bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design of Multivariate PID Controller for Power Networks Using GEA and PSO
Authors: Zadehbagheri Mahmoud , Ma'arif Alfian , Ildarabadi Rahim , Ansarifard Mehdi , Suwarno Iswanto 
Published: June 2023

The issue of proper modeling and control for industrial systems is one of the challenging issues in the industry. In addition, in recent years, PID controller design for linear systems has been widely considered. The topic discussed in some of the articles is mostly speed control in the field of electric machines, where various algorithms have been used to optimize the considered controller, and always one of the most important challenges in this field is designing a controller with a high degree of freedom. In these researches, the focus is more on searching for an algorithm with more optimal results than others in order to estimate the parameters in a more appropriate way. There are many techniques for designing a PID controller. Among these methods, meta-innovative methods have been widely studied. In addition, the effectiveness of these methods in controlling systems has been proven. In this paper, a new method for grid control is discussed. In this method, the PID controller is used to control the power systems, which can be controlled more effectively, so that this controller has four parameters, and to determine these parameters, the optimization method and evolutionary algorithms of genetics (EGA) and PSO are used. One of the most important advantages of these algorithms is their high speed and accuracy. In this article, these algorithms have been tested on a single-machine system, so that the single-machine system model is presented first, then the PID controller components will be examined. In the following, according to the transformation function matrix and the relative gain matrix, suitable inputs for each of the outputs are determined. At the end, an algorithm for designing PID controller for multivariable MIMO systems is presented. To show the effectiveness of the proposed controller, a simulation was performed in the MATLAB environment and the results of the simulations show the effectiveness of the proposed controller.
A Novel Improved Sea-Horse Optimizer for Tuning Parameter Power System Stabilizer
Authors: Aribowo Widi 
Published: June 2023

Power system stabilizer (PSS) is applied to dampen system oscillations so that the frequency does not deviate beyond tolerance. PSS parameter tuning is increasingly difficult when dealing with complex and nonlinear systems. This paper presents a novel hybrid algorithm developed from incorporating chaotic maps into the sea-horse optimizer. The algorithm developed is called the chaotic sea-horse optimizer (CSHO). The proposed method is adopted from the metaheuristic method, namely the sea-horse optimizer (SHO). The SHO is a method that duplicates the life of a sea-horse in the ocean when it moves, looks for prey and breeds. In This paper, The CSHO method is used to tune the power system stabilizer parameters on a single machine system. The proposed method validates the benchmark function and performance on a single machine system against transient response. Several metaheuristic methods are used as a comparison to determine the effectiveness and efficiency of the proposed method. From the research, it was found that the application of the logistics Tent map from the chaotic map showed optimal performance. In addition, the application of the PSS shows effective and efficient performance in reducing overshoot in transient conditions.
Film Boiling Around a Finite Size Cylindrical Apecimen - A Transient Conjugate Heat Transfer Approach
Authors: Cukrov Alen , Sato Yohei , Boras Ivanka , Ničeno Bojan 
Published: June 2023

The DNS of film boiling requires strong computational resources that are difficult to obtain for a daily CFD use by the practitioners of the industrial R&amp;amp;D experts. On the other hand, the film boiling experiments are associated with the usage of the expensive and highly sophisticated apparatus, and the research to this end is found to be relatively difficult due to high heat flow rates that are present in the process itself. When combined with a transient heat conduction in a solid, the problem becomes significantly difficult. Therefore, a novel method in computation of conjugate heat transfer during film boiling in a quiescent liquid has been proposed in this paper. The method relies on the solution of mass, momentum and energy conservation equations in a two-fluid framework, supplemented with the appropriate closures. Furthermore, the turbulent flow has been found as an important parameter in obtaining the accurate solution of the temperature field evolution in a solid specimen, via the proper modeling of turbulent kinetic energy (TKE) value, that has been imposed as a constant value, i.e., the frozen turbulence approach. It was found, in addition, that the appropriate TKE value can be obtained by use of Kelvin-Helmholtz instability theory in conjunction with the boundary layer theory. The obtained results show excellent agreement with the experimental data within the first 15 s of the experiment, i.e., the first ca. 10 % of the total duration of the film boiling mode of heat transfer. Furthermore, the heat transfer coefficient has matched the error bands prescribed by the authors of the paper that has presented the correlations, whilst the averaged values are far beyond this band, i.e., are slightly more than 30 % higher. The further inspection has revealed a measure of similarity between the computational result of the volume fraction field distribution and the experiment, thus confirming the capability of the method to obtain realistic interface evolution in time. The method has shown full capability for further pursuing the industrial-scale film boiling problems that involve turbulent flow and the conjugate heat transfer approach.
Autonomous Inflow Control Valve for Medium-Light Oil and its Effective Performance
Authors: Langaas Kåre , Gisholt Einar , Bjerke Anita , Mathiesen Vidar 
Published: June 2023

Abstract
Most horizontal oil wells will after a time start producing unwanted fluids. Autonomous Inflow Control Valves (AICV) may help to choke these unwanted fluids and consequently improve the carbon efficiency. This paper publishes new experimental data describing how an AICV handles a medium-light oil (6 centipoise), water and gas at full reservoir conditions. A further objective is to evaluate how the AICV might impact well performance under various conditions.
To verify the single and multi-phase flow behaviour of the AICV for medium-light oil viscosity, an extensive multi-phase flow loop campaign was performed. The test was performed under real reservoir conditions, i.e., with formation water, reservoir oil and hydrocarbon gas at the given reservoir temperature and pressure. Preceding the external and independent verification, internal laboratory studies were performed with model fluids. A simple conceptual reservoir model with realistic boundary conditions was built to explore and understand the impact of this AICV for various reservoir scenarios.
At various differential pressures the single-phase oil, water, and gas rates were measured. Performance at varying water and gas fractions were measured to get improved understanding and knowledge of multi-phase flow occurring in a well. The results show clearly that the AICV will choke gas and water effectively, both at single and multi-phase flow conditions. The external and independent verification are consistent with the internal laboratory evaluations with model fluids. The AICV shows roughly a linear transition from 100% oil to 100% gas performance, and similar for 100% oil to 100% water, implying that the AICV will always prioritize sections with the largest oil fraction. A mathematical model match of the AICV performance is possible via the 10-parameter extended AICD equation, that enables practical evaluation of the AICV in industry standard reservoir simulators. Various scenarios are explored with a conceptual reservoir model and the AICV shows its capacity to reduce water production and enable more gradual and controlled increase in gas-oil-ratio for most scenarios. AICV used in segmented reservoirs shows the largest potential to reduce unwanted fluids and in addition increase oil recovery. In cases with uncertain aquifer and/or gas cap strength, or large variation in effective permeability, the AICV will make an infill well more robust as it autonomously adapts to reality and chokes unwanted fluids and consequently enables more carbon efficient reservoir management.
Delivering NET ZERO– A Case Study of Minimized Carbon Intensity Production Using Autonomous Inflow Control Technologies from a Remote Location in the Peruvian Amazon
Authors: Moradi Mojtaba , García Willy , Amado Percy Martin , Konopczynski Michael Robert 
Published: June 2023

Abstract
Growing energy demand heightened by climate change challenges has seen the oil and gas industry tightly embrace smarter and more sustainable technologies. The motivation is to quickly grasp net-zero targets, while safely optimising oil-gas production.
By its nature, the industry has the ingenuity to eliminate unnecessary carbon emissions. However, traditional development plans relied on the use of wells with minimal or no emphasis on the well completion in terms of optimum deliverability. This would produce a mixture of oil and excessive unwanted fluids such as water and/or gas which requires costly energy-intensive processes. Although the process has been optimized to some extent and often re-injects these unwanted fluids back to the reservoir, there has been not enough attention to the environmental impacts as these repetitive treatment processes of the fluids results in discharging excessive and unnecessary Greenhouse Gas (GHG) into the atmosphere. The issue is now widely recognized to be one of the industry challenges in its drive toward net-zero energy delivery.
A case study of a heavy crude oil field with a strong water drive, located in a natural reserve in the Marañon basin of the Peruvian Amazon is presented. Here, the implementation of autonomous inflow control devices (AICDs) technology, through a knowledge management process, has made it possible to significantly reduce the volumes of water produced, which are reinjected again, thus generating significant savings in fluid lifting, treatment and energy consumption associated with the operations in this field.
The study introduces a workflow that uses a publicly available GHG footprint estimator to evaluate the carbon intensity of different oil and gas field development plans. The estimator predicts the amount of GHG emitted from any individual operation, process and treatment involved in a field development from exploration to delivery at the gate of a refinery. Having this calculation enables the operators to recognize the major GHG emitter operations and optimise the process toward net zero using new technologies, methods and/or workflows. The workflow has then been applied to the field located in the Peruvian Amazon to illustrate the significant impact of flow control technologies on the reduction of GHG emissions and achieving net-zero targets. For example, the amounts of carbon intensity, GHG emission and energy consumption from the field have been estimated to been reduced by up to 56%, 64% and 78% respectively with AICD completions compared to a case of non-AICD completion such as stand-alone screen (SAS) was installed in the wells instead.
This study provides the engineers with a workflow to quantify the impacts of the use of new technologies especially flow control devices. It also illustrates the significant role of flow control technologies in achieving net-zero production.
Machine-Learning Based Selection of Candidate Wells for Extended Shut-In Due to Fluctuating Oil Prices
Authors: Lobut Beyza , Artun Emre 
Published: June 2023

Abstract
Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts. In periods of significant drops in the prices, shutting in wells for extended durations such as 6 months or more may be considered for economic purposes. For example, prices during the early days of the Covid-19 pandemic forced operators to consider shutting in all or some of their active wells. In the case of partial shut-in, selection of candidate wells may evolve as a challenging decision problem considering the uncertainties involved. In this study, a mature oil field with a long (50+ years) production history with 150+ wells is considered. Reservoirs with similar conditions face many challenges related to economic sustainability such as frequent maintenance requirements and low production rates. We aimed to solve this decision-making problem through unsupervised machine learning with the help of the data obtained during production. Average reservoir characteristics at well locations, well performance statistics and well locations are used as potential features that could characterize similarities and differences among wells. After a multivariate data analysis that explored correlations between all parameters, K-means clustering algorithm was used to identify groups of wells that are similar with respect to aforementioned features. Using the field’s reservoir simulation model, scenarios of shutting in different groups of wells were simulated. 3 years of forecasted reservoir performance was used for economic evaluation that assumed an oil price drop to $30/bbl for 6, 12 or 18 months. Results of economic analysis were analyzed to identify which group of wells should have been shut-in by also considering the sensitivity to different price levels. It was observed that well performances can be easily characterized in the 3-cluster case as low, medium and high performance wells. Analyzing the forecasting scenarios showed that shutting in all or high- and medium-performance wells altogether during the downturns results in better economic outcomes. The results were most sensitive to the oil price during the high-price era. This study demonstrated the effectiveness of unsupervised machine learning in well classification, particularly for the problem studied. Operating companies may use this approach for selecting wells for extended durations of shut-in in periods of low oil prices.
Energy Transition Strategies to Implement Energy Security of Natural Resources: Albania Case Study
Authors: Hoxha Beso Buranaj , Ternava Trim , Bucaj Dafina 
Published: June 2023

Abstract
The staggering vulnerability of most conventional energy sources has caused the need to diversify the energy mix for most countries, if not all with most recent issues. Subsequently, numerous countries are experiencing a surge in energy demand and are confronted with the need to meet this increase in energy demand with a response to be ‘clean energy’ at an affordable price.
The scope of this paper focuses on defining the different drivers and strategies that developing countries are utilizing to transform their local and interconnected energy sector. For ‘developing’ countries, the primary (current-) focus is on the fundamental commitment for energy diversification (e.g., energy security) in order to achieve sustainable self-sufficient energy source/s that are less impacted by socio-economical or geo-political factors. Furthermore, in this paper, specifically, the case study of Albania will be explained and how its strategy aligns with the global energy transition pathway. Additionally, the paper explores new alternative energy sources; technologies that before were barely considered, and how/why they are being deployed for adoption.
Case Study of the Use of a Digital Twin for Leak Detection and Quantification in Underground Gas Storage Wells
Authors: Baronio Emanuele , Abdo Elia , Mauro Stefano , Troise Mario , Salamina Laura 
Published: June 2023

Abstract
Underground gas storage (UGS) are essential components in energy security. However, UGS wells present a complicated and delicate combination of elements where ensuring safe and secure functionality over long periods is paramount. Today, with the advancement of continuous remote monitoring and digitalization, evaluating the integrity of UGS wells has become quicker and more efficient. This paper showcases how a digital twin is used to evaluate and forecast the link between leaks and temperature and pressure trends in a UGS well, allowing the identification and quantification of defects and, subsequently, well barrier integrity.
UGS wells present additional criticalities with respect to normal production wells due to its longer life span and the repetitive production and injection cycles. This makes early and accurate leak detection essential for a safe management of the well barriers. The proposed digital twin has been developed using material and energy balances and considering each annulus as a separate control volume. Each control volume can exchange heat and mass through predesigned barriers. Simulating evolution in time of pressure and temperature in the control volumes., and comparing results with data from field, allows the identification of position and size of leaks. A genetic algorithm is applied to optimize placement of leaks on their specific barriers. The system aims to identify the position and dimension of possible leaks by matching historical pressure, temperature, and flow data. Once a leak is identified, a risk assessment is conducted to evaluate the overall integrity of the well. If the status of the well is found to be critical enough, an intervention may be planned.
The system has been in use for little over a year and has shown great potential in accurate and efficient identification of leaks. This has accelerated the process of well integrity evaluation and allowed timely interventions on wells that required it. On the other hand, the process has highlighted cases where previous assumptions about leak location and size were corrected using the digital twin, therefore reducing the costs of interventions. Finally, the model showcased a clear readiness for predictive capabilities aimed to select, plan and design fit for purpose mitigating actions.
This paper highlights the power that a digital twin can present leveraging field data with advanced algorithms. The paper also showcases workflows that allow convenient, efficient, and timely evaluation of well integrity, which leads to safer operating conditions and lower operational costs.
High-Temperature DAP Treatments of Carbonate Rocks for Proppant Embedment Severity Mitigation
Authors: Samarkin Yevgeniy , Amao Abduljamiu Olalekan , Aljawad Murtada Saleh , Sølling Theis Ivan , AlTammar Murtadha J. , Alruwaili Khalid M. 
Published: June 2023

Abstract
Fractured carbonate formations composed of chalk and limestone rock lithologies develop several issues over time, reducing fractures’ conductivity. One such issue is the embedment of the proppant that happens due to the soft nature of the carbonate rocks. Reduction of fractures’ conductivity results in the need for refracturing operations that require pumping tremendous amounts of water. The refracturing operations can be avoided if the fractures are maintained conductive for a longer time. This research targets reducing the severity of proppant embedment issues in carbonate formations through rock hardening by diammonium hydrogen phosphate (DAP) treatment.
The chalk and limestone rock samples were treated with a DAP solution of 0.8M concentration at three temperatures, namely 30°C (ambient), 50°C, and 80°C. The samples were treated by immersion in solution, in which rocks were kept reacting for 72 hours. The treated samples were analyzed using the SEM-EDX technique to identify new minerals and changes in the morphology of the rock samples. Moreover, the changes in the hardness of the samples were analyzed by the impulse hammering technique. In addition, the proppant embedment scenario was mimicked in the rocks by utilizing Brinell hardness measurements before and after their treatment.
The SEM analysis demonstrated that the treatment of carbonate rocks with a DAP solution results in the formation of hydroxyapatite (HAP) minerals. In addition, it was observed that the temperature of the treatment affects the crystallization patterns of the HAP minerals. Further results demonstrated that DAP treatment at elevated temperatures significantly improves the hardness of the samples. Young’s modulus of the rock samples increased by up to 60 - 80% after the treatment. In addition, studies have shown the improvement of rocks’ resistance to indentations. The sizes of the dents created by the Brinell hardness device were smaller than before the treatment. Overall, it was demonstrated that the Brinell hardness of the rock samples improved by more than 100%.
This research demonstrated that treating carbonate rocks with DAP solution results in their hardening and improved samples’ resistance to indentation. Moreover, the treatment of rock samples at temperatures similar to reservoir conditions even further improves the mechanical properties of the carbonate rocks. Upscaling laboratory DAP treatment techniques for reservoir applications will introduce new practical methods for maintaining the long-term conductivity of propped fractures. Such a procedure will help avoid refracturing operations, resulting in better and more sustainable management of water resources.
Role of the Hydromechanical Properties of Fault on Fluid Injection-Induced Seismicity with Rate-And-State Dependent Friction Model
Authors: Ma Tianran , M. Nick Hamidreza 
Published: June 2023

AbstractInjecting fluid into subsurface strata has the potential to cause earthquakes by altering pore pressure and subsurface stress. To assess the seismic hazard associated with subsurface flow processes, it is necessary to understand the underlying mechanics of fluid-induced fault reactivation. In this study, we conduct a coupled hydro-mechanical modeling of fluid injection to a strike-slip fault with rate-and-state friction. We account for the fluid flow across and along the fault, as well as the hydromechanical properties of faults in the normal and tangential directions. We model the injection-induced slip of a strike-slip fault, and the simulation results indicate that there are two primary factors that affect injection-induced seismicity. The first factor is that the initiation of rupture is directly related to the diffusion of pore pressure in the near field where there is high shear stress and a large reduction in fault strength due to the significant pressure change. The second factor is that the transfer of shear stress from the nucleation zone promotes the advancement of the slip front to the near- and far field. Our results are quite conservative since the model chose pf as the relevant pressure when calculating the effective normal stress and the shear stress has a slight effect on the pressure variation. Finally, the sensitivity analysis indicates that greater tangential permeability values delay the onset of fault rupture and diminish the likelihood of fault reactivation. Higher stiffness induces fault slip earlier but reduces its magnitude.