bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rock Property Cross-Plot Analysis and Post-Stack Acoustic Impedance Inversion for Optimal Reservoir Characterization in Aplha Field, Onshore Niger Delta Basin
Authors: Azuoko George-Best , Ekwe Amobi , Emmanuel Amulu , Usman Ayatu , Ndidiamaka Eluwa , Victor Omonona , Kufre Udo 
Published: August 2021

Abstract
In the quest to recover by-passed hydrocarbons, extend the life of mature fields, increase hydrocarbon reserves and satiate the increasing global demand for energy, the need for robust reservoir characterization using acoustic impedance inversion continues to grow. In this study, petrophysical parameters were evaluated for two sand intervals RX2 and RX5. Detailed cross-plot analysis of robust petrophysical properties, (density, water Saturation, Lambda-rho and Mu-rho and Porosity) facilitated fluid and lithology discrimination. Well to seismic correlations and acoustic-Impedance model-based, 3-D seismic inversion was done using Hampson Russell software, while petrophysical attribute slices and event-time structure maps were extracted at two horizons - H1 and H2. Results show that RX2 is 100ft thick in Well A, ranging from 5860ft to 5960ft, and 141ft thick in Well B, ranging from 5794ft to 5935ft. Interval RX5, 71ft thick, ranges from 6447ft to 6518ft in Well A, and 88ft thick in Well B, ranging from 6447ft to 6535ft. These intervals had average densities of 2.20g/cc for RX2 and 2.23g/cc for RX5 in Well A. In well B, density values are 1.95g/cc in RX2 and 2.06g/cc for RX5. Average porosities of 25.5% and 27.5% in RX2 and RX5 respectively for Well A; 29% and 19% in RX2 and RX5 respectively for Well B were observed. Respectively, average water and hydrocarbon saturation values of 0.31Swand 0.69Shfor Well A; 0.51Swand 0.49Shfor Well B, was recorded in both intervals. From the results, the thicknesses of RX2 and RX5 conform to the standard thickness of hydrocarbon reservoirs in the study area. Furthermore, the discrimination of the reservoir contents into fluid and lithology by the cross plots, and the observations in the attribute slices indicate that the selected intervals RX2 and RX5 are viable conventional hydrocarbon reservoirs.
Predicting Oil Production Flow Rate Using Artificial Neural Networks - The Volve Field Case
Authors: George Alexander 
Published: August 2021

Abstract
Accurate prediction of oil production flow rates helps production engineers to detect anomalous values which in turn will provide insights about any flaws in huge oil well systems. To aid this, oil flow rate is commonly estimated using empirical correlations. However, in some cases, significant error is inherent in application of this empirical correlation and will often yield inaccurate results.
This present work aims to develop a machine learning algorithm based on an Artificial Neural Network to predict with (high accuracy) the oil production flow rate, using an open source data obtained from Volve production field in Norway. The Downhole Pressure and Temperature, Average Tubing and Annular Pressure Details, Onstream Hours, and Choke Details are used as the input parameters to the algorithm. The procedure can be considered a valid approach for its high accuracy and due to the wide acceptance of data-driven analytics in the industry today.
To develop the model structure, 70% of the data was used the training dataset, and to further evaluate the performance, 30% of the data was used to derive the mean square error and determination coefficient. An error distribution histogram and the cross-plot between simulation data and verification data were drawn.
These results show high predictability of the model and affirmed that ANN has the ability to handle large dataset and also will give a better prediction of oil flow rate when compared to the empirical correlations method. Therefore, equipping production engineers with the capacity to accurately predict oil flow rates from upstream pressure, choke size, and producing gas to oil ratio of a producing well rather than the use of empirical correlations.
Determination of Hydrocarbon Dew Point Hdp of Natural Gas – Experimental and Theoretical
Authors: Mmata Bella , Okeke Hilary , Onyekonwu Mike , Joshua Jesse 
Published: August 2021

Abstract
Hydrocarbon dew point (HDP) temperature is defined as the temperature at which the first hydrocarbon liquid begins to condense in a natural gas cooled at constant pressure, which is an important qualitative parameter for pipeline operators. Hydrocarbon liquid drops-out along the gas pipeline will decrease the effective cross-sectional area of the pipes causing increased pressure drop, reduced line capacity as well as system shutdowns. It is therefore imperative that the gas pipelines be operated above the hydrocarbon dew point (HDP) temperature to ensure a trouble free line; this of course requires the knowledge of the HDP for the pipeline. In this work, onsite measurement of the HDP of 5 different natural gases from different flow stations and gas processing plants in the Niger-Delta region was done using an automatic optical condensation dew point meter. Subsequently, each of the gases was collected in stainless steel Proserv bottles and taken to the laboratory for compositional analysis using gas chromatographic technique with reference to GPA 2286. In addition, the Peng Robinson (PR) and Soave Redlich Kwong (SRK) Equations of state (EOS) were used to predict the hydrocarbon dew point temperature of the gases at the flow line pressures. The Average Absolute Error (AAE) for EOS PR was 9.33% while that for EOS SRK was 14.68%. Obviously, it showed that PR EOS gave better predictions than SRK EOS. The result of this work also showed that even a 50% variation in molar contributions of the non-hydrocarbon components had negligible effects on the predicted hydrocarbon dew point temperatures for all the gas samples tested.
Design and Construction of an Air Quality Monitoring System to Mitigate Virus Spread
Authors: Ochie Samuel , Ochie Karen 
Published: August 2021

Abstract
Air pollution is one of the most dangerous problems we face in the world today. It causes many illnesses and diseases that affect the immune system of humans and non-human animals and is also a means of propagating the novel COVID-19 or corona virus. Temperature, humidity, and the level of carbon dioxide characterize the air quality in an environment. In addition to abnormal temperatures and humidity causing direct problems to humans like headaches, heatstroke, hypothermia and hyperthermia, death and so on, it could also cause complicated problems like the acceleration of the growth and lifespan of harmful viruses like the corona virus especially in closed spaces like on a drilling rig or a processing facility. Multiple studies show that the influenza virus, coronavirus, and many others spread from host to host faster in areas with low humidity and high temperatures and upon infection, mortality rate is higher in low humidity regions. Inhalation of toxic levels of carbon dioxide has adverse effects ranging from drowsiness to coma and even death. Despite safety measures put in place in offshore facilities, there are still cases of corona virus outbreaks, hence this study aims to combat the facilitated spread of viruses and enhancement of good air quality via the design and construction of a device that measures the temperature, humidity, and carbon dioxide levels, using a DHT11 and MQ135 sensor. The temperature, humidity and carbon dioxide levels of the living area, bedroom, kitchen, and balcony in a facility was captured by the device to determine the quality of air and characterized. The values were then compared with the expected values from a trusted website to determine the accuracy of the device. The device showed a 99.8% accuracy and passed quality check making it a recommendation to enhance air quality in facilities, houses, or offices.
Managing the Impact of in Situ Fines Migration in Production Optimization – A Case History from a Niger Delta Field
Authors: Oruambo Tamunomiete , Arochukwu Elias , Okoro Felix , Dennar Linda , Otubu Olalekan 
Published: August 2021

Abstract
In the oil and gas business, a key strategy of well management is the deployment of the right tools and knowledge to enable continuous and optimized production. One of such tools is Matrix acidizing - A stimulation activity designed to remove wellbore damage and improve well inflow. The ability to sustain optimal production from most wells after acidization is often hampered with further fines migration problems and this requires specialized treatment to mitigate.
WELL-001 quit production and was re-entered for a workover in 2018, to recomplete shallower on the same reservoir sand and restore production, however, post workover and subsequent clean up, the well failed to sustain flow. Two additional stimulation operations were also unsuccessful despite gas lift assistance. An Integrated review was held which identified key damage mechanisms impeding flow; deep fines migration which are not well handled by conventional stimulation recipes, emulsion and impairment from Loss Circulation Material (CaCO3 + XCD Polymer).
A Novel solution was identified which included an Ultra-Thin Tackifying Agent (UTTA) as part of the stimulation cocktail with the primary purpose of stabilizing the fines at source and preventing further migration with the flowing fluids. The treatment was deployed successfully and the well lifted immediately, achieving a rate of 800 bopd vs a planned potential of 650 bopd. The impact of this success is not only evident in production but also in resource volume estimation and booking.
Investigating the Use of Machine Learning Models for the Prediction of Pressure Gradient and Flow Regimes in Multiphase Flow in Horizontal Pipes
Authors: Isemin Isemin , Nkundu King-Akanimo 
Published: August 2021

Abstract
During multiphase flow, there is a variation of the physical distribution of the phases within the conduit leading to different flow regimes and consequently variation in the pressure gradient along with the flow regime, hence flow parameter is of vital importance in the prediction of flow regime and pressure gradient in multiphase flow. Analytical solutions and empirical correlations have been developed to predict the flow regime and the pressure gradient respectively. However, in this study, we seek to use supervised machine learning to make predictions taking parameters such as relative phase volume, bulk fluid flow rates, individual phase flow rates, conduit diameters, inclination, phase densities and temperature as input to the model. The data representing these parameters can be regularly updated to reflect the flow conditions in the well. The flow is composed of water, oil and air at different temperatures. The machine learning models used are Logistic Regression, Decision Trees and Principal Component Analysis. The first two is supervised and are tuned for accuracy dependent on pressure gauge readings while the third seeks to determine the parameters of greatest influence on the predicted output, the flow regime and pressure gradient.
The model is constrained to learning and making predictions for fluid production through the tubing only. The trained model shows promise for application in the industry as it allows for automation of systems used to control flow and affords a more comprehensive approach to mitigating flow problems in pipeline systems and flow systems in oilfields.
Comparative Evaluation of Artificial Intelligence Models for Drilling Rate of Penetration Prediction
Authors: Adetifa Ololade , Iyalla Ibiye , Amadi Kingsley 
Published: August 2021

Abstract
Rate of penetration is an important parameter in drilling performance analysis. The accurate prediction of rate of penetration during well planning leads to a reduction in capital and operating costs which is vital given the recent downturn in oil prices. The industry is seen to embrace the use of novel technologies and artificial intelligence in its bid to be sustainable which is why this study focuses on the use of artificial intelligent models in predicting the rate of penetration. The predictive performance of three data-driven models [artificial neural network (ANN), extreme learning machine (ELM) and least-square support vector machine (LS-SVM)] were evaluated using actual drilling data based on three performance evaluation criteria [mean square error (MSE), coefficient of determination (R2) and average absolute percentage error (AAPE)]. The models were validated using the physics based Bourgoyne and Young's model. The results show that all three models performed to an acceptable level of accuracy based on the range of the actual drilling data because, although the ELM had the least MSE (1317.44) and the highest R2 (0.52 i.e. 52% prediction capability) the LS-SVM model had a smaller spread of predicted ROP when compared with the actual ROP and the ANN had the least AAPE (38.14). The results can be improved upon by optimizing the controllable predictors. Validation of the model's performance with the Bourgoyne and Young's model resulted in R2 of 0.29 or 29% prediction capability confirming that artificial intelligent models outperformed the physics-based model.
Understanding the Factors Influencing Pedestrian Walking Speed over Elevated Facilities using Tree-Based Ensembles and Shapley Additive Explanations
Authors: Banerjee Arunabha , Raoniar Rahul , Maurya Akhilesh Kumar 
Published: August 2021

Abstract
Accurate estimation of factors affecting pedestrian walking speed is of paramount importance for efficient operation and management of at-grade and grade-separated infrastructures (such as foot over bridges or skywalks). Understanding such factors helps in planning for better circulation of pedestrians within confined elevated passageways as well as evacuation preparedness during emergencies. The walking speed on elevated infrastructure generally depends on the microscopic factors (demographics characteristics), macroscopic factors (average flow and density), and geometric factors (obstruction, land use type, length, connectivity, and effective width). The wide variability of these factors and their impact on walking speed makes the speed prediction modeling complex. Therefore, accuracy of such models depends on accurate field data collection, identification of pertinent variables, and implementation of appropriate modeling approaches. With the increase in computational capabilities, tree-based ensembles have gained immense popularity due to their high prediction accuracy in comparison to traditional regression models. The tree-based ensembles provide better interpretable results without a huge data requirement and are able to capture the complex non-linear relationships. These properties make tree-based ensemble models better candidates for modeling pedestrian walking speed, however, exploration on the tree-based ensemble in pedestrian related research is limited. In the current study, an attempt is made to model and compare seven tree-based models (including ensembles) to suggest the best modeling approach to identify the dominating factors and accurate prediction of pedestrian walking speeds over elevated walkways. The result of the present study showed that Gradient Boosted Trees (MAE 9.27) and Light Gradient Boosted Trees (MAE: 9.96) were best in predicting walking speed over the skywalk and foot over bridge facilities, as these boosting based methods improved the weak trees (on the basis of accuracy) sequentially. The variable importance of final models was estimated using SHapley Additive exPlanations (SHAP) which revealed that walking speed was dependent on the average flow, average density, and length of the facility. Moreover, other features such as gender, age, height, and width of the facility also play a significant role in determining the pedestrian walking speeds. The identification of important variables not only provides better insight on factors that affect walking speed over elevated facilities but also provides a valuable source of information to researchers, planners, and policymakers for better designing, operation, and management of the elevated pedestrian infrastructures.
CDK10 Regulates Gastric Cancer Metastasis By Inhibiting lncRNA-C5ORF42-5 Via Phosphorylation Level of AKT/ERK
Authors: Deng Zi-Jian , Chen Dong-Wen , Chen Xi-Jie , Fang Jia-Ming , Xv Liang , Peng Jun-Sheng , Chen Shi 
Published: August 2021

Abstract
Background: Gastric cancer is the fourth most common malignant disease. Both CDK10 and long noncoding RNAs (lncRNAs) have been found to exert biological functions in multiple cancers. However, it is still unclear whether CDK10 represses tumor progression in gastric cancer by reducing potential targeting lncRNAs. Methods: The functions of CDK10 and lncRNA-C5ORF42-5 in proliferation, invasion and migration were assessed by MTS assays, colony formation assays, cell cycle and apoptosis assays, Transwell assays, wound healing assays and animal experiments. We used high-throughput sequencing to confirm the existence of lncRNA-C5ORF42-5 and quantitative real-time PCR was used to evaluate lncRNA expression. Then, with RNA-seq sequencing as well as GO function and KEGG enrichment analysis, we identified the signaling pathways in which lncRNA-C5ORF42-5 was involved in gastric cancer. Finally, western blotting was used to identify the genes regulated by lncRNA-C5ORF42-5. Results: Our results showed that CDK10 is expressed at relatively low levels in gastric cancer cell lines and inhibits the progression of gastric cancer cells both in vitro and in vivo. Next, based on high-throughput sequencing, we identified a novel lncRNA, lncRNA-C5ORF42-5, in the stable CDK10-overexpressing cell line compared with the CDK-knockdown cell line and their controls. Additionally, we confirmed that lncRNA-C5ORF42-5 acts as an oncogene to promote metastasis in gastric cancer in vitro and in vivo. We then ascertained that lncRNA-C5ORF42-5 is a major contributor to the function of CDK10 in gastric cancer metastasis by upregulating lncRNA-C5ORF42-5 to reverse the effects of CDK10 overexpression. Finally, we explored the mechanism by which lncRNA-C5ORF42-5 overexpression affects gastric cancer cells to elucidate whether lncRNA-C5ORF42-5 may increase the activity of the SMAD pathway of BMP signaling and promote the expression of EMT-related proteins, such as E-cadherin. Additionally, overexpression of lncRNA-C5ORF42-5 affected the phosphorylation levels of AKT and ERK. Conclusion: Our findings suggest that CDK10 overexpression represses gastric cancer tumor progression by reducing lncRNA-C5ORF42-5 and hindering activation of the related proteins in metastatic signaling pathways, which provides new insight into developing effective therapeutic strategies in the treatment of metastatic gastric cancer.
Organic and Inorganic Template Assisted Synthesis of Silica Nanotubes and Evaluation of Its Properties for Drug Targeting
Authors: Mahendran Logesh , Ravichandran Abimanyu , Ballamuraugan A.M. 
Published: August 2021

Abstract
Mesoporous silica network nanotubes were fabricated using both organic and inorganic templates such as citric acid (CA), cetyltrimethylammonium bromide (CTAB), and sodium bicarbonate (SBC). The phase analysis of synthesized silica network was confirmed by X-ray diffractometer (XRD) analysis, and the present functional groups were revealed by Fourier Transformer Infrared Spectroscopy (FTIR) and the formation of tubular morphology was analyzed by Transmission Electron Microscopy (TEM). The mesoporous nature of each template sample was studied using Brunauer–Emmett–Teller (BET) instrument. The surface area and porous size were calculated successfully for fabricated silica network nanotubes.
A Novel Nomogram for Predicting Cancer-Specific Survival in Women with Uterine Sarcoma: A Large Population-Based Study
Authors: Li Yuan-jie , Lyu Jun , Li Chen , He Hai-rong , Wang Jin-feng , Wang Yue-ling , Fang Jing , Ji Jing 
Published: August 2021

Abstract
Background: Uterine Sarcoma (US) is a rare malignant uterine tumor in women with aggressive behavior and rapid progression. The purpose of this study was to perform a comprehensive nomogram to predict the cancer-specific survival (CSS) for US based on the Surveillance, Epidemiology, and End Results (SEER) database.Methods: Retrospetive population-based study was conducted using the data of patients with US between 2010 and 2015 from SEER database. They were randomly divided into a training cohort and a validation cohort in a 7-to-3 ratio. Multivariate Cox analysis was performed to identify independent prognostic factors. Subsequently, nomogram was established to predict the patients’ CSS. The discrimination and calibration of the nomogram were evaluated by concordance index (C-index) and the area under the curve (AUC). Finally, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA) were used to evaluate the benefits of the new prediction model.Results: A total of 3861 patients with US were included in our study. As revealed in multivariate Cox analysis, age at diagnosis, race, marital status, insurance record, tumor size, pathology grade, histological type, SEER stage, AJCC stage, surgery status, radiotherapy status, and chemotherapy status were found to be independent prognositic factors. In our nomogram, pathology grade has the highest risk on CSS in US, followed by age at diagnosis, then surgery status. Comparing to the AJCC staging system, the new nomogram showed better predictive discrimination with higher C-index in both training and validation cohort (0.796 and 0.767 vs0.706 and 0.713, respectively) . Furthermore, AUC value, calibration plotting, NRI, IDI, and DCA also demonstrated better performance than the traditional system.Conclusion: Our study validated the first comprehensive nomogram for US which could provide more accurately and individualized survival predictions for US patients in clinical practice.
Avoiding CFRP Delamination During Abrasive Water Jet Piercing: A New Piercing Method
Authors: Popan Ioan Alexandru , Bâlc Nicolae , Popan Alina Ioana 
Published: August 2021

Abstract
Carbon Fibre Reinforced Polymer (CFRP) is used in top industries like aerospace, automotive or medicine. Abrasive water jet (AWJ) technology has demonstrated its capacity in machining CFRP parts with a high dimensional accuracy due to its low mechanical loading, reduced machining temperature, high productivity, reduced tooling, and environmental friendliness. An important challenge when machining composite materials with AWJ is material delamination, determined by the high-speed water jet hitting the material during the piercing process. It is the ideal tool for cutting complex CFRP parts, in cases where the piercing point is outside of the workpiece. The challenge lies in machining features where material piercing is required, like holes, slots or internal contours. This paper presents a method of piercing the composite materials with abrasive water jet, that can avoid delamination. The method requires adding the abrasive particles in the water jet at the very beginning of jet formation, thus obtaining a mixed abrasive water jet during the first impact with the composite workpiece. A new cutting system was designed and set up based on the proposed piercing method and was compared with a conventional AWJ cutting system. The insertion of the abrasive particles into the water jet was monitored by using acoustic emission (AE). An analysis of the influence of piercing parameters (water pressure, standoff distance, abrasive inlet angle and abrasive delay time) on the delamination was conducted. The process outcomes such as hole surface integrity, delamination, particles embedment, uncut fibers and dimensional characteristics, were evaluated. The results show that the method is promising in reducing delamination.