bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancement of Indistinct Sequence Stratigraphic Correlations Using Geochemical Signatures: An Example from the Paleozoic Successions Saudi Arabia
Authors: Hussain Maaruf , Amao Abduljamiu , Al-Ramadan Khalid , Babalola Lamidi , Humphrey John 
Published: September 2021

Abstract
The Paleozoic sequence in Kingdom of Saudi Arabia (KSA) represents a thick succession of carbonate, shale, and siltstone/sandstone lithologies that were deposited in a range of environments. This succession hosts abundant unconventional reservoirs with huge amount of natural gas reserves. However, similarity in lithologies and poor biostratigraphic constrains are making facies analysis, stratigraphic correlation, and geosteering applications problematic. In this study, we documented the use of an automated statistical protocol for the identification of chemofacies and correlatable chemozones within highly homogenous formations.
Securing Zonal Isolation Across a Highly Depleted GoM Deep Water Reservoir
Authors: Esquitin Yosafat , Chima Chima , Vasques Ricardo , Hazel Paul , Schwanitz Brian 
Published: September 2021

Abstract
Effective zonal isolation within a layered reservoir in the Gulf of Mexico is a necessity to meet regulations for stacked reservoirs and to maximize total recoverables. Effective zonal isolation also ensures maximum production is achieved via a high-pressure proppant fracture treatment. A primary cement operation of a 10 1/8" production liner (within a 12 ¼" drilled hole section) was challenging due to a combination of high equivalent circulation density (ECD) and potential losses across a layered GOM reservoir. One layer had potential and significant depletion up to 8,000 psi. Critical well parameter considerations were: maintaining the liner burst of 18,200 psi, maximizing rotation and reciprocation capability of the liner, minimizing the impact on circulation and ECD, and ensuring compatibility with the mud systems.
Following careful job planning, including the analysis of caliper data from logging while drilling (LWD) for the optimum placement, two metal expandable packers (MEPs) were installed on the 10 1/8" liner. The MEPs were positioned to straddle the highly depleted layer (one above and one below) in the 12 ¼" open hole section. The liner was deployed, and the cement operation was executed with minimal ECD impact from the inclusion of the MEPs. Surface pressure was applied to create sufficient differential pressure across the 10 1/8" liner wall to hydraulically expand the MEPs quickly under full surface control.
This paper covers the qualification, planning, and deployment of MEPs to provide cement assurance (CA) for zonal isolation and improve the effectiveness of contingency cement squeezes. Though this technology has been used in other regions for several years, this was the first deployment in the Gulf of Mexico (GOM). The solution improved the probability of success of the primary cement job, negating the requirement for a remedial cement squeeze. The decision to run MEPs was based on the estimated cost savings of 3.5 million USD for remedial squeeze operations, a value proposition that did not account for the net present value (NPV) gain, due to improved fracture placement, compared to the case of poor cement isolation.
Relationship between Residual Saturations and Wettability using Pore-Network Modeling
Authors: Purswani Prakash , Johns Russell T. , Karpyn Zuleima T. 
Published: September 2021

Abstract
The relationship between residual saturation and wettability is critical for modeling enhanced oil recovery (EOR) processes. The wetting state of a core is often quantified through Amott indices, which are estimated from the ratio of the saturation fraction that flows spontaneously to the total saturation change that occurs due to spontaneous flow and forced injection. Coreflooding experiments have shown that residual oil saturation trends against wettability indices typically show a minimum around mixed-wet conditions. Amott indices, however, provides an average measure of wettability (contact angle), which are intrinsically dependent on a variety of factors such as the initial oil saturation, aging conditions, etc. Thus, the use of Amott indices could potentially cloud the observed trends of residual saturation with wettability.
Using pore network modeling (PNM), we show that residual oil saturation varies monotonically with the contact angle, which is a direct measure of wettability. That is, for fixed initial oil saturation, the residual oil saturation decreases monotonically as the reservoir becomes more water-wet (decreasing contact angle). Further, calculation of Amott indices for the PNM data sets show that a plot of the residual oil saturation versus Amott indices also shows this monotonic trend, but only if the initial oil saturation is kept fixed. Thus, for the cases presented here, we show that there is no minimum residual saturation at mixed-wet conditions as wettability changes. This can have important implications for low salinity waterflooding or other EOR processes where wettability is altered.
Machine Learning Predictive Models to Estimate the UCS and Tensile Strength of Rocks in Bakken Field
Authors: Chemmakh Abderraouf 
Published: September 2021

Abstract
Uniaxial Compressive Strength (UCS) and Tensile Strength (TS) are among the essential rock parameters required and determined for rock mechanical studies in Petroleum Engineering. However, the determination of such parameters requires some laboratory experiments, which may be time-consuming and costly at the same time. In order to estimate these parameters efficiently and in a short period, some mathematical tools have been used by different researchers. When regression tools proved to give good results only in the limited range of data used, machine learning methods proved to be very accurate in generating models that can cover a wide range of data. In this study, two machine learning models were used to predict the UCS and TS, Support Vector Regression optimized by Genetic Algorithm (GA-SVR) and Artificial Neural Networks (ANNs).
The results were discussed for both uniaxial compressive strength and tensile strength in terms of coefficient of determination R2, root mean squared error (RMSE) and mean average error (MAE). First, for the case of UCS, values of 0.99 and 0.99, values of 3.41 and 2.9 and values of 2.43 and 1.9 were obtained for R2, RMSE and MAE for the ANN and GA-SVR, respectively. Second, for the TS, the same analogy was followed, a coefficient R2 of 0.99 and 0.99, RMSE values of 0.41 and 0.45 and MAE values of 0.30 and 0.39 were obtained for ANNs and GA-SVR, respectively. The next step was to assess these models on a different dataset consisting of data obtained from Bakken Field in Williston Basin, North Dakota, United States. The models showed excellent results comparing to the correlations they were compared with, outperforming them in terms of R2, RMSE and MAE, giving the following results for ANN and SVR respectively, R2 of 0.93, 0.92, RMSE of 9.54, 11.22 and MAE of 7.28, 9.24. The resultant conclusion of this work is that the use of machine learning algorithms can generate universal models which reduce the time and effort to estimate some complex parameters such as UCS and Tensile Strength.
Minimum Miscibility Pressure of Gas Injection in Unconventional Reservoirs
Authors: Yang Gang 
Published: September 2021

Abstract
Unconvnetional reservoirs are predominantly consisted of nanoscale pores. The strong confinement effect within nanopores imposes significant deviations to the confined fluid phase behavior. Minimum miscibility pressure (MMP) in unconventional reservoirs, as a parameter highly related to the phase behavior of confined fluids, is inevitably affected by the nanoscale confinement. The objective of this work is to investigate the impact of nanoscale confinement on MMP of unconventional reservoir fluids and to recognize a reliable theoretical approach to determine the MMP values in unconventional reservoirs.
A modified Peng-Robinson equation of state (PR EOS) applicable for confined fluid characterization is applied to perform the EOS simulation of the vanishing interfacial tension (VIT) experiments. The MMP of a binary mixture at bulk and 50 nm are obtained via the VIT simulation. Meanwhile, the multiple mixing cell (MMC) algorithm coupled with the modified PR EOS is applied to compute the MMP for the same binary system. Comparison of the calculated results to the experimental values recognize that the MMC approach has higher accuracy in determining the MMP of confined fluid systems. Moreover, this approach is then applied to predict the MMP values of both Bakken and Eagle Ford oil at different pore sizes with various injected gases. Results demonstrate that the nanoscale confinement causes drastic suppression to the MMP of unconventional reservoir fluids and the suppression rate increases with decreasing pore size. The drastic suppression of MMP is highly favorable for the miscible gas injection EOR in unconventional reservoirs.
Utilizing Data-Driven Models to Predict Brittleness in Tuscaloosa Marine Shale: A Machine Learning Approach
Authors: Ahmadov Jamal 
Published: September 2021

Abstract
The Tuscaloosa Marine Shale (TMS) formation is a clay- and liquid-rich emerging shale play across central Louisiana and southwest Mississippi with recoverable resources of 1.5 billion barrels of oil and 4.6 trillion cubic feet of gas. The formation poses numerous challenges due to its high average clay content (50 wt%) and rapidly changing mineralogy, making the selection of fracturing candidates a difficult task. While brittleness plays an important role in screening potential intervals for hydraulic fracturing, typical brittleness estimation methods require the use of geomechanical and mineralogical properties from costly laboratory tests. Machine Learning (ML) can be employed to generate synthetic brittleness logs and therefore, may serve as an inexpensive and fast alternative to the current techniques. In this paper, we propose the use of machine learning to predict the brittleness index of Tuscaloosa Marine Shale from conventional well logs.
We trained ML models on a dataset containing conventional and brittleness index logs from 8 wells. The latter were estimated either from geomechanical logs or log-derived mineralogy. Moreover, to ensure mechanical data reliability, dynamic-to-static conversion ratios were applied to Young's modulus and Poisson's ratio. The predictor features included neutron porosity, density and compressional slowness logs to account for the petrophysical and mineralogical character of TMS.
The brittleness index was predicted using algorithms such as Linear, Ridge and Lasso Regression, K-Nearest Neighbors, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost and Gradient Boosting. Models were shortlisted based on the Root Mean Square Error (RMSE) value and fine-tuned using the Grid Search method with a specific set of hyperparameters for each model. Overall, Gradient Boosting and Random Forest outperformed other algorithms and showed an average error reduction of 5 %, a normalized RMSE of 0.06 and a R-squared value of 0.89. The Gradient Boosting was chosen to evaluate the test set and successfully predicted the brittleness index with a normalized RMSE of 0.07 and R-squared value of 0.83.
This paper presents the practical use of machine learning to evaluate brittleness in a cost and time effective manner and can further provide valuable insights into the optimization of completion in TMS. The proposed ML model can be used as a tool for initial screening of fracturing candidates and selection of fracturing intervals in other clay-rich and heterogeneous shale formations.
A Unified Parameter to Represent Reservoir Heterogeneity
Authors: Ramabhadra Agastya Nandana 
Published: September 2021

Abstract
We aim to find a universal method and/or parameter to quantify impact of overall heterogeneity on waterflood performance. For this purpose, we combined the Lorenz coefficient, horizontal permeability to vertical permeability ratio, and thief zone permeability to average permeability ratio, with a radar chart. The area of the radar chart serves as a single parameter to rank reservoirs according to heterogeneity, and correlates to waterflood performance.
The parameters investigated are vertical and horizontal permeability. Average porosity, initial water saturation, and initial diagonal pressure ratio are kept constant. Computer based experiments are used over the course of this entire research. We conducted permeability studies that demonstrate the effects of thief zones and crossflow. After normalizing these parameters into a number between 0 and 1, we then plot them on a radar chart.
A reservoir's overall degree of heterogeneity can be inferred using the radar chart area procedure discussed in this study. In general, our simulations illustrate that the larger the radar chart area, the more heterogenous the reservoir is, which in turn yields higher water cut trends and lower recovery factors. Computer simulations done during this study also show that the higher the Lorenz coefficient, the higher the probability of a thief zone to exist. Simulations done to study crossflow also show certain trends with respect to under tonguing and radar chart area.
Unlocking Field Potential of Mature Fields Using Hybrid Fuzzy Modelling and Kriging Method
Authors: Surana Saransh 
Published: September 2021

Abstract
Reservoir uncertainties, high water cut, completion integrity along with declining production are the major challenges of a mature field. These integrated with dying facilities and poor field production are key issues that each oil and gas company is facing these days. Arresting production decline is an inevitable objective, but with the existing techniques/steps involved, it becomes a cumbersome and exorbitant affair for the operators to meet their requirements. In addition, incompetent and flawed well data makes it more challenging to analyze mature fields. Although flow rate data is the most easily accessible data for mature fields, the absence of pressure data (flowing bottom-hole or wellhead pressure) remains a big obstacle for the application of conventional production enhancement and well screening strategies for most of the mature fields.
A real-time optimization tool is thus constructed by developing a hybrid modelling technique that encapsulates Kriging and Fuzzy Logic to account for the imprecisions and uncertainties involved while identification of subsurface locations for production optimization of a mature field using only production data. The data from the existing wells in the field is used to generate a membership function based on its historical performance and productivity, thereby generating a spatial map of prospective areas, where secondary development operations can be taken up for production optimization.
Inflammatory Bowel Disease in Pregnancy: Case Series with Review of Literature
Authors: Lenin Audrin , Ninan George Abraham , Vijayaselvi Reeta , Rathore Swati , Jasmine Sudha 
Published: September 2021

Abstract

Background:Inflammatory bowel disease(IBD) affects women in their reproductive years with severe disease associated with maternal morbidity and adverse fetal outcomesMethodology and Results:We report our experience of managing pregnant women with IBD in South India. Of the five patients with IBD and 8 pregnancies, 2 presented with active severe disease during pregnancy and were treated with steroids and azathioprine. One developed chorioamnionitis and the other metabolic complications. All except 1 pregnancy required operative delivery by LSCS or instrumental delivery. The mean GA at delivery was 38+5 weeks with a mean birth weight of 2.93kg.Conclusion:Pregnancy outcomes in IBD are dependent on the disease activity status at conception. Diagnosis of IBD in pregnancy is a challenge as full length colonoscopy and CT imaging is not feasible. Chorioamnionitis is a complication associated with IBD, probably due to immunosuppressive drugs. There were no neonatal complications.
Analytical Study for Chlorpyrifos Breakdown Derivatives Qualification After Silver Vanadate (Agvo3) Nanoparticles Fragmentation Reaction
Authors: Ghoniem Islam R. , Elmarsafy Ashraf M. , Amer Mohamed A. , Gammal Hassan A. El- , Aly Mohamed M. Abo , Sayed Mostafa A. 
Published: September 2021

Abstract
The use of pesticides in agriculture has become one of the basic necessities to meet the world's food requirements as a result of the continuous population increase. But sometimes the use of some of these pesticides in large quantities and in high concentrations poses a great danger to the environment and then to humans. Through weather factors, pesticides are transferred to the water and then harm aquatic organisms, which leads to a direct impact and harm to the fish wealth. Monitoring these compounds in the environment requires a large cost of time and money to ensure food safety. Therefore, a new method was used in this study in an attempt to get rid of these compounds in water. The silver vanadate nanoparticles (AgVO3) were synthesized using a novel method in an attempt to eliminate different groups of pesticides that had been detected in Egyptian agricultural crops and water. The effect of 0.1 g of AgVO3 nanoparticles were found on three compounds: chlorpyrifos, dimethoate and malathion. Where a concentration of these compounds, which is 0.5 mg / kg, was used and it was estimated using Exion HPLC coupled with Sciex Qtrap API 6500 + LC-MS/MS System. After 15 minutes of treatment with 0.1 g of AgVO3 nanoparticles, these compounds were undetectable on the device's chromatogram, which is regarded a short period in Affecting these compounds and changing their nature in an attempt to get rid of them. Determination of chlorpyrifos breakdown products by using mass scan of Qtrap API 6500 + LC-MS/MS System and Thermo Scientific Q Exactive Focus Orbitrap LC-MS/MS System was investigated. Supported AgVO3 NPs, according to the study, could be used in long-term environmental remediation because they can be used in aqueous solutions at room temperature without the need for additional stimulus such as UV light.
Application of Response Surface Methodology for Optimization of Biodiesel Production from Microalgae Through Nanocatalytic Transesterification Process.
Authors: Mittal Vaishali , Ghosh Uttam Kumar 
Published: September 2021

Abstract
Production of biodiesel from microalgae is gaining popularity since it does not compromise food security or the global economy. This article reports biodiesel production with Spirulina microalgae through nanocatalytic transesterification process. The nanocatalyst calcium methoxide Ca(OCH3)2 was synthesized using wet impregnation method and utilized to carry out the transesterification process. The nanocatalyst was characterized to evaluate its structural and spectral characteristics using different characterization techniques such as Thermogravimetric analysis (TGA), X-ray diffraction (XRD), Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), and Brunaeur-Emmett-Teller(BET) measurement for surface area. The result demonstrates that calcium methoxide Ca(OCH3)2 possesses a high catalytic activity compared to a heterogeneous catalyst such as calcium oxide (CaO). The impact of several process parameters such as reaction temperature, the molar ratio of methanol to oil, catalyst concentration, and reaction time used in the transesterification process was optimized by employing central composite design(CCD) based response surface methodology(RSM). The polynomial regression equation of second order was obtained for methyl esters. The model projected a 99% fatty acid methyl esters (FAME) yield for optimal process parameters of reaction time 3hrs,3 wt.% of Ca(OCH3)2 catalyst loading, 80°C reaction temperature, and 30:1 methanol to oil molar ratio.
Performance Model for Factory Automation in 5G Networks
Authors: wang jiao , Weitzen Jay , Bayat Oguz , Sevindik Volkan , Li Mingzhe 
Published: September 2021

Abstract

The fifth generation (5G) of mobile networks is emerging as a key enabler of modern factory automation (FA) applications that ensure timely and reliable data exchange between network components. Network slicing (NS), which shares an underlying infrastructure with different applications and ensures application isolation, is the key 5G technology to support the diverse quality of service requirements of modern FA applications. In this article, an end-to-end NS solution is proposed for FA applications in a 5G network. Regression approaches are used to construct a performance model for each slice to map the service level agreement to the network attributes. Interference coordination approaches for switched beam systems are proposed to optimize radio access network performance models. A case study of a non-public network is used to show the proposed NS approach.
Hijacking of transcriptional condensates by endogenous retroviruses
Authors: Asimi Vahid , Hetzel Sara , Kumar Abhishek Sampath , Niskanen Henri , Riemenschneider Christina , Naderi Julian , Fasching Nina , Popitsch Niko , Kretzmer Helene , Smith Zachary , Walther Maria , Meierhofer David , Wittler Lars , Buschow René , Timmermann Bernd , Ameres Stefan , Meissner Alexander , Hnisz Denes 
Published: September 2021

Abstract
Endogenous retroviruses (ERVs) comprise ~10% of mammalian genomes, and ERVs are repressed by multiple cellular mechanisms including heterochromatin, DNA methylation, and methylation of their RNA transcripts 1-13. Although the vast majority of ERVs in mammals are incapable of retrotransposition, ERV de-repression during early development is associated with embryonic lethality 14-16, suggesting that ERV transcription or RNA transcripts may underlie essentiality of ERV repression. Here we report that rapid and selective degradation of the TRIM28 heterochromatin adapter protein in murine embryonic stem cells triggers dissociation of transcriptional condensates from loci encoding super-enhancer -driven pluripotency genes, and association of transcriptional condensates with transcribed ERV loci. Knockdown of ERV RNA or forced expression of super-enhancer -enriched transcription factors rescued condensate localization at super-enhancers in TRIM28-degraded cells. In a biochemical reconstitution system, ERV RNA facilitated phase separation of RNA Polymerase II, and partitioning of NFY and Mediator into heterotypic droplets, suggesting a mechanistic basis for the association of transcriptional condensates with ERVs. Using a zygotic perturbation platform, we found that timing and amount of ERV transcription correlate with the onset of lethality, and that loss of TRIM28 leads to specific depletion of pluripotent lineages in mouse embryos. We propose that retrotransposons contribute to the genomic distribution of nuclear condensates, and that coding and non-coding nascent RNAs may facilitate “hijacking” of transcriptional condensates in various developmental and disease contexts.