Arabian Journal for Science and Engineering最新文献

{"title":"Utilizing Atomic Force Microscopy for Microstructural Surface Analysis and Reservoir Evaluation of Shale","authors":"Lei Xu,&nbsp;Rui Shen,&nbsp;Hang Yang,&nbsp;Hekun Guo","doi":"10.1007/s13369-024-09401-y","DOIUrl":"10.1007/s13369-024-09401-y","url":null,"abstract":"<div><p>This study leverages atomic force microscopy (AFM) to analyze shale structure, thereby assessing the reservoir capacity of shale formations. The methodologies employed include the observation of surface morphology, characterization of surface roughness, mechanical property measurements, examination of interfacial forces between solids and liquids, and evaluation of surface potential. Utilizing these methods, we have examined shale samples from the Weiyuan area and discovered that different samples exhibit varying degrees of roughness and surface potential distribution. Additionally, we have identified a correlation between the mechanical properties of functional probes made from oil droplets and the samples. The study’s main conclusion underscores the effectiveness of AFM as a tool for evaluating shale reservoir capacities. Through a thorough investigation, this research illuminates the current state of AFM applications in shale studies and suggests prospective avenues for future research.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 7","pages":"5253 - 5263"},"PeriodicalIF":2.6,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the Influence Mechanism of Cement Sheath and Formation Elastic Modulus on the Mechanical Behavior of Oil and Gas Well Casing","authors":"Jiajia Jing,&nbsp;Yan Chen,&nbsp;Yang Wang,&nbsp;Xiuming Zhang,&nbsp;Yi Xiang,&nbsp;Xuelun Zhang,&nbsp;Siyan Liu,&nbsp;ZiYin Jiang","doi":"10.1007/s13369-024-09724-w","DOIUrl":"10.1007/s13369-024-09724-w","url":null,"abstract":"<div><p>Casing is the physical barrier of oil and gas well, and its integrity is the key to realize wellbore integrity. Cement sheath is one of the most important barriers to ensure the integrity of casing. There are many studies on the influence of mechanical parameters of cement sheath on the mechanical behavior of casing, but most studies ignore the close relationship between the casing force and the elastic modulus of the cement sheath and formation. Therefore, a two-dimensional finite element model of casing-cement sheath-formation is established; the influence of sensitive factors on the mechanical behavior of casing under the elastic modulus different matching relationships of cement sheath and formation was analyzed. The adaptability analysis of the elastic modulus of the two was carried out to optimize the range of elastic modulus of cement sheath. The results show that the elastic modulus different matching relationships between cement sheath and formation may lead to a 90° transformation of the maximum stress distribution of casing, resulting in a great difference in the location of casing damage in the same geological blocks. Moreover, the matching relationship between the two affects the influence of sensitive factors such as casing wall thickness, wellbore ellipticity and casing eccentricity on casing stress. Therefore, it is necessary to optimize the range of cement sheath elastic modulus according to the elastic modulus of formation and working conditions. The research results are of great significance for the study of casing failure mechanism and the selection of elastic modulus of cement sheath.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 7","pages":"5233 - 5252"},"PeriodicalIF":2.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design, Development and Validation of a Novel Mechanical Pain Inducer and the EMG Signal Analysis for the Induced Mechanical Pain","authors":"Nayan Jyoti Boro,&nbsp;K. Shankar","doi":"10.1007/s13369-024-09732-w","DOIUrl":"10.1007/s13369-024-09732-w","url":null,"abstract":"<div><p>The measurement of physiological parameters is a crucial method for measuring pain objectively. Precise measurement of physiological parameters, including electromyography (EMG), can categorize the pain intensity levels objectively. This work designed and developed a novel mechanical pain inducer to induce pain in human subjects and analysed the EMG signal for normal (pain-free or no pain) and different intensity of pain levels (i.e., threshold, moderate, and tolerance). This mechanical pain inducer applies force to a healthy individual’s hand to create different pressure intensity levels to cause pain. Twenty volunteers (male 11, female 9) in the age range of 20–40 years participated in this experiment. The different levels of pain intensity are measured using the numerical pain rating scale based on the participant's reaction. The EMG signal was acquired for both the normal state and the different pain levels. The calculation for the root mean square (RMS) value and analysis of variance were carried out on the EMG signals for normal and each level of pain. The investigation of the RMS value of the EMG signal shows that as the intensity level of pain increases, the RMS value of the EMG signal also increases accordingly for each level of pain<b>.</b> This indicates that the induced pain can affect the electrical potential of the EMG signal. Also, a significant mean difference in the RMS value is observed between normal and different intensities of pain levels at <i>p</i> &lt; 0.05.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 8","pages":"5979 - 5988"},"PeriodicalIF":2.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Obtaining the Optimum Concentration of TiO2/ZSM-5 Nanocomposites and Demonstrating Novel Approaches to Increase Oil Recovery in Carbonate Reservoirs","authors":"Yaser Ahmadi,&nbsp;Mohsen Mansouri","doi":"10.1007/s13369-024-09717-9","DOIUrl":"10.1007/s13369-024-09717-9","url":null,"abstract":"<div><p>Nanoparticles have been employed in many fields to improve oil recovery methods in recent years. This work introduced TiO₂/ZSM-5 nanocomposites (TZ-NCs) at the optimal concentration as a new enhanced oil recovery agent. The results from pure TiO₂ nanoparticles (T-NPs) and TZ-NCs were then compared. The reservoir conditions were applied in this study for wettability change, interfacial tension, and zeta potential tests. The same operational ideas were used to create spontaneous imbibition experiments after identifying the optimal concentration. Both TZ-NCs and T-NPs samples were characterized using SEM, FTIR, EDAX, XRD, and BET. XRD confirms the phase purity and high crystalline nature of the prepared T-NPs. The analysis of physicochemical properties confirmed the successful presence of titanium on the ZSM-5 zeolite substrate and the formation of the TZ-NCs. Different concentrations, ranging from 0.05 to 0.3 wt%, were used, and based on the highest zeta potential, contact angle reduction, and interfacial tension, 0.15 wt% was recommended as an optimum concentration for T-NPs and TZ-NCs. At 0.15 wt%, (contact angle, zeta potential) were changed to (72.03°, -45.35 mV) and (50.10°, -50.48 mV) with T-NPs and TZ-NCs, respectively. In order to get ready for additional study, higher oil recovery experiments were conducted for both NPs and NCs at an optimum concentration. After 350 h of operation, the percentage of oil recovered from base, T-NPs, and TZ-NCs increased to 35.70, 58.61, and 67.29%, respectively.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 7","pages":"5223 - 5232"},"PeriodicalIF":2.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Automated Approach for Discriminating Hole Cleaning Efficiency While Predicting Penetration Rate in Egyptian Western Desert Wells","authors":"Mohamed Y. Saad,&nbsp;Adel M. Salem,&nbsp;Omar Mahmoud","doi":"10.1007/s13369-024-09706-y","DOIUrl":"10.1007/s13369-024-09706-y","url":null,"abstract":"<div><p>Higher rate of penetration (ROP) indicates successful drilling operation but is not the only drilling success measure. However, Conventional ROP prediction methods focus on increasing ROP and neglect the hole cleaning state, which can be altered by ROP changes. Higher ROP in vertical and deviated wells may increase cutting concentration, leading to hole cleaning problems such as overpulling and stuck pipe. With this problem in mind, this paper utilized geological, rheological, and drilling data of 31 vertical wells across four oil fields located in the Egyptian Western Desert, developed intelligent ROP prediction model through back propagation neural network (BPNN), and compared the proposed BPNN results with an empirical model. Finally, the pattern recognition algorithms including discriminant analysis, support vector machines, and neural network pattern recognition (NNPR) were implemented to discriminate hole cleaning efficiency following the ROP prediction process. Recognition models were developed based on predicted ROP, bit wear rate, specific energy, and drilling fluid carrying capacity index to evaluate hole cleaning. The accuracy of the multi-strategy classifier was evaluated using area under curve, confusion matrix, and receiver operating characteristic. The BPNN model outperformed the empirical model in terms of linear correlation coefficient (R = 98.6%) and average absolute error (AAE = 5.5%). Additionally, the best classification performance was achieved using the NNPR algorithm with 91% accuracy and a cross-validation error equal to zero. For validity, the proposed approach predicted ROP and classified hole cleaning efficiency for new vertical well in adjacent oil field, resulting in a 6% improvement in ROP.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 7","pages":"5195 - 5221"},"PeriodicalIF":2.6,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13369-024-09706-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Enhanced Cascaded Five-Level Inverter Based on Switched Capacitors and Single dc Source","authors":"Prabir Ranjan Kasari,&nbsp;Subhadeep Bhattacharjee","doi":"10.1007/s13369-024-09544-y","DOIUrl":"10.1007/s13369-024-09544-y","url":null,"abstract":"<div><p>An alternative approach of the cascaded H-bridge inverter (CHB) in the form of an enhanced cascaded multilevel inverter is introduced in this paper. The proposed topology comprises two integrated switched-capacitor modules, an inductor and eight power switches, enabling the generation of five-level voltage with simply a single dc source. It has the capability for boosting the voltage as well as being able to exchange power on either side with reduced isolated source count. In this cascaded H-bridge multilevel inverter module, the capacitor charging phenomenon is independent of the load and the switching techniques developed are suitable to generate multilevel output voltages. Besides, the current spike issue for switched capacitors is found to be low in this topology. The output voltage enhancement and bidirectional power flow capability distinguish the proposed CHB multilevel inverter over other available technologies. Both simulation and experimental results are presented in the paper to validate the operational principle of the proposed bidirectional enhanced five-level cascaded H-bridge inverter (E5CHB).</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 8","pages":"5963 - 5978"},"PeriodicalIF":2.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Approach with Multiple Feature Selection Techniques to Diagnose the Inter-Turn Winding Faults in Induction Motor","authors":"Rajeev Kumar,&nbsp;R. S. Anand","doi":"10.1007/s13369-024-09681-4","DOIUrl":"10.1007/s13369-024-09681-4","url":null,"abstract":"<div><p>This paper presents an innovative and effective approach for detecting, analysing and classifying stator winding faults in induction motor using the motor current signature analysis (MCSA), combined with machine learning models. Stator inter-turn winding faults are a critical issue affecting the reliability of induction motors, which require accurate fault detection to maintain motor performance and prevent failures. This approach employs advanced signal processing techniques for fault identification, including signal envelope identification analysis, Park’s vector magnitude analysis and zero-crossing time detection (ZCTD), to extract deep features from stator current under both healthy and faulty motor conditions. The motor fault features are computed using statistical feature analysis methods from recorded current signals. The most appropriate feature subsets are identified using feature selection techniques known as Fisher Score, minimum redundancy maximum relevance (m-RMR) and Relief. In the classification stage, conventional machine learning models like k-nearest neighbours (k-NN), logistic regression (LR), random forest (RF) and support vector machine (SVM) are applied to these selected features to efficiently classify the healthy and faulty states of induction motor. To validate the proposed methodology, an experimental study is conducted in the laboratory to record stator current data from both healthy and multiple fault phases of the induction motor under varying load conditions. Hence, this paper presents a promising solution for accurate fault detection and classification of stator winding faults, reducing the need for extensive manpower and sensor usage while enhancing the reliability of predictive maintenance schemes.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 8","pages":"5945 - 5961"},"PeriodicalIF":2.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine-Learning Predictive Model for Semiautomated Monitoring of Solid Content in Water-Based Drilling Fluids","authors":"Shadfar Davoodi,&nbsp;Sergey V. Muravyov,&nbsp;David A. Wood,&nbsp;Mohammad Mehrad,&nbsp;Valeriy S. Rukavishnikov","doi":"10.1007/s13369-024-09689-w","DOIUrl":"10.1007/s13369-024-09689-w","url":null,"abstract":"<div><p>Accurate and frequent monitoring of the solid content (SC) of drilling fluids is necessary to avoid the issues associated with improper solid particle concentrations. Conventional methods for determining SC, such as retort analysis, lack immediacy and are labor-intensive. This study applies machine learning (ML) techniques to develop SC predictive models using readily available data—Marsh funnel viscosity and fluid density. A dataset of 1290 data records was collected from 17 wells drilled in two oil fields located in southwest Iran. Four ML models—least squares support vector machine (LSSVM), multilayered perceptron neural network, extreme learning machine, and generalized regression neural network—were developed to predict SC from the compiled dataset. Multiple assessment techniques were applied to attentively evaluate the models’ prediction performances and select the best-performing, SC prediction model. The LSSVM model generated the least errors, exhibiting the lowest root-mean-square error values for the training (1.80%) and testing (1.84%) subsets. The narrowest confidence interval, 0.18, achieved by the LSSVM model confirmed its reliability for SC prediction. Leverage analysis revealed minimal influence of outlier data on the LSSVM model's SC prediction performance. The trained LSSVM model was further validated on unseen data from another well drilled in one of the studied oil fields, demonstrating the model’s generalizability for providing credible close-to-real-time SC predictions in the studied fields.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 7","pages":"5175 - 5194"},"PeriodicalIF":2.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Peak Current Limit Strategy and Active Power Oscillation Reduction in a Three-Phase Grid-Interfaced PV-Battery System (GIPVBS) with Low Voltage Ride-Through Control","authors":"Abhishek Kumar Singh,&nbsp;Dinesh Kumar Tiwari,&nbsp;Nalin Behari Dev Choudhury,&nbsp;Jiwanjot Singh","doi":"10.1007/s13369-024-09625-y","DOIUrl":"10.1007/s13369-024-09625-y","url":null,"abstract":"<div><p>This research study presents a grid-interfaced photovoltaic (PV) battery-assisted system with a single-stage configuration and low-voltage ride-through (LVRT) control that adheres to the Indian grid code standards. The suggested LVRT approach ensures continuous connection of solar power to the grid, preventing system shutdown during grid disturbances and faults. The inverter ensures continuity of the connection by supplying reactive power to the grid, utilizing its capacity to effectively restore stable voltage levels at the grid connection point. A straightforward yet efficient inverter peak current limiter control is proposed, determining the maximum allowable power for various scenarios and adjusting the grid injection current accordingly. A control algorithm is also recommended to mitigate active power oscillations and address DC-link voltage fluctuations. To enhance the system’s dynamic performance post-fault, a modified drift-free Perturb and Observe (P&amp;O) Maximum Power Point Tracking (MPPT) algorithm is implemented. The proposed approach effectively limits the maximum current to the inverter’s rated capacity while reducing active power oscillations and stabilizing the DC-link voltage. The system’s performance is validated through simulations using MATLAB/Simulink, which involves an 8.3 kW single-stage grid-connected solar PV plant subjected to both symmetrical and asymmetrical faults. The setup and control system is implemented on a real-time test bench (RTS) called OPAL-RT 4510 for practical validation. The results obtained from this real-time setup are then compared with the outcomes of the simulation, confirming the effectiveness of the LVRT control strategy.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 8","pages":"5921 - 5943"},"PeriodicalIF":2.6,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Line Enhancer for Passive Sonars Based on Frequency-Domain Sparsity, Shannon Entropy Criterion and Mixed-Weighted Error","authors":"Zhe Li,&nbsp;Yusheng Cheng,&nbsp;Jiaxing Qiu","doi":"10.1007/s13369-024-09682-3","DOIUrl":"10.1007/s13369-024-09682-3","url":null,"abstract":"<div><p>Adaptive line enhancer (ALE) is one of the vital signal processing techniques to the detection and recognition of underwater acoustic targets for passive sonars. Conventional ALEs, based on Gaussian noise assumption and least mean square (LMS) algorithm, can achieve good line enhancement property in Gaussian noise background. However, limited by the high steady-state misadjustment of LMS algorithm, the performance of conventional ALEs deteriorates under non-Gaussian noise background and degrades severely in processing signals with comparably lower signal-to-noise ratio (SNR). Therefore, it’s of great necessity to improve the line enhancement performances of ALE techniques to meet the demands of engineering application in passive sonars. In order to optimize the robustness and adaptability of conventional ALEs in dealing with underwater acoustic signals with much lower-SNR and in non-Gaussian noise background, a modified ALE algorithm called frequency-domain ALE based on <i>l</i>₁-norm, Shannon entropy criterion and mixed-weighted norm (<i>l</i>₁-SE-MWE-FALE) is proposed in this paper. The proposed <i>l</i>₁-SE-MWE-FALE algorithm is based on the integration of frequency-domain sparsity, Shannon entropy (SE) criterion along with mixed-weighted error of LMS and least absolute deviation (LAD) to improve the ALE performance in situations above. The simulation results demonstrate that, when the input SNR is as low as – 25 dB, the local SNR (LSNR) gain for line spectrums by <i>l</i>₁-SE-MWE-FALE is 9.8 dB, 3.7 dB and 2.3 dB higher than conventional ALE, <i>l</i>₁-norm-based frequency-domain ALE (<i>l</i>₁-FALE) and <i>l</i>₁ norm-Shannon entropy criterion-based frequency-domain ALE (<i>l</i>₁-SE-FALE), respectively. Meanwhile, the simulation results also indicate that the parameters of the proposed method can be chosen loosely and hence are insensitive to the choice of their values. Furthermore, the processing results of two different kinds of real ship-radiated noise signals recorded by passive sonars also imply the advantages of the proposed method over the other three ALEs both qualitatively and quantitatively in the respect of line spectrum LSNR gain and parameter insensitivity. The simulation and experiment results both validate the performance insensitivity to parameter adjustment and hence exhibit a good perspective of applications for passive sonars.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 8","pages":"5899 - 5920"},"PeriodicalIF":2.6,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13369-024-09682-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}