bluuebunny/crossref_metadata_2025_split huggingface dataset:
Major Inflammatory Markers and Their Significance in Predicting Severity of COVID-19 Disease Pattern
Authors: Aedh Abdullah , Hajri Ali , Alshahrani Abdulaziz , Adam Mohammed , Dahab Abubaker , Babker Asaad , Mohamed Hatem 
Published: December 2021

The unanticipated outbreak of the COVID-19 pandemic has shocked the world in terms of both lives and livelihood. SARS-CoV-2 virus primarily affects the respiratory system, although other organ systems are also involved. Early diagnosis followed up by a retrospective analysis and tracking of a few markers relevant to the immunological status of the individual may aid in determining the state of the patient's disease prognosis. The aim of the present study was to evaluate immunological parameters such as neutrophil to lymphocyte ratio (NLR), C-reactive protein (CRP), and D-dimer, taking into account the patient's age and oxygen saturation level.	Our retrospective analysis of clinical data revealed that such parameters as CRP, D-dimer, and NLR should be taken into consideration to predict severe COVID-19-related complications. The data obtained indicate that patients over age 60 are especially vulnerable to severe COVID-19.
Thermodynamic Basis of Brine Density on Pressure, Temperature, and Chemical Composition in Ultrahigh Pressure/High Temperature Environments
Authors: Rahman Shaikh M. , Sathuvalli Udaya B. , Suryanarayana P. V. 
Published: December 2021

Summary
Temperature change and the pressure/volume/temperature (PVT) response of wellbore annular fluids are the primary variables that control annular pressure buildup in offshore wells. Though the physics of annular pressure buildup is well understood, there is some ambiguity in the PVT models of brines. While custom tests can be performed to determine the PVT response of brines, they are time-consuming and expensive. In this light, our paper presents a method to determine the density of brines from their chemical composition, as a function of pressure and temperature. It compares theoretical predictions with the results of tests on brines used in our industry and available test data from the oil and gas and other industries.
In 1987, Kemp and Thomas used the principles of chemical thermodynamics to develop equations for the density of brines as a function of pressure and temperature and their electrolytic actions. However, their paper contained two (inadvertent, and probably typographical) errors. One of the errors lay in the set of the Debye-Hückel parameters, and the other was contained in the coefficients of the series expansion for the infinite dilution molal volume. Furthermore, they (inadvertently) did not mention the role of a crucial parameter that accounts for the interaction between the ionic constituents of the salt. As a result, nearly a generation of engineers in our industry has been unable to reproduce their valuable results or apply their technically rigorous results to other brine chemistries.
In this paper, we return to the basic equations of chemical thermodynamics and the principles of stoichiometry and delineate the inadvertent errors that had crept into the Kemp and Thomas equations. We then present the rectified equations and reproduce their example with the corrected results. Further, we compare the predictions from the original Kemp and Thomas work with results from a leading chemical engineering model. Finally, we compare the results of theoretical models with test measurements from the laboratory and characterize the uncertainty inherent in each model. Thereby, we have rendered the Kemp and Thomas (1987) model useful to the well design community.
Time-Lapse Pulsed Neutron Well Logging in Oil Sands for Monitoring Steam Chamber Development
Authors: Kim Yonghwee , Kotov Alexandr , Chace David 
Published: December 2021

Abstract
Steam-assisted gravity drainage (SAGD) technology, although a relatively new oil recovery method, has already proved its value in economic development of heavy-oil sands in Western Canada. The SAGD process requires a lifetime monitoring of steam chamber growth to optimize reservoir development, improve oil recovery, and minimize environmental impact. Operators have widely used pulsed neutron well logs to monitor their life cycles of oil sand reservoirs. Time-lapse pulsed neutron logs acquired in observation wells enable operators to effectively track the growth of the steam chamber and identify the changes of formation fluid saturations. We present high-temperature pulsed neutron logging technology and an algorithm to quantify steam, heavy oil and water saturations in SAGD wells.
One of the major challenges in well logging operation is to withstand the thermal shock from the steam chamber. Reservoir temperature often varies abruptly, by as much as 250 degrees C in a very short interval, so the logging tool must be stable in drastic temperature variations. Well logging conditions such as a steam-filled wellbore, extra completion hardware and bad cement quality are challenging factors as well. Furthermore, formation fluid saturation analysis in Canadian oil sands is typically complex because the formation water salinity is relatively fresh but varies, clay properties are not homogeneous, and SAGD operations create conditions in which three-phase fluids coexist in the formation. These environmental conditions make it difficult to rely only on commonly used thermal neutron capture cross-section measurements (formation sigma).
In this paper, case study examples present the above-mentioned challenges and solutions to identify the multi-component formation fluids. The multi-detector pulsed neutron well logging instrument has been modified with a custom-designed heat flask to handle the extreme temperature variations in the SAGD environment. This heat-flask equipped instrument ensures a stable data acquisition in the presence of rapid and extreme temperature variation and enables a prolonged and time-efficient operation through effective heat management. For saturation analysis, we demonstrate an advanced algorithm to quantify three fluid components using a combination of gamma ray ratio and carbon/oxygen (C/O) measurements.
Gas Well Deliquification in the Presence of High Content of Condensate: From Laboratory to Field Test
Authors: He Xiujuan , Lei Wei , Xu Xin , Xu Jian , Qiu Jun , Dai Wei , Cui Leyu , Ma Junwei , Shen Zhiqin , Li Yingcheng 
Published: December 2021

Abstract
Two foaming formulations, an amphoteric surfactant (noted as Fam) and a blend of anionic-cationic-amphoteric surfactants (noted as Facam) have been prepared and tested at lab and in field in the presence of high content of condensate (60 vol% on average). Foam height with Facam are close to those with Fam. Although Fam has better foam stability without condensate, the half-life of the foam (t1/2) decreases by 50% in presence of condensate. Foam generated by Facam shows better oil resistance performance due to negative spreading coefficient (S). Liquid unloading efficiency with Facam are close to those with Fam at lab. Nevertheless in field application, Facam is more efficient than Fam for the deliquification in the gas well. The depth of gas well is 2126 m. Foaming formulations were injected respectively from casing pipe with injection amount of 1-2kg/day. The pressure difference between casing and tubing pipes (ΔPc-t) decreased from 1.0 MPa to 0.28 MPa, and the decline of gas production was slowed down after the injection of Facam in the gas well. As a contrast, both theΔPc-t and decline rate of gas production were increased with Fam. Foam resistance to condensate is a factor, while emulsion viscosity is inferred to be another crucial factor for the performance of formulations in the deliquification process.
Integration of Post-Fracturing Spectral Noise Log, Temperature Modeling, and Production Log Diagnoses Water Production and Resolves Uncertainties in Openhole Multistage Fracturing
Authors: Asif Adnan Bin , Hansen Jon , Khan AbdulMuqtadir , Sheshtawy Mohamed 
Published: December 2021

Abstract
Hydrocarbon development from tight gas sandstone reservoirs is revolutionizing the current oil and gas market. The most effective development strategy for ultralow- to low-permeability reservoirs involves multistage fracturing. A cemented casing or liner completed with the plug-and-perf method allows nearly full control of fracture initiation depth. In uncemented completions equipped with fracturing sleeves and packers, clearly identifying the fracture initiation points is difficult due to lack of visibility behind the completion and long openhole intervals between packers. Also, the number of fractures initiated in each treatment is uncertain.
A lateral was completed with access to 3,190 ft of openhole section across five fracturing stages in a high-temperature and high-pressure tight-gas interval. All stages were successfully stimulated, fracture cleanup flowback was conducted, and entry ports were milled out. A high-definition spectral noise log (SNL) was then conducted along with numerical temperature modeling. Additional logging was done with a set of conventional multiphase sensors. A multi-array production log suite was also performed. Finally, the bottom four stages were isolated with a high-temperature isolation plug based on the integrated diagnosis.
The SNL helped to analyze the isolation packer integrity behind the liner. The initiation of multiple fractures was observed, with as many as nine fractures seen in a single-stage interval. A correlation was found between the openhole interval length and the number of fractures. A correlation of fracture gradient (FG) and initiation depths was made for the lateral in a strike-slip fault regime. The fractures were initiated at depths with low calculated FG, confirming the conventional understanding and increasing confidence in rock property calculations from openhole log data. SNL and temperature modeling aided quantitative assessment of flowing fractures and stagewise production behind the liner. Multi-array production logging results quantified the flow and flow profile inside the horizontal liner. The production flow assessments from both techniques were in good agreement. The integration of several datasets was conducted in a single run, which provided a comprehensive understanding of well completion and production. High water producing intervals were isolated. Downstream separator setup after the isolation showed a water cut reduction by 95%.
The integration of the post-fracturing logs with the openhole logs and fracturing data is unique. The high-resolution SNL provided valuable insight on fracture initiation points and the integrity of completion packers. Fracturing efficiency, compared to the proppant placed, provides treatment optimization for similar completions in the future.
Optimizing Seawater Based Fracture Fluids Rheology Utilizing Chelating Agents
Authors: Othman Amro , Aljawad Murtada Saleh , Kamal Muhammad Shahzad , Mahmoud Mohamed , Patil Shirish 
Published: December 2021

Abstract
Due to the scarcity and high cost of freshwater, especially in the Gulf region, utilization of seawater as a fracturing fluid gained noticeable interest. However, seawater contains high total dissolved solids (TDS) that may damage the formation and degrade the performance of the fracturing fluids. Numerous additives are required to reduce the damaging effect and improve the viscosity resulting in an expensive and non-eco-friendly fracturing fluid system. Chelating agents, which are environmentally benign, are proposed in this study as the replacement of many additives for seawater fracturing fluids.
This study focuses on optimizing chelating agents to achieve high viscosity employing the standard industry rheometers. Carboxymethyl Hydroxypropyl Guar Gum (CMHPG) polymer, which is effective in hydraulic fracturing, was used in this research with 0.5 and 1.0 wt% in deionized water (DW) as well as seawater (SW). It was first tested as a standalone additive at different conditions to provide a benchmark then combined with different concentrations, and pH level chelating agents. In this study the hydration test was conducted through different conditions.
It was observed that CMHPG, when tested as a standalone additive, provided slightly higher viscosity in SW compared to DW. Also, increasing polymer concentration from 0.5 to 1.0 wt% provided three folds of viscosity. The viscosity did not show time dependence behavior at room temperature for the aforementioned experiments where all hydration tests were run at 511 1/s shear rate. Temperature, however, had a significant impact on both viscosity magnitude and behavior. At 70 °C, the fluid viscosity increased with time where low viscosity was achieved early on but kept increasing with shearing time. Similarly, high pH chelating agents provided time dependant viscosity behavior when mixed with CMHPG. This behavior is important as low viscosity is favorable during pumping but high viscosity when the fluids hit the formation.
The study investigates the possibility of utilizing chelating agents with seawater to replace numerous additives. It acts as a crosslinker at early shearing times, where a gradual increase in viscosity was observed and a breaker in the reservoir harsh conditions. It also captures the divalent ions that are common in seawater, which replaces the need for scale inhibitors. The viscosity increase behavior can be controlled by adjusting the pH level, which could be desirable during operations.
Switched Reluctance Motor for Electric Submersible Pump
Authors: Adams Robert , Xiao Jinjiang , Cross Michael , Deffenbaugh Max 
Published: December 2021

Abstract
Switched reluctance motors may be advantageous when used as the primary motor for an electric submersible pump system. They are less susceptible to jamming failures due to their high starting torque and ability to reverse direction. Driving these motors requires well-timed pulse waveforms and precise control of the motor based on its rotational position. It is demonstrated that the pulses required to drive switched reluctance motors can still be applied over along cable lengths. Additionally, the current at the surface can be used to monitor and control the operation of the motor downhole, even with long cable lengths separating the surface power source and downhole motor.
Applied Transfer Learning for Production Forecasting in Shale Reservoirs
Authors: Odi Uchenna , Ayeni Kola , Alsulaiman Nouf , Reddy Karri , Ball Kathy , Basri Mustafa , Temizel Cenk 
Published: December 2021

Abstract
There are documented cases of machine learning being applied to different segments of the oil and gas industry with different levels of success. These successes have not been readily transferred to production forecasting for unconventional oil and gas reservoirs because of sparsity of production data at the early stage of production. Sparsity of unconventional production data is a challenge, but transfer learning can mitigate this challenge.
Application of machine learning for production forecasting is challenging in areas with insufficient data. Transfer learning makes it possible to carry over the information gathered from well-established areas with rich data to areas with relatively limited data. This study outlines the background theory along with the application of transfer learning in unconventionals to aid in production forecasting. Similarity metrics are utilized in finding candidates for transfer learning by using key drivers for reservoir performance. Key drivers include similar reservoir mechanisms and subsurface structures. After training the model on a related field with rich data, most of the primary parameters learned and stored in a representative machine or deep learning model can be re-used in a transfer learning manner. By employing the already learned basic features, models with sparse data have been enriched by using transfer learning. The approach has been outlined in a stepwise manner with details.
With the help of the insights transferred from related sites with rich data, the uncertainty in production forecasting has decreased, and the accuracy of the predictions increased. As a result, the details of selecting a related site to be used for transfer learning along with the challenges and steps in achieving the forecasts have been outlined in detail.
There are limited studies in oil and gas literature on transfer learning for oil and gas reservoirs. If applied with care, it is a powerful method for increasing the success of models with sparse data. This study uses transfer learning to encapsulate the basics of the substructure of a well-known area and uses this information to empower the model. This study investigates the application to unconventional shale reservoirs, which have limited studies on transfer learning.
Integrating Deep Learning and Physics-Based Models for Improved Production Prediction in Unconventional Reservoirs
Authors: Mohd Razak Syamil , Cornelio Jodel , Jahandideh Atefeh , Jafarpour Behnam , Cho Young , Liu Hui-Hai , Vaidya Ravimadhav 
Published: December 2021

Abstract
The physics of fluid flow and transport processes in hydraulically fractured unconventional reservoirs are not well understood. As a result, the predicted production behavior using conventional simulation often does not agree with the observed field performance data. The discrepancy is caused by potential errors in the simulation model and the physical processes that take place in complex fractured rocks subjected to hydraulic fracturing. Additionally, other field data such as well logs and drilling parameters containing important information about reservoir condition and reservoir characteristics are not conveniently integrated into existing simulation models. In this paper, we discuss the development of a deep learning model to learn the errors in simulation-based performance prediction in unconventional reservoirs. Once trained, the model is expected to forecast the performance response of a well by augmenting physics-based predictions with the learned prediction errors from the deep learning model. To learn the discrepancy between simulated and observed production data, a simulation dataset is generated by using formation, completion, and fluid properties as input to an imperfect physics-based simulation model. The difference between the resulting simulated responses and observed field data, together with collected field data (i.e. well logs, drilling parameters), is then used to train a deep learning model to learn the prediction errors of the imperfect physical model. Deep convolutional autoencoder architectures are used to map the simulated and observed production responses into a low-dimensional manifold, where a regression model is trained to learn the mapping between collected field data and the simulated data in the latent space. The proposed method leverages deep learning models to account for prediction errors originating from potentially missing physical phenomena, simulation inputs, and reservoir description. We illustrate our approach using a case study from the Bakken Play in North Dakota.
Single Trip Deployment of Multistage Completion Liners Through the Use of Interventionless Flotation Collars
Authors: Tait William , Munawar Mohammed 
Published: December 2021

Summary
In difficult wellbores, the traditional method for deploying liners was to run drillpipe. The case studies discussed in this paper detail an alternative method to deploy liners in a single trip on the tieback string so the operator can reduce the overall costs of deployment. Previously, this was not often practical because the tieback string weight could not overcome the wellbore friction in horizontal applications.
In each case, a flotation collar is required to ensure there is enough hookload for the deployment of the liner system. The flotation collars used are an interventionless design using a tempered glass barrier that shatters at a predetermined applied pressure. The glass debris is between 5 and 10 mm in diameter and can be easily circulated through the well without damaging downhole components. This is done commonly on a cemented liner and cemented monobore installations, but more rarely with openhole multistage completions. The authors of this paper have overseen thousands of cemented applications of this technology in Western Canada, the US onshore, Latin America, and the Middle East. For openhole multistage completions, the initial installation typically requires a ball drop activation tool at the bottom of the well to set the hydraulically activated equipment above.
The effects of circulating the glass debris through one specific style of activation tool were investigated. Activation tools typically have a limited flow area and could prematurely close if the glass debris accumulates. Premature closing of the tool would leave drilling fluids in contact with the reservoir, potentially harming production. The testing was successfully completed, and the activation tool showed no signs of loading. This resulted in a full-scale trial in the field, where a 52-stage, openhole multistage fracturing liner was deployed using this technology.
Through close collaboration with the operator, an acceptable procedure was established to safely circulate the glass debris and further limit the risk of prematurely closing the activation tool. This paper discusses the openhole and cemented multistage fracturing completion deployment challenges, laboratory testing, and field qualification trials for the single trip deployed system. It also highlights operational procedures and best practices when deploying the system in this fashion.
Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network
Authors: Du Enda , Liu Yuetian , Cheng Ziyan , Xue Liang , Ma Jing , He Xuan 
Published: December 2021

Summary
Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.