bluuebunny/crossref_metadata_2025_split huggingface dataset:
Female Vocational Training
Authors: Al-Abdulwahed Khalid , Al-Ashwan Nouf 
Published: December 2021

Abstract
The development of any country lies in all members of society in a country, the old generation to the younger and new ones. After launching the vision of 2030 pillars, the circle of women barriers becomes wider and unlimited in the field of employment. In order to merge women in the oil and gas industry, the first milestone must be considered is creating opportunities in the labour market alongside educating and training them to acquire great learning and hone skills that qualify the women to be in the industrial workforce. It will widely contribute to the socio-economic change in a country.
The female has individual skills and capabilities that the companies’ needs to achieve its business objectives. The institutes which are fundamentally structured; can open another facility which is targeted the female vocational and technical training based on the same assets (strategies & policies). Another way to do so is through collaboration with international vocational institutions, local female universities and colleges. These days there is no doubt that the oil and gas companies are critically needed for the local talents and diversity of its range. As an example, SPSP has planned to inaugurate a new female vocational & technical center, in the meantime will offer a major source of job opportunities for well trained and qualified young Saudi women that how we encourage and retain more Saudi female to the petroleum energy sector. The training programs will include Health & Safety, and Electrical Diploma. There is a lack of trained and qualified Saudi female technical workforce at the industry sector. To solve this problem, the education and the labour sectors must work simultaneously to empower the female in this field. Many companies need to retool the female candidates from functional roles such as HR or Finance to target them into practice hands-on roles.
To sum up, as Vision 2030 of rewarding opportunities to the women stated, ‘’ we are directing significant investment toward unlocking their talents and supporting their contribution to the Kingdom’s economic growth.’’ Business leaders should call for an action to increase female’s opportunity at the energy sector side by side the government’s efforts in the female vocational training programs.
Talent Acquisition Process Optimization Using Machine Learning in Resumes’ Ranking and Matching to Job Descriptions
Authors: Alghazal Mohammed 
Published: December 2021

Abstract
Employers commonly use time-consuming screening tools or online matching engines that are driven by manual roles and predefined keywords, to search for potential job applicants. Such traditional techniques have not kept pace with the new digital revolution in machine learning and big data analytics. This paper presents advanced artificial intelligent solutions employed for ranking resumes and CV-to-Job Description matching.
Open source resumes and job descriptions' documents were used to construct and validate the machine learning models in this paper. Documents were converted to images and processed via Google cloud using Optical Character Recognition algorithm (OCR) to extract text information from all resumes and job descriptions' documents, with more than 97% accuracy. Prior to modeling, the extracted text were processed via a series of Natural Language Processing (NLP) techniques by splitting/tokenizing common words, grouping together inflected form of words, i.e. lemmatization, and removal of stop words and punctuation marks.
After text processing, resumes were trained using the unsupervised machine learning algorithm, Latent Dirichlet Allocation (LDA), for topic modeling and categorization. Given the type of resumes used, the algorithm was able to categorize them into 4 main job sectors: marketing and business, engineering, computer science/IT and health. Scores were assigned to each resume to represent the maximum LDA probability for ranking. Another more advanced deep learning algorithm, called Doc2Vec, was also used to train and match potential resumes to relevant job descriptions. In this model, resumes are represented by unique vectors that can be used to group similar documents, match and retrieve resumes related to a given job description document provided by HR. The similarity is measured between each resume and the given job description file to query the top job candidates. The model was tested against several job description files related to engineering, IT and human resources, and was able to identify the top-ranking resumes from over hundreds of trained resumes.
This paper presents an innovative method for processing, categorizing and ranking resumes using advanced computational models empowered by the latest fourth industrial resolution technologies. This solution is beneficial to both job seekers and employers, providing efficient and unbiased data-driven method for finding top applicants for a given job.
Study of the Effect of Wetting on Viscous Fingering Before and After Breakthrough by Lattice Boltzmann Simulations
Authors: Mora Peter , Morra Gabriele , Yuen Dave , Juanes Ruben 
Published: December 2021

Abstract
We present a suite of numerical simulations of two-phase flow through a 2D model of a porous medium using the Rothman-Keller Lattice Boltzmann Method to study the effect of viscous fingering on the recovery factor as a function of viscosity ratio and wetting angle. This suite involves simulations spanning wetting angles from non-wetting to perfectly wetting and viscosity ratios spanning from 0.01 through 100. Each simulation is initialized with a porous model that is fully saturated with a "blue" fluid, and a "red" fluid is then injected from the left. The simulation parameters are set such that the capillary number is 10, well above the threshold for viscous fingering, and with a Reynolds number of 0.2 which is well below the transition to turbulence and small enough such that inertial effects are negligible. Each simulation involves the "red" fluid being injected from the left at a constant rate such in accord with the specified capillary number and Reynolds number until the red fluid breaks through the right side of the model. As expected, the dominant effect is the viscosity ratio, with narrow tendrils (viscous fingering) occurring for small viscosity ratios with M ≪ 1, and an almost linear front occurring for viscosity ratios above unity. The wetting angle is found to have a more subtle and complicated role. For low wetting angles (highly wetting injected fluids), the finger morphology is more rounded whereas for high wetting angles, the fingers become narrow. The effect of wettability on saturation (recovery factor) is more complex than the expected increase in recovery factor as the wetting angle is decreased, with specific wetting angles at certain viscosity ratios that optimize yield. This complex phase space landscape with hills, valleys and ridges suggests the dynamics of flow has a complex relationship with the geometry of the medium and hydrodynamical parameters, and hence recovery factors. This kind of behavior potentially has immense significance to Enhanced Oil Recovery (EOR). For the case of low viscosity ratio, the flow after breakthrough is localized mainly through narrow fingers but these evolve and broaden and the saturation continues to increase albeit at a reduced rate. For this reason, the recovery factor continues to increase after breakthrough and approaches over 90% after 10 times the breakthrough time.
A 3D Simulation Technology for the Dynamic Process of Pipe String Tripping in Borehole
Authors: Lian Zhilong , Zhang Jingtian , Li Mu 
Published: December 2021

Abstract
A 3D simulation technology for the process of pipe string tripping in wellbore is presented that uses ABAQUS's edge-to-edge contact technology as its basis for calculations. Results are compared with Landmark software tripping in analysis model. The new approach is based on a 3D finite element method of pipe string in an elastic borehole. It considers bending stiffness, torsional stiffness, pipe internal pressure and external pressure, buoyancy, contact forces and friction with localization of contact points. A numerical method description is provided that has proven to have high stability. Complete finite element model is provided and the method is described in detail to permit readers to replicate all results. The simulation model is compared to Landmark software tripping in analysis result. A ultra-deep horizontal well with measure depth more than 6000m is presented. Von Mises stress and triaxial safe factor for the new dynamics model are compared to Landmark software model. The most significant value of this model is that local contact open, contact forces and effective axial force can be obtained dynamically when pipe string stripping in, and it can be used to predict where and how pipe string will be stucked. The novelty of the new dynamics model is in the ability to solve tripping in operations of the entire pipe string in reasonable time using standard engineering computers.
Breaking ERD Records with Optimized Engineering and Practices: Making the Impossible Possible
Authors: Alzaki Hussien , Rahmani Nadhir , Carr Matthew 
Published: December 2021

Abstract
Long-extended reach drilling (ERD) well has become necessary to reach untapped resources. This paper will describe pre-planning, execution and post results of drilling ERD wells with large bore design of 12¼" as the main step out section and deploying 9⅝" casing on shallow TVD of 4,200’. Progressive increase of the ERD ratio and complexity from one well to the next was planned and executed till we reached the longest well deploying 8 KM of 9⅝" casing with 5.4 ERD ratio at 26,179' TD horizontally all the way.
A learning curve was established on drilled wells while progressively increasing reach and complexity. Subject well was the longest of any well planned in the field by far. Success involved implementation of technically modeled engineered solutions and verified during execution. Operational procedures including but not limited to: proper planning and execution of well profile to ensure optimum placement in a specific formation and minimum side forces. Drilling and tripping procedures to ensure the lowest friction factor (FF) and allow drilling to target depth (TD) with optimum rig capability. Engineered solution for casing running technologies, which involved rotation and conventional running and floatation.
The longest ERD well was drilled to 26,179' TD with field ROP record in 12¼" hole section, maintaining very good hole quality proved by smooth bit trips out of hole and the final trip at TD on elevators. Hole cleaning and fluids strategy was developed and executed efficiently to measure FFs as low as possible for successful 9⅝" deployment. Engineered solution was proposed for 9⅝" deployment and was successfully trial tested on a shorter well to validate simulations. Casing rotation FFs came close to the modeled FFs. The 9⅝" Casing was deployed to bottom as planned and the cement job was performed successfully. Various records were achieved: the subject well achieved the deepest 9⅝" horizontal casing, the deepest 12¼" horizontal at TVD shallower than 5,000'. The longest 12¼" horizontal open hole at TVD shallower than 5,000' with section footage of 16,164'. The 9⅝" casing was deployed as a long string, eliminating the cost and challenges of a liner hanger and the need for a future tieback and also keeping hole sizes available for main and contingency sections to drill the reservoirs ahead.
In addition to existing developed procedures and practices for ERD wells, subject well was dealing with the challenge of drilling a long 12 ¼" hole with a torque limitation of 30K lbsf.ft on TDS, and 4200 psi on surface equipment, and running the longest casing horizontally at such a shallow TVD, which is being done the first time globally. The success proved that challenging ERD wells can be drilled with optimum investments on rig capabilities.
A Machine Learning Approach to Predict the Pressure Gradient of Different Oil-Water Flow Patterns in a Horizontal Wellbore
Authors: Wahid MD Ferdous , Tafreshi Reza , Khan Zurwa , Retnanto Albertus 
Published: December 2021

Abstract
Fluid pressure gradient in a wellbore plays a significant role to efficiently transport between source and separator facilities. The mixture of two immiscible fluids manifests in various flow patterns such as stratified, dispersed, intermittent, and annular flow, which can significantly influence the fluid’s pressure gradient. However, previous studies have only used limited flow patterns when developing their data-driven model. The aim of this study is to develop a uniform data-driven model using machine-learning (ML) algorithms that can accurately predict the pressure gradient for the oil-water flow with two stratified and seven dispersed flow patterns in a horizontal wellbore. Two different machine-learning algorithms, Artificial Neural Network (ANN) and Random Forest (RF), were employed to predict the pressure gradients. A total of 662 experimental points from nine different flow patterns were extracted from five sources that include twelve variables for different physical properties of oil-water, wellbore’s surface roughness, and input diameter. The variables are entrance length to diameter ratio, oil and water viscosity, density, velocity, and surface tension, between oil and water surface tension, surface roughness, input diameter, and flow pattern. The algorithms’ performance was evaluated using median absolute percentage error (MdAPE) and root mean squared error (RMSE). A repeated train-test split strategy was used where the final MdAPE and RMSE were computed from the average of all repetitions. The MdAPE and RMSE for the prediction of pressure gradients are 13.89% and 0.138 kPa/m using RF and 12.17% and 0.088 kPa/m using ANN, respectively. The ML algorithms’ ability to model the pressure gradient is demonstrated using measured vs. predicted analysis where the experimental data points are mostly located in close proximity of the diagonal line, indicating a suitable generalization of the models. Comparing the performance between RF and ANN shows that the latter algorithm’s prediction accuracy is significantly better (p&lt;0.01).
How Leaders Can Shape the Oil & Gas Industry – Accelerating Innovations Through Business & Environmental Intelligent Systems
Authors: Louis Clifford , Khan Hassan , Ali Yawar 
Published: December 2021

Abstract
One of the harms to climate brought about by anthropogenically instigated environmental change is the overabundance creation of CO2 because of industrialization. Research and development endeavors so far have been focused on the improvement of CCS (Carbon Capture and Sequestration), with the fundamental spotlight on the best way to eliminate CO2 from vent gases and how to cover it perpetually in deep aquifers or depleted oil and gas reservoirs to save the environment from the detrimental effects of CO2. At one side, the alarming situation due to excess emission of CO2 from industries has been bulled out and simultaneously, there is higher potential for CO2 in the depleted oil fields which can aid to the Enhanced Oil Recovery (EOR) through the prolonged CO2 injection in depleted oil fields. It is currently turning out to be certain that CCS technology could advance the utilization of fossil fuels than in any case recently thought.
This paper discusses the integration of Carbon Capture and Sequestration (CCS) technology with the progressive strategy of Enhanced Oil Recovery (EOR). CCS includes various advances that can be utilized to catch CO2 from point sources. Countries that are badly affected by the harmful effects of global warming with depleting oil reserves in the very near future can be the most viable target of the CCS Project. The scope and potential of different techniques of CCS along with the opportunities and challenges and the real case scenarios happening in the world are discussed in detail.
The economics, process cycle and case studies of this futuristic technology intend to give valuable insight to the implementation of this integrated technique to the prevalent depleting oil fields around the globe.
Successful Remediation of Sustained Casing Pressures in Gas Cap Injector Wells with the Use of Flexible and Self Healing System
Authors: Jain Bipin , Tambe Abhijeet , Waugh Dylan , MunozRivera Moises , Campbell Rianne 
Published: December 2021

Abstract
Several injection wells in Prudhoe Bay, Alaska exhibit sustained casing pressure (SCP) between the production tubing and the inner casing. The diagnostics on these wells have shown communication due to issues with casing leaks. Conventional cement systems have historically been used in coiled-tubing-delivered squeeze jobs to repair the leaks. However, even when these squeeze jobs are executed successfully, there is no guarantee in the short or long term that the annular communication is repaired. Many of these injector wells develop SCP in the range of 300-400 psi post-repair. It has been observed that the SCP development can reoccur immediately after annulus communication repair, or months to years after an injector well is put back on injection. Once SCP is developed the well cannot be operated further.
A new generation of cement system was used to overcome the remedial challenge presented in these injector wells.
This document provides the successful application of a specialized adaptive cement system conveyed to the problematic zone with the advantage of using coiled tubing equipment for optimum delivery of the remedial treatment.
Increasing Reservoir Productivity at Yuzhno Vyintoyskoye Field, Western Siberia
Authors: Dulkarnaev Marat , Husein Nadir , Malyavko Evgeny , Liss Vladimir , Bolshakov Viacheslav , Buyanov Anton , Gazizov Ruslan , Medvedev Evgeny , Upadhye Vishwajit 
Published: December 2021

Abstract
The new economic conditions characterised by the instability in the global oil and gas industry push market players to search for profitable and efficient ways of developing oil and gas deposits.
One of the key opportunities is Enhanced Oil Recovery projects in hard-to-recover reservoirs and formations. When planning the entire scope of development operations, well interventions and surveys, it is important to follow a strategy that would help successfully overcome the geological and engineering challenges facing the operators.
In this project, a geological feasibility study of the field development management was conducted with regards to the one formation of the Yuzhno-Vyintoyskoye field based on the data obtained using marker-based production surveillance in horizontal wells and flow simulation.
AI-Based Well Ranking System for Hydraulic Fracturing Events
Authors: Erofeev Andrei , Orlov Denis , Perets Dmitry , Koroteev Dmitry 
Published: December 2021

Abstract
We are presenting a new, highly intelligent AI-based ranking system for selecting the most appropriate candidates for well treatment. The system is trained to predict flow rates after hydraulic fracturing (HF) and rank wells by the expected effect of the event with machine learning techniques.
We demonstrate a significant effort for preprocessing the available field data to create a dataset for training machine learning (ML) models. The dataset included information about geology, transport and storage properties, depths, oil/liquid rates before fracturing for target and neighboring wells. Each ML model has been trained to predict monthly production of oil and liquid right after fracturing and after flow stabilization. Also, confidence intervals of the prediction have been provided.
To study the dynamics of future oil rate decline after HF on a stable regime, we have trained several regression models to make predictions at each future point (6 next months after fracturing).
To estimate the effect due to HF, we defined expected production "without fracturing." Typically, wells behave with a stable decline trend of production that is approximated by Arps function. The function is defined before HF, then extrapolated to the period after the event where it shows expected production without fracturing. One may conclude about the effectiveness of HF by calculating areas difference under the extrapolated curve (cumulative production without HF), and ML predicted cumulative production for future six months. Reservoir engineers could calculate these differences for each well and create a ranking list from the highest effect to the lowest. The developed system does this automatically for the required oilfield or its part. Therefore, one may easily define the list of best candidates for HF.
Gradient Boosting algorithm has been applied to obtain results. Feature selection and tuning of hyperparameters have been provided with the application of cross-validation technique.
To test the developed approach, we have divided the dataset from 8 conventional oil fields at a ratio of four to one. The total dataset included 700+ well interventions. Then we have trained and validated models for flow rate prediction on the major part and tested on the holdout part. For different oil field determination coefficients (R2) and normalized root mean square errors (n-RMSE) for oil rate predictions were around R2=0.8 and n-RMSE=0.35 correspondently.
The proposed technique is a new approach for fast, accurate, and objective selection of the candidates for hydraulic fracturing based on real-time state of a field. Such AI-based system could become very handy assistant for reservoir engineer in addition to hydraulic fracturing and hydrodynamic simulators. The presented solution computationally efficient and does not require detailed information about HF design.
Full-Field History-Matching of Commingling Stacked Reservoirs: A Case Study of an Oman Southern Asset
Authors: Aderemi Samuel , Ali Al Lawati Husain , Khalfan Al Rawahy Mansura , Kolivand Hassan , Kumar Singh Manish , Darous Christophe , Bouchet Francois 
Published: December 2021

Abstract
This paper presents an innovative and practical workflow framework implemented in an Oman southern asset. The asset consists of three isolated accumulations or fields or structures that differ in rock and fluid properties. Each structure has multiple stacked members of Gharif and Alkhlata formations. Oil production started in 1986, with more than 60 commingling wells. The accumulations are not only structurally and stratigraphically complicated but also dynamically complex with numerous input uncertainties.
It was impossible to assist the history matching process using a modern optimization-based technique due to the structural complexities of the reservoirs and magnitudes of the uncertain parameters. A structured history-matching approach, Stratigraphic Method (SM), was adopted and guided by suitable subsurface physics by adjusting multi-uncertain parameters simultaneously within the uncertainty envelope to mimic the model response. An essential step in this method is the preliminary analysis, which involved integrating various geological and engineering data to understand the reservoir behavior and the physics controlling the reservoir dynamics.
The first step in history-matching these models was to adjust the critical water saturation to correct the numerical water production by honoring the capillary-gravity equilibrium and reservoir fluid flow dynamics. The significance of adjusting the critical water saturation before modifying other parameters and the causes of this numerical water production is discussed. Subsequently, the other major uncertain parameters were identified and modified, while a localized adjustment was avoided except in two wells. This local change was guided by a streamlined technique to ensure minimal model modification and retain geological realism. Overall, acceptable model calibration results were achieved. The history-matching framework's novelty is how the numerical water production was controlled above the transition zone and how the reservoir dynamics were understood from the limited data.