bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting Heavy Oil Combustion Kinetics with Machine Learning
Authors: Anderson Timothy I. , Li Yunan , Kovscek Anthony R. 
Published: April 2021

Abstract
Heavy oil resources are becoming increasingly important for the global oil supply, and consequently there has been renewed interest in techniques for extracting heavy oil. Among these, in-situ combustion (ISC) has tremendous potential for late-stage heavy oil fields, as well as high viscosity, very deep, or other unconventional reservoirs. A critical step in evaluating the use of ISC in a potential project is developing an accurate chemical reaction model to employ for larger-scale simulations. Such models can be difficult to calibrate, however, that in turn can lead to large errors in upscaled simulations. Data-driven models of ISC kinetics overcome these issues by foregoing the calibration step and predicting kinetics directly from laboratory data.
In this work, we introduce the Non-Arrhenius Machine Learning Approach (NAMLA). NAMLA is a machine learning-based method for predicting O2 consumption in heavy oil combustion directly from ramped temperature oxidation (RTO) experimental data. Our model treats the O2 consumption as a function of only temperature and total O2 conversion and uses a locally-weighted linear regression model to predict the conversion rate at a query point. We apply this method to simulated and experimental data from heavy oil samples and compare its ability to predict O2 consumption curves with a previously proposed interpolation-based method. Results show that the presented method has better performance than previously proposed interpolation models when the available experimental data is very sparse or the query point lies outside the range of RTO experiments in the dataset. When available data is sufficiently dense or the query point is within the range of RTO curves in the training set, then linear interpolation has comparable or better accuracy than the proposed method. The biggest advantage of the proposed method is that it is able to compute confidence intervals for experimentally measured or estimated O2 consumption curves. We believe that future methods will be able to use the efficiency and accuracy of interpolation-based methods with the statistical properties of the proposed machine learning approach to better characterize and predict heavy oil combustion.
An Experimental Study of Steam-Assisted Gravity Drainage Using A Large-Scale Physical Model
Authors: Sheng Kai , Okuno Ryosuke , Imran Muhammad , Yamada Tomomi 
Published: April 2021

Abstract
This paper presents a large-scale experimental study of the compositional effect on produced bitumen properties in SAGD. The SAGD experiment used a sandpack in the cylindrical pressure vessel that was 1.22 m in length and 0.425 m in internal diameter. The pore volume of the sandpack was 58 L, and the porosity and permeability were 0.33 and 5.5 D, respectively. The sandpack was initially saturated with 93% bitumen and 7% deionized water.
The SAGD experiment after preheating was operated mostly at a steam injection rate of 35 cm3/min (cold-water equivalent) at 3600 kPa (244°C). The produced fluids (gas, oil, and water) were analyzed; e.g., ten oil samples were analyzed in terms of carbon number distribution (CND), the asphaltene content, density, and viscosity to investigate the compositional change of the produced bitumen. After the experiment, the sandpack was excavated and samples were taken for analysis of solid, water, oil, asphaltene, and sulfur contents. Experimental data (e.g., propagation of a steam chamber and production of oil and water) were history-matched by using a numerical reservoir simulator.
Results showed that the produced bitumen was lighter and contained 1 to 5 wt% less asphaltenes than the original bitumen. Also, the remaining oil inside the steam chamber contained 6 wt% more asphaltenes. As a result, the produced bitumen was 1 to 6 kg/m3 less dense than the original bitumen. In the actual operations, bitumen is diluted with condensate to reduce the oil viscosity for pipeline shipping. This reduction in bitumen density corresponds to a reduction of the diluent cost by 5-10%. The produced bitumen became less dense with increasing steam-chamber volume. The history-matched simulation indicated that the progressively decreasing density of the produced bitumen can be attributed to the vaporization of the relatively volatile components in the remaining oil, and condensation of those components near the chamber edge. The history-matching also indicated that varying flow regimes (counter-current and co-current flow of water and oil) affected the oil recovery during in the SAGD experiment.
Deep Learning for Downhole Data Prediction: A Cost-Effective Data Telemetry Through Data Analytics
Authors: Thakur Bharat , Samuel Robello 
Published: April 2021

Abstract
Accurate real-time downhole data collection provides a better understanding of downhole dynamics and formation characteristics, which can improve wellbore placement and increase drilling efficiency by improving the rate of penetration (ROP) and reducing downtime caused by tool failure. High-speed telemetry through wired drill string has enabled real-time data acquisition, but there are significant additional costs associated with the technology. Data-driven techniques using recursive neural networks (RNN) have proven very efficient and accurate in time-series forecasting problems. In this study, we propose deep learning as a cost-effective method to predict downhole data using surface data.
Downhole drilling data is a function of surface drilling parameters and downhole conditions. The downhole data acquired using relatively inexpensive methods usually have a considerable lag time depending on the signal travel length. So, the first step in the proposed method is syncing the downhole data and surface data. After the data are synced, they are then fed into an RNN-based long-term short memory (LSTM) network, which learns the relationship between the surface parameters and downhole data. LSTM networks can learn long-term relationships in the data, thus making them ideal for time-series forecasting applications. The trained model is then used to make predictions for downhole data using the given surface data.
The median error for the prediction of downhole data using surface data was as low as 3% in this study. The study suggests that the developed model can accurately predict downhole data in real-time. The model is also very robust to the amount of noise or outliers present in the data and can predict downhole conditions 50–60 ft ahead with reasonable accuracy. It was observed that the prediction accuracy varied from well to well and drilling depths. The results demonstrate how deep learning can be cost-effectively employed for downhole data prediction.
This paper presents a novel method for using surface data to predict downhole data by employing deep learning. The method can be deployed in real-time to aid in wellbore placement and improve drilling performance.
The Essence of Horizontal Drilling Challenges in Depleted Reservoirs
Authors: Elgaddafi Rida Mohamed , Soriano Victor , Ahmed Ramadan , Osisanya Samuel 
Published: April 2021

Abstract
Horizontal well technology is one of the major improvements in reservoir stimulation. Planning and execution are the key elements to drill horizontal wells successfully, especially through depleted formations. As the reservoir has been producing for a long time, pore pressure declines, resulting in weakening hydrocarbon-bearing rocks. Drilling issues such as wellbore stability, loss circulation, differential sticking, formation damage remarkably influenced by the pore pressure decline, increasing the risk of losing part or even all the horizontal interval. This paper presents an extensive review of the potential issues and solutions associated with drilling horizontal wells in depleted reservoirs.
After giving an overview of the depleted reservoir characteristics, the paper systematically addresses the major challenges that influence drilling operations in depleted reservoirs and suggests solutions to avoid uncontrolled risks. Then, the paper evaluates several real infill drilling operations through depleted reservoirs, which were drilled in different oilfields. The economic aspect associated with potential risks for drilling a horizontal well in depleted reservoirs is also discussed. The most updated research and development findings for infill drilling are summarized in the article.
It is recommended to use wellbore strengthening techniques while drilling a horizontal well through highly depleted formations. This will allow using higher mud weight to control unstable shales while drilling through the production zone. Managed Pressure Drilling should be considered as the last option for highly depleted formations because it will require a greater level of investment which is not going to have a superior rate of return due to the lack of high deliverability of the reservoir. Using rotary steerable systems is favored to reduce risks related to drilling through depleted formations. Precise analysis of different drilling programs allows the drilling team to introduce new technology to reduce cost, improve drilling efficiency and maximize profit. It is the responsibility of the drilling engineer to evaluate different scenarios with all the precautions needed during the planning stage to avoid unexpected issues.
The present market conditions and the advancement in technologies for drilling horizontal wells increase the feasibility of producing the depleted reservoirs economically. This paper highlights the challenges in drilling horizontal wells in highly depleted reservoirs and provides means for successfully drilling those wells to reduce risks while drilling
Evaluation of Hydraulic Fracturing Treatment with Microseismic Data Analysis in A Marcellus Shale Horizontal Well
Authors: El Sgher Mohamed , Aminian Kashy , Ameri Samuel 
Published: April 2021

Abstract
The objective of this study was to perform an integrated analysis to gain insight for optimizing fracturing treatment and gas recovery from Marcellus shale. The analysis involved all the available data from a Marcellus Shale horizontal well which included vertical and lateral well logs, hydraulic fracture treatment design, microseismic, production logging, and production data. A commercial fracturing software was utilized to predict the hydraulic fracture properties based on the available vertical and lateral well logs data, diagnostic fracture injection test (DFIT), fracture stimulation treatment data, and microseismic recordings during the fracturing treatment. The predicted hydraulic fracture properties were then used in a reservoir simulation model developed based on the Marcellus Shale properties to predict the production performance.
In this study, the rock mechanical properties were estimated from the well log data. The minimum horizontal stress, instantaneous shut-in pressure (ISIP), process zone stress (PZS), and leak-off mechanism were determined from DFIT analysis. The stress conditions were then adjusted based on the results of microseismic interpretations. Subsequently, the results of the analyses were used in the fracturing software to predict the hydraulic fracture properties. Marcellus Shale properties and the predicted hydraulic fracture properties were used to develop a reservoir simulation model. Porosity, permeability, and the adsorption characteristics were estimated from the core plugs measurements and the well log data. The image logs were utilized to estimate the distribution of natural fractures (fissures). The relation between the formation permeability and the fracture conductivity and the net stress (geomechanical factors) were obtained from the core plugs measurements and published data. The predicted production performance was then compared against production history.
The analysis of core data, image logs, and DFIT confirmed the presence of natural fractures in the reservoir. The formation properties and in-situ stress conditions were found to influence the hydraulic fracturing geometry. The hydraulic fracture properties are also impacted by stress shadowing and the net stress changes. The production logging tool results could not be directly related to the hydraulic fracture properties or natural fracture distribution. The inclusion of the stress shadowing, microseismic interpretations, and geomechanical factors provided a close agreement between the predicted production performance and the actual production performance of the well under study.
Comparing Steam Flood Analytical and Simulation Models ~ Strengths and Limitations
Authors: Hampton Thomas J. , El-Mandouh Mohamed , Weber Stevan , Thaker Tirth , Patel K. , Macaul Barclay , Erdle Jim 
Published: April 2021

Abstract
Mathematical Models are needed to aid in defining, analyzing, and quantifying solutions to design and manage steam floods. This paper discusses two main modeling methods – analytical and numerical simulation. Decisions as to which method to use and when to use them, requires an understanding of assumptions used, strengths, and limitations of each method.
This paper presents advantages and disadvantages through comparison of analytical vs simulation when reservoir characterization becomes progressively more complex (dip, layering, heterogeneity between injector/producer, and reservoir thickness).While there are many analytical models, three analytical models are used for this paper:Marx & Langenheim, Modified Neuman, and Jeff Jones.The simulator used was CMG Stars on single pattern on both 5 Spot and 9 Spot patterns and Case 6 of 9 patterns, 5-Spot.
Results were obtained using 6 different cases of varying reservoir properties based on Marx & Langenheim, Modified Neuman, and Jeff Jones models.Simulation was also done on each of the 6 cases, using Modified Neuman steam rates and then on Jeff Jones Steam rates using 9-Spot and 5-Spot patterns.This was done on predictive basis on inputs provided, without adjusting or history matching on analog or historical performance.Optimization runs using Particle Swarm Optimization was applied on one case in minimizing SOR and maximize NPV.
Conclusion from comparing cases is that simulation is needed for complex geology, heterogeneity, and changes in layering. Also, simulation can be used for maximizing economics using AI based optimization tool. While understanding limitations, the analytical models are good for quick looks such as screening, scoping design, some surveillance, and for conceptual understanding of basic steam flood on uniform geologic properties.
This paper is innovative in comparison of analytical models and simulation modeling.Results that quantify differences of oil rate, SOR, and injection rates (Neuman and Jeff Jones) impact on recovery factors is presented.
Designing and Theoretical study of fluorinated small molecule donor materials for organic solar cells
Authors: Mubashar Usama , Farhat Afifa , Khera Rasheed Ahmad , Saleem Rabia , IQBAL Javed 
Published: April 2021

Abstract
A recently synthesized photoactive donor named fluorinated thienyl substituted benzodithiophene (DRTB-FT), modified with four novel end capped acceptor molecules, have been investigated through different electrical, quantum and spectrochemical techniques for their enhanced electro-optical and photovoltaic properties. DRTB-FT was connected to 2-methylenemalononitrile (D-1), 2-methylene-3-oxobutanenitrile (D-2), 2-(2-methylene-3-oxo-2,3-dihydro-1H-inden-1-ylidene) malonitrile (D-3) and 3-methyl-5methylene-2-thioxothiazolidin-4-one (D-4) as terminal acceptor moieties. The architectural D-1 and D-3 molecules owe reduced optical band gap of 2.45 and 2.28 eV benefited from A-D-A configuration and have broaden maximum absorption band (λmax) at 617 and 602 nm in polar organic solvent (chloroform). Reduced optical band gap set the ease for enhanced absorption. Reorganization energy of electron (λe) of D-3 molecule (0.00397 eV) was smaller amongst all which disclosed its greater mobility of conducting electrons (ICT). Larger values of dipole moment (µ) of D-1(5.939 Debye) and D-3 (3.661 Debye) molecules in comparison to R indicated greater solubilities of the targeted molecules. Among the tailored molecules, D-3 showed lowest binding energy of 0.25 eV in solvent phase and 0.08 eV in gaseous phase. The voltaic strength of designed molecules was examined with respect to fullerene derivative (PC61BM) which exposed that D-1 is the best choice for achieving higher PCE. TDM (transition density matrix), DOS (density of states) analysis and binding energies all were estimated at MPW1PW91/6-31G (d, p) level of DFT (density functional theory).
Sudden Sensorineural Hearing Loss with Auditory Neuropathy: Evidence Based on Audiological Test and Inner Ear MRI
Authors: Ai Yu , Hu Na , Wang Mingming 
Published: April 2021

Abstract
Background: Sudden sensorineural hearing loss (SSNHL), defined as a hearing loss of ≥30 dB affecting at least three consecutive frequencies occurring over a 72-hour period, is commonly in audiologic and otolaryngologic practice. However, SSNHL with auditory neuropathy is rarely reported. We described the clinical characteristics of a patient with SSNHL and auditory neuropathy, together with its etiologic mechanism. Case presentation: A 27-year-old woman was referred to our otological clinic with acute diminished hearing on the left ear, associated with persistent tinnitus, aural fullness and vertigo, all for a duration of 20 days. Audiological examination showed presence of otoacoustic emissions and cochlear microphone with absent ABR on the left ear, which was consistent with the criteria of auditory neuropathy. Magnetic resonance images of the brain and inner ear implicated demyelinating lesions in the auditory nerve. Forty months after onset, all the auditory retests were normal and symmetric T2-FLAIR signals of both auditory nerves were found in inner ear magnetic resonance images.Conclusion: We showed that the SSNHL with auditory neuropathy could be caused by demyelination. The case also suggests that some SSNHL caused by demyelination is reversible.
Glycaemic control and complications in people with diabetes: sex and socioeconomic status differences in the city of Madrid
Authors: Ares-Blanco Sara , Polentinos-Castro Elena , Rodríguez-Cabrera Francisco , Gullón Pedro , Perruca Luis Sánchez , Tejero Manuel Franco , González Isabel del Cura 
Published: April 2021

Abstract
Background: Differences in complications and multimorbidity for people with type 2 diabetes mellitus (T2D) have been found according to patient sex and socioeconomic status (SES). We analysed the association between sex and SES with glycaemic and cardiovascular control in diabetic primary care patients from the whole city of Madrid.Methods: This was an observational, descriptive, retrospective, multicenter study with an analytical approach within the Heart Healthy Hood Project (HHH). A total of 68,535 T2D patients between 40-75 years who were living in the municipality of Madrid were included. Age, sex, area-level SES (in quintiles from least to most wealth), tobacco use, comorbidities, complications, and health center characteristics were collected from primary care electronic health records during 2017-2018. The primary outcome was glycaemic control indicated by an HbA1c ≤ 7%. The secondary outcomes were combined control target (HbA1c ≤ 7%, blood pressure (BP), < 140/90 mm Hg, LDL <100 mg/ml) and body mass index (BMI) <30 kg/m2). Descriptive, bivariate analyses and multilevel logistic regression models analyzed factors associated with suboptimal glycaemic control. Results: The mean age was 62.7 years old, 43,2% were female. Glycaemic control was achieved by 63% of patients. Women had better glycaemic control than men (64.2% vs 62.4%). Those who were in quintile 1 of SES achieved glycaemic control more often than those in quintile 5 (65.9% vs 59.7%). The optimal combined target (HbA1c, BP, LDL) was reached by 10% of patients (women: 9.3% vs men: 11.2%), especially those in quintile 5. If BMI<30 kg/m2 was added, the combined target attainment rate decreased to 6.6%. In the adjusted regression model, we found that sex (AOR:0.97, 95%CI: 0.94-1.01) was not related with suboptimal glycaemic control but being part of quintile 4 (AOR: 1.02, 95% CI: 1.06-1.26) and 5 (AOR: 1.20, 95% CI: 1.10-1.32) of SES was associated with not reaching the glycaemic goal.Conclusions: Suboptimal glycaemic and combined control were associated with being less affluent but not with sex in the whole population of people with diabetes in the city of Madrid. Public policies and doctors should take social determinants into account to reduce social and health inequalities.
Robot Motion Planning Benchmarking and Optimization using Motion Planning Pipeline
Authors: Liu Shuai , Liu Pengcheng 
Published: April 2021

Abstract
Algorithms have been designed for robot motion planning with various adaptability to different problems. However, how to choose the most suitable planner in a scene has always been a problem worthy of research. This paper aims to find the most suitable motion planner for each query under three different scenes and six different queries. The work lies in optimization of sampling-based motion planning algorithms through Motion Planning Pipeline and Planning Request Adapter. The idea is to use the pre-processing of the planning request adapter, to run OMPL as a pre-processer for the optimized CHOMP or STOMP algorithm, and connect through the motion planning pipeline, to realize the optimization of the motion trajectory. The optimized trajectories are compared with original trajectories through benchmarking. The benchmarking determines the most suitable motion planning algorithm for different scenarios and different queries. Experimental results show that after optimization, the planning time of the algorithm is longer, but the efficiency is significantly improved. In the low-complexity scenes, STOMP optimizes the sampling algorithm very well, improves the trajectory quality greatly, and has a higher success rate. CHOMP also has a good optimization of the sampling algorithm, but it reduces the success rate of the original algorithm. However, in more complex scenes, optimization performance of the two optimization methods may not be as good as the original algorithm. In future work, we need to find better algorithms and better optimization algorithms to tackle with complex scenes.