fluid for stimulation treatment in tight gas reservoirs is increasing within the industry because it can transport proppant, help reduce pipe friction generated during hydraulic-fracturing treatments, and efficiently clean up similar to the lower viscosity friction reducers (FRs). In this paper the implementation of the robust HVFR that is building higher viscosity at low concentrations, which minimizes energy loss and promotes turbulent flow within the pipe during the pumping of low viscosity, is discussed in detail.
Performance evaluation of the new HVFR was conducted in the laboratory and compared to the lower viscosity FR. The study consisted of viscosity measurements at 70 and 180°F, compatibility with other additives, and proppant transport capabilities. Additionally, the viscosity generated from both FRs was compared using two water sources: water well A and treated sewage water. Viscosity measurements were performed across a wide range of FR and HVFR concentrations and under varying shear rates using a digital viscometer.
To validate drag reduction capabilities for this HVFR in the field, the same groundwater with low salinity and low total dissolved solids (TDS) content were used for comparison purposes. The test plan for this new HVFR was for a well to be drilled to a total depth of 17,801 ft MD (10,693 ft TVD) with a 6,016-ft lateral section. Another part of the plan was to complete 41 stages—the first stage with the toe initiator, and subsequent stages using ball drops until Stage 8, were completed using the current FR. For Stage 8, the drag reduction from the new HVFR was evaluated against the current FR only during the pad stage. Then, FR or HVFR concentrations were used, with a gradual reduction from 2 to 1 gpt without compromising proppant placement from stages 9 to 37, alternating current FR and the new HVFR every four stages. From Stage 38 to 41, the same approach was used but with treated sewage water and alternating every other stage using current FR or HVFR at 1gpt.
The implementation of the new HVFR showed better friction reduction when using the same concentration of the current FR. Also, achieving better average treating pressures with lower concentration. Based on that it is a cost-effective solution and the performance is better, this lead to reduce the HVFR volume to be pumped per stage compared to the current FR.

Applications/Significance/Novelty
For this study, drag reduction capabilities for this new HVFR were validated in the field at higher pumping rate conditions, potentially optimizing (reducing) the polymer concentration during a freshwater application. It was shown that lower concentrations of this HVFR provided higher viscosity, which helps improve proppant transport and operation placement.
Organic Oil Recovery - Resident Microbial Enhanced Production Pilot in Bahrain
Authors: Venske Christopher , Mohamed Ali , Shaban Ammar , Maan Nelson , Hill Dr. Colin , Carroll Michael , Findlay Roger 
Published: December 2021

Abstract
Tatweer Petroleum has been involved in a Pilot study to determine the efficacy of Organic Oil Recovery (OOR), a unique form of microbial enhanced oil recovery as a means of maximising oil recovery from its Rubble reservoir within the Awali field.
OOR harnesses microbial life already present in an oil-bearing reservoir to improve oil recovery through changes in interfacial tensions, which in the case of Rubble will increase the heavy oil's mobility and improve recovery rates and reservoir wettability. These changes could increase recoverable reserves and extend field life through improved oil recovery with negligible topsides modifications. The Pilot injection is implemented by injecting a specific nutrient blend directly at the wellhead with ordinary pumping equipment. The well is then shut-in for an incubation period and thereafter returned to production.
In Tatweer Petroleum's Awali field the Rubble reservoir is one of the shallowest oil reservoirs in the Bahrain and the first oil discovery in the Gulf Cooperation Council (GCC) region. The reservoir can be found at depths of around 1400 – 1900 ft. During initial laboratory testing of the Rubble target wells the reservoir showed a diverse and abundant resident ecology which has been proven capable of undergoing the necessary characteristic changes to facilitate enhanced production from the target wells. The Pilot test on one of these wells, called Well (A) within this paper, took place in July 2020 and due to this process, the ecology of this well showed these same changes in characteristics in the reservoir along with an associated oil response. The full method of implementation of the Pilot test will also be discussed in detail and will include any challenges and/or successes in this area. The initial state ecology reports of Well (A) are demonstrated and compared to that of post-Pilot test ecology. We also present the production figures for the well prior to and post the Pilot implementation. A correlation will be demonstrated between changes in ecology and an increase in production.
A Novel and Sustainable Generation of Advanced Mud Gas Logging System for Managed Pressure Drilling Applications: An Explorative Well Deployment from North Germany
Authors: Marum Daniela , Cartellieri Ansgar , Shahini Edisa , Scanavino Donata 
Published: December 2021

Abstract

Summary
In the high risk Managed Pressure Drilling operations, increased certainty given by Mud Logging is a critical deliverable to guarantee a safe drilling environment even under challenging conditions and, to provide the first indications for reservoir evaluation. This paper describes a novel product application that successfully obtains advanced mud gas data from a Managed Pressure Drilling environment, proven in flow-loop and field applications (in Lower Saxony, Germany), by reducing service footprint as well as power consumption.
Slickline Descaling Technology Delivers Cost Savings Over Coiled Tubing
Authors: Abdallah Tawakol , Al-Fawwaz Abdullah , Eldaw Galal , Abdallah Wael 
Published: December 2021

Abstract
Al-Khafji Joint Operations (KJO), a joint operation representing both Saudi and Kuwaiti energy interests in the divided zone, recently encountered obstructions in their offshore field. Routine pressure and temperature surveys revealed that an increasing number of wells were developing scale. The operation required an efficient mechanical tool to clean out extensive accumulated scale bridging within a vertical production string and restore full wellbore accessibility. The well had been previously shut down from operations for five years. The operator considered using a coiled tubing (CT) unit or workover rig to clear the scale but sought a more cost-effective solution.
The operator chose a slickline wellbore cleanup and debris breaking tool, which is an impact-driven tool designed to break up scale deposits in a cost-effective, efficient manner. It is jarred down mechanically in the well, each jar applying a short-duration torque via the unique, helically split torque sub. The well's accessible tubing inner diameter was reduced from 2.9-in, nominal to 2-in, at the wireline reentry guide depth. To combat this issue, the slickline technology was deployed with subs increasing in outer diameter (OD) from 1.9-in. to 2.5-in. OD tools.
The special features of the wellbore cleanup and debris breaking tool made it better adapted to the well environment and greatly increased the descaling efficiency. Thirty runs enabled the team to clear the scale accumulations down to 3,652 ft (1113 m). The operator confirmed integrity of the tubing at the end of the slickline operation, allowing the slickline team to access the wellbore and run a memory pressure temperature survey to check the well deliverability. The implementation of the wellbore cleanup and debris breaking tool enabled the operator to reduce inventory and overall descaling time.
Microscopic and Fourier transform infrared analyses of the scale determined it was calcite (CaCO3) with some small hydrocarbon impurities from either oil or diesel. The descaling rate and cost savings achieved using the wellbore cleanup and debris breaking tool has since resulted in the operator adopting this technology and looking into the feasibility of starting a campaign for scale removal in more than 20 wells.
The presence of calcite as a scaling agent is potentially due to the carbonate-saturated formation water and the loss of carbon dioxide from this water to the hydrocarbon phase as pressure decreases. Creating a detailed reservoir characterization that defines fracture orientation, relative aperture produced fluid analysis, and rock properties can help minimize the effect of scale at an early stage. Continuous well monitoring can lead to early identification of scale and determine the need for chemical treatment or further mechanical interventions. This case study demonstrates the benefits of using this wellbore cleanup and debris breaking tool as the first method of mechanical descaling.
Workflow to Optimize Cluster Spacing Design of Horizontal Multistage Fractured Well in Unconventional Source Rock
Authors: Mesdour Rabah , Abdelrahman Moemen , Alhayaf Abdulbari 
Published: December 2021

Abstract
Horizontal drilling and multistage hydraulic fracturing applied in unconventional reservoirs over the past decade to create a large fracture surface area to improve the well productivity. The combination of reservoir quality with perforation cluster spacing and fracture staging are keys to successful hydraulic fracturing treatment for horizontal wells. The objective of this work is to build and calibrate a dynamic model by integrating geologic, hydraulic fracture, and reservoir modeling to optimize the number of clusters and other completion parameters for a horizontal well drilled in the source rock reservoir using simulation and analytical models. The methodology adopted in this study covers the integration of geological, petrophysical, and production data analysis to evaluate reservoir and completion qualities and quantify the heterogeneity and the perforation clusters number required within a frac stage. Assuming all perforation clusters are uniformly distributed within a stage. The hydraulic planer fracture attributes assumed and the surface production measurement together with the production profile were used to calibrate the reservoir model.
The properties of the Stimulated Reservoir Volume "SRV" were defined after the final calibration using reservoir model including hydraulic fractures. The calibrated reservoir model was used to carry out sensitivity analyses for cluster spacing optimization and other completion parameters considering the surface and reservoir constraints. An optimum cluster spacing was observed based on the Estimated Ultimate Recovery "EUR" of the subject well by reservoir properties. The final results based on 70% of perforation clusters contribution to production observed from PLT log, and the results of this study were implemented. Afterwards, another study has been undertaken to increasing the stimulation effectiveness and maximizing the number of perforation clusters contributing to productivity as an area for improvement to engineering the completion design. The methodology adopted in this study identifies the most important parameters of completion affecting well productivity for specific unconventional reservoirs. This study will help to engineer completion design, improve cluster efficiency, reduce cost and increase well EUR for the development phase.
Automations in Chemostratigraphy: Toward Robust Chemical Data Analysis and Interpretation
Authors: Michael Nikolaos A. , Scheibe Christian , Craigie Neil W. 
Published: December 2021

Abstract
Elemental chemostratigraphy has become an established stratigraphic correlation technique over the last 15 years. Geochemical data are generated from rock samples (e.g., ditch cuttings, cores or hand specimens) for up to c. 50 elements in the range Na-U in the periodic table using various analytical techniques. The data are commonly displayed and interpreted as ratios, indices and proxy values in profile form against depth. The large number of possible combinations between the determined elements (more than a thousand combinations), makes it a time-consuming effort to identify meaningful variations that resulted in correlative chemostratigraphic boundaries and zones between wells. The large number of combination means that 30-40% of the information is not used for the correlations that maybe crucial to understand the geological processes. Automation and artificial intelligence (AI) are envisaged as likely solutions to this challenge.
Statistical and machine learning techniques are tested as a first step to automate and establish a workflow to define (chemo-) stratigraphic boundaries, and to identify geological formations. The workflow commences with a quality check of the input data and then with principle component analysis (PCA) as a multivariate statistical method. PCA is used to minimize the number of elements/ratios plotted in profile form, whilst simultaneously identifying multidimensional relationships between them. A statistical boundary picking method is then applied define chemostratigraphic zones, for which reliability is determined utilizing quartile analysis, which tests the overlap of chemical signals across these statistical boundaries. Machine learning via discriminant function analysis (DFA) has been developed to predict the placement of correlative boundaries between adjacent sections/wells.
The proposed workflow has been tested on various geological formations and areas in Saudi Arabia. The chemostratigraphic correlations proposed using this workflow broadly correspond to those defined in the standard workflow by experienced chemostratigraphers, while interpretation times and subjectivity are reduced.
While machine learning via DFA is currently further researched, early results of the workflow are very encouraging. A user-friendly software application with workflows and algorithms ultimately leading to automation of the processes is under development.
A Tool for Derivation of Real Time Lithological Information from Drill Bit Sound
Authors: Yang Yunlai , Li Wei , Almalki Fahd A. , Almarhoon Maher I. 
Published: December 2021

Abstract
Real time lithological information at the drill bit is required for some important drilling operations, such as geo-steering and casing shoe positioning. This paper presents a novel tool "Petro-phone" for recording and processing drill bit sounds, which are generated by the drill bit cutting the rock, in order to provide real time lithological information for the rock at the drill bit. A prototype and a preliminary professional version of Petro-phone have been developed and field trialed. Petro-phone is a surface tool with its acoustic sensors attached to the top drive of a drill rig at some strategical locations for maximally picking up drill bit sounds. The drill bit sounds generated at the drill bit transmit along drill string and drive shaft to reach to the acoustic sensors. Since all the parts along the drill bit sound transmission pathway are made of steel, the drill bit sounds transmit efficiently from the source (drill bit) to the sensors. Preliminary results from two field trials show that drill bit sound patterns correlate with lithologies. The results also indicate that a parameter "Apparent Power" of drill bit sounds negatively correlates with gamma log. Due to its true real time nature, Petro-phone potentially has some real time applications, such as geo-steering, casing shoes positioning. Recorded drill bit sound can also potentially be used to derive lithological information, such as lithology type.
Shut-in Stabilization Time Optimization for Better Reservoir Pressure Monitoring Harnessing Neural Network Modeling
Authors: Al Kadem Mohammad , Al Ssafwany Ali , Abdulghani Ahmed , Al Nasir Hussain 
Published: December 2021

Abstract
Stabilization time is an essential key for pressure measurement accuracy. Obtaining representative pressure points in build-up tests for pressure-sensitive reservoirs is driven by optimizing stabilization time. An artificial intelligence technique was used in the study for testing pressure-sensitive reservoirs using measuring gauges.
The stabilization time function of reservoir characteristics is generally calculated using the diffusivity equation where rock and fluid properties are honored. The artificial neural network (ANN) technique will be used to predict the stabilization time and optimize it using readily available and known inputs or parameters. The values obtained from the formula known as the diffusion formula and the ANN technique are then compared against the actual values measured from pressure gauges in the reservoirs.
The optimization of the number of datasets required to be fed to the network to allow for coverage over the whole range is essential as opposed to the clustering of the datasets. A total of about 3000 pressure derivative samples from the wells were used in the testing, training, and validation of the ANN. The datasets are optimized by dividing them into three fractional parts, and the number optimized through monitoring the ANN performance. The optimization of the stabilization time is essential and leads to the improvement of the ANN learning process. The sensitivity analysis proves that the use of the formula and ANN technique, compared to actual datasets, is better since, in the formula and ANN technique, the time was optimized with an average absolute relative error of 3.67%. The results are near the same, especially when the ANN technique undergoes testing using known and easily available parameters. Time optimization is essential since discreet points or datasets in the ANN technique and formula would not work, allowing ANN to work in situations of optimization.
The study was expected to provide additional data and information, considering that stabilization time is essential in obtaining the pressure map representation. ANN is a superior technique and, through its superiority, allows for proper optimization of time as a parameter. Thus it can predict reservoir log data almost accurately. The method used in the study shows the importance of optimizing pressure stabilization time through reduction. The study results can, therefore, be applied in reservoir testing to achieve optimal results.