Production Operations Research Articles

Research on Convergence Media Ecological Model Based on Blockchain

Currently, the media industry is in the rapid development stage of media integration, which has brought about great changes in content production mode, presentation form, communication mechanism, operation and maintenance management, etc. At the same time, it is also faced with problems such as difficult information traceability, declining industry credibility, low data circulation quality and efficiency, difficult data security and user privacy protection, etc. Utilizing blockchain’s characteristics can solve these problems that the media industry is currently facing. This paper designs a convergence media ecology model based on blockchain (CMEM-BC), focusing on the basic elements of the model, node operation and maintenance system, node management mechanism, value circulation mechanism, and storage mechanism, trying to establish a decentralized, traceable, and immutable convergence media ecosystem. On this basis, this paper summarizes the ecological framework and ecological model of CMEM-BC. Finally, the paper describes the verification of the effectiveness of CMEM-BC in key links through simulation experiments, verifying that CMEM-BC has high originality and is more suitable for the application of convergence media ecology through model analysis and comparison.

Evaluating the radioactivity and health risks in oil and gas production areas: insights from Titas Gas Field, Bangladesh

ABSTRACT Conducting comprehensive studies of radioactivity and associated risk assessments in the oil and gas industry is crucial due to the potential accumulation of Naturally Occurring Radioactive Materials in production operations. This pioneering study investigates radioactivity levels in surface soil from the Titas Gas Field area in Bangladesh by a High Purity Germanium (HPGe) detector, a crucial resource of the nation’s energy infrastructure. In surface soil, the activity of 226Ra, 232Th, and 40K varied from 18–41 Bq/kg, 28–62 Bq/kg, and 280–600 Bq/kg, respectively. Particularly, the levels of 232Th exceeded the global average value for soil. These elevated 232Th levels are likely attributed to a combination of natural geological processes of enriching thorium bearing minerals, geochemical processes favouring thorium retention and human activities selectively impacting these radionuclides. The estimated of radiological risk reveals that some indices exceed recommended limits, hence pose a concern for public health. The study recommends periodic monitoring, public awareness campaigns, remediation efforts, and long-term management strategies to protect environmental and public health. The findings of this research underscore the critical need to recognise and address radiological hazards associated with gas extraction activities to safeguard the health and safety of workers and nearby communities in Bangladesh.

Models and P4R asset description for digital twin-based advanced planning and scheduling using cyber-physical integration for resilient production operation

Advanced planning and scheduling (APS) addresses the complex and uncertain nature of production control. A digital twin (DT), which incorporates simulations through cyber-physical integration, provides an advanced functionality for APS. To facilitate efficient design and implementation, a DT-based APS must satisfy three requirements: technical functionalities for resilience, robust models for diverse operational constraints, and efficient interoperability through cyber-physical integration. Although several studies have proposed the use of DT as a primary technology for APS, proposals that address the process, functionality, integration, and information models are lacking. Additionally, the existing asset descriptions cannot adequately capture the sophisticated characteristics of DT and necessary informational elements for APS. Thus, this study designed a process model, functionalities, and integration models for the DT-based APS and asset descriptions for snapshot synchronization. Crucial service-compositions and functionalities were defined using work-center-level lifecycles. Consequently, a process model was developed, which focused on core activities for resilience. Moreover, horizontal integration between DT and control functionalities and vertical integration between DT and standards, were proposed to enhance the DT-based APS. The proposed method effectively managed the product, process, plan, plant, and resource classes by ensuring adherence to asset administration shell principles. To validate the effectiveness of the proposed methods, two work centers with distinctly different characteristics were employed and demonstrated dominant preventive measures compared to static functionality-based methods. The primary contributions encompass the facilitation of integration and interoperability within a DT-based APS. The proposed methods support the advanced characteristics of DT, ensuring robustness and neutrality across heterogeneous operational contexts.

Analysis and implementation of the dmaic six sigma method as an effort to control weaving fabric production defects: Industrial textile case study

Defective items are those that are produced during the manufacturing process but do not meet the imposed quality standards. These items can be repaired economically if certain costs are covered, which must be less than the post-repair selling value of the product. Semi-finished textiles are produced by the research institute. There are still issues with the production of semi-finished cloth. As a result, the researcher attempted to control and evaluate flaws in the production of semi-finished fabrics by combining the DMAIC approach (Define, Measure, Evaluate, Improve, and Control) with the six-sigma method. A million production operations have a 3.3% Defect Per Million Opportunities (DPMO), or a sigma level value of 2,625 or 133,219 probability of damage, according to the study's findings. There are two types of production-related damage: non-process and process. After one month of implementing and evaluating improvements, there has been a gradual improvement in all production processes, with the most recent calculation for June 2024 yielding a sigma value of 3.51.

Advanced methods for laser dismantling of spent fuel assemblies and fuel rods in the primary operations of the robotic production in a closed nuclear fuel cycle

Advanced methods for laser dismantling of spent fuel assemblies and fuel rods in the primary operations of the robotic production in a closed nuclear fuel cycle

Research on Production Operation System of Sucker Rod Pumping Units

Oil is the most important raw material in modern industry, and its extraction mainly relies on natural flow and artificial lifting. Sucker rod pumping is currently the most widely used method of artificial lifting for mechanical oil extraction, accounting for about 60% to 70% of all methods. This paper deeply explores the theoretical methods involved in the production operation process of sucker rod pumping units. From the perspective of sucker rod pumping unit production operation, a literature review is conducted on the flow patterns in the wellbore, pump efficiency analysis, and intermittent oil extraction systems, clarifying the optimization objectives of liquid production and system efficiency, and sorting out from the aspects of dynamic liquid level, pump efficiency, reasonable submergence depth, and intermittent operation systems. Through in-depth analysis of these key areas, a solid methodological foundation is provided for the future design and development of sucker rod pumping unit production operation systems.

Influence of ore-drawing port position on ore-rock flow characteristics in ore pass and lateral pressure on ore pass wall

The blockage and the deformation and failure of the ore pass walls constitute two major problems in applying the ore passes in mines. These problems, which affect the normal operation of mine production, have attracted widespread attention worldwide. The labeled-particle layers method based on numerical simulation was used to investigate the flow characteristics of the ore-rock bulk in the ore pass under different eccentric distances of the ore-drawing port center and ore pass centerline. Moreover, the overpressure coefficient and overpressure number are used to evaluate the degree of damage to the ore-pass wall. The results show that: (1) During the ore drawing process under different eccentricities, the flow patterns of the topmost labeled-particle layers in the ore pass are always in a “—” shaped distribution, and the other layers in the ore pass gradually transition from a “—” shape to a “U” shaped distribution, and then gradually to a “V” shape closer to the drawing funnel; (2) in the range of the ore-drawing funnel, the flow pattern of the ore-rock bulk gradually changes from an upright “V” shape to an italic “V” shape with increasing eccentricity and tip slants to the drawing port, and is less affected in the shaft; and (3) the dynamic lateral pressure caused by the ore-rock flow mainly acts on the lower part of the storage section. When the eccentricity is 0.5 m, the maximum overpressure coefficient and overpressure times are the smallest, leading to the lowest damage degree of the ore pass wall.

Low-Cost, High-Efficiency Aluminum Zinc Oxide Synaptic Transistors: Blue LED Stimulation for Enhanced Neuromorphic Computing Applications.

Neuromorphic devices are electronic devices that mimic the information processing methods of neurons and synapses, enabling them to perform multiple tasks simultaneously with low power consumption and exhibit learning ability. However, their large-scale production and efficient operation remain a challenge. Herein, we fabricated an aluminum-doped zinc oxide (AZO) synaptic transistor via solution-based spin-coating. The transistor is characterized by low production costs and high performance. It demonstrates high responsiveness under UV laser illumination. In addition, it exhibits effective synaptic behaviors under blue LED illumination, indicating high-efficiency operation. The paired-pulse facilitation (PPF) index measured from optical stimulus modulation was 179.6%, indicating strong synaptic connectivity and effective neural communication and processing. Furthermore, by modulating the blue LED light pulse frequency, an excitatory postsynaptic current gain of 4.3 was achieved, demonstrating efficient neuromorphic functionality. This study shows that AZO synaptic transistors are promising candidates for artificial synaptic devices.

Steel mill solid waste transformation: Optimizing converter sludge recycling through RHF technology

Current methods for treating converter sludge include reintroducing it into the sintering or pelletizing process. However, this method leads to the enrichment of zinc in the blast furnace, which affects the stability of the production operation. To solve this problem, a direct reduction process utilizing multi-solid waste collaborative rotary hearth furnace (RHF) has been implemented in steel plants to recover and safely dispose of valuable components in waste. The laboratory results show that the dezincization rate of the metallized pellets is 99.12 % and the metallization rate is 80.51 % under the premise of reducing 50 °C by calcination at 1200 °C for 15 min in the nitrogen atmosphere. In the industrial test conducted in RHF production line of Shagang from March 2022 to August 2023, the temperature in the high temperature zone was reduced by 50 °C, and the time in the high temperature zone was controlled to 15 min. The industrial test results showed that the residual zinc content in the metallized pellets was 0.63 %, and the metallization rate was about 75.12 %. These particles can be used as feedstock or coolant in the steelmaking process. The average grade of by-product zinc oxide coarse powder can reach 65.88 %, which can be further processed into zinc products with remarkable economic benefit. Through RHF integrated management of converter sludge and other solid waste in steel mills, metallized pellets and high-value products can be produced, which can effectively reduce solid waste in steel enterprises and improve resource utilization.

Sustainable biodiesel production from Cassia javanica oil with eggshell-derived catalyst: optimization, engine performance, and emission study

ABSTRACT As global energy demand rises, it is crucial to explore sustainable alternatives. Biodiesel emerges as a safe, reusable, and eco-friendly energy source. It offers a greener option to petro-diesel while maintaining reliable performance. This study focuses on producing biodiesel from novel non edible feedstock Cassia javanica seeds. It uses a calcium oxide (CaO) catalyst derived from eggshells. CaO, a highly active base catalyst, was confirmed through various analyses. Using Cassia javanica seed oil as a source of non-edible methyl ester, this study optimizes transesterification parameters. A yield of 90.14% was achieved by transesterifying Cassia javanica oil at 60°C with 2% catalyst and 15:1 methanol to oil ratio. Based on the results, Cassia javanica methyl esters were found to conform to ASTM D6751 and EN 14,214 standard specifications, with kinematic viscosity of 3.556 mm2/s and acid value of 0.41 mg KOH/g, cloud point of −4°C, pour point of −2°C, flash point of 148°C and cetane index of 59 min. The biodiesel properties were also estimated. Tests revealed that blends had lower emissions and improved engine performance compared to pure fuel. Higher blends showed better engine performance. The B20 blend was found to be the most effective. It enhances engine performance and reduces emissions. C. javanica could be a viable source of biodiesel production and engine operation. As a result of sensitivity analyses, NPV varied with feedstock costs and biodiesel selling prices. In India, interventions are needed to make B20 policy based on second-generation technology profitable. The policy with feedstocks from the second generation of technology is technically viable, but interventions are needed to make it economically viable.

Overseas production or domestic production? Impacts of tax disparity and market difference

Overseas and domestic production are two commonly employed strategies for multinational firms (MNFs) to manage their global production operations. Recent tax-cutting initiatives have made domestic production an attractive option for MNFs. We aim to investigate whether such initiatives can effectively induce MNFs to produce domestically, especially when they cater to both domestic and foreign markets. In this study, we develop a game-theoretical model that considers an MNF with two subsidiaries - a production subsidiary and a retail subsidiary - located in different markets, and a third-party seller that resells the MNF's product in the domestic market. We take into account two important differences between the two markets, namely the tax disparity and the market potential difference. Our analysis reveals that a higher foreign market potential can strengthen the MNF's tax-saving incentive under the domestic production strategy. We find that a relatively lower domestic tax rate may not induce the MNF's domestic production even if the foreign market potential is low. On the other hand, when the domestic channel competition is intense, a higher domestic tax rate may still incentivize the MNF to produce domestically. These results depend on the tradeoffs among the tax-saving benefit, the third-party seller's free-riding, and the channel competition. We caution that promoting domestic production through a low tax rate may harm domestic consumer surplus, highlighting the need for policymakers to carefully consider the impact of tax policies on consumers.

PERTANGGUNGJAWABAN PIDANA BAGI PELAKU TINDAK PIDANA PERTAMBANGAN MINERAL DAN BATUBARA (ANALISIS PUTUSAN PN PRAYA NOMOR 123/PID. B/LH/2023/PN PYA)

The mining sector is one of the supports for the welfare of the Indonesian people. Therefore, mining activities must be carried out according to procedures and have legality as applicable regulations. Criminal liability for perpetrators of mineral and coal mining crime Analysis of Decision Number 123/Pid.B/LH/2023/PN Pya is that the defendant has been proven guilty and fulfills the elements in Article 160 of Law of the Republic of Indonesia Number 3 of 2020 concerning amendments to Law Number 4 of 2009 concerning Mineral and Coal Mining. Criminal liability for case Number 123/Pid.B/LH/2023/PN Pya that the Defendant has been proven guilty and meets the elements in Article 160 of Law of the Republic of Indonesia Number 3 of 2020 concerning amendments to Law of the Republic of Indonesia Number 4 of 2009 concerning Mineral and Coal Mining, namely the provision that Every person who has an IUP or IUPK at the Exploration activity stage but carries out Production Operation activities shall be punished with a maximum imprisonment of 5 (five) years and a maximum fine of IDR 100,000,000,000.00 (one hundred billion rupiah). When associated with the maximum penalty, the penalty imposed on the Defendant is still relatively low because mining without a permit is a very serious case and has a major impact on the state and society. The judge's consideration in imposing a criminal sentence based on juridical and non-juridical considerations, the judge decided that the defendant was sentenced to imprisonment of 7 (seven) months and a fine of Rp. 20,000,000.00 (twenty million rupiah) subsidair 2 (two) months.

Assessment of a solar-powered trigeneration plant integrated with thermal energy storage using phase change materials

This study presents a comprehensive thermodynamic assessment of a trigeneration plant producing electricity, fresh water through multi-effect desalination (MED), and cooling through an absorption refrigeration cycle. The MED and absorption refrigeration systems utilize the rejected heat from the power cycle, driven by concentrated solar power (CSP). Situated in Qatar, the present system leverages the abundant solar irradiance to optimize the efficiency of electricity generation, water desalination, and cooling. The design features parabolic trough collectors with synthetic oil as the heat transfer fluid, direct thermal storage, and a Rankine steam turbine cycle with three turbine stages. The system also incorporates phase change materials (PCMs) based thermal energy storage (TES) to improve the system performance and offset the mismatch between demand and supply. The present system evaluation is based on energy and exergy analyses, while the Aspen Plus is used to simulate the power production and desalination operations, providing detailed insights into its efficiency and potential for large-scale implementation. The proposed system operates with an energy efficiency of 56.72 % and an exergy efficiency of 31.24 %, respectively.

Simulation Model of a Steelmaking-Continuous Casting Process Based on Dynamic-Operation Rules.

The steelmaking-continuous casting process (SCCP) is a complex manufacturing process which exhibits the distinct features of process manufacturing. The SCCP involves a variety of production elements, such as multiple process routes, a wide array of smelting and auxiliary devices, and a variety of raw and auxiliary materials. The production-simulation of SCCP holds a natural advantage in being able to accurately depict the intricate production behavior involved, and this serves as a crucial tool for optimizing the production operation of the SCCP. This paper thoroughly considers the various production elements involved in the SCCP, such as the fluctuation of the converter smelting cycle, fluctuation of heat weight, and ladle operation. Based on the Plant Simulation software platform, a dynamic simulation model of the SCCP is established and detailed descriptions are provided regarding the design of an SCCP using dynamic-operation rules. Additionally, a dynamic operational control program for the SCCP is developed using the SimTalk language, one which ensures the continuous operation of the caster in the SCCP, using the discrete simulation platform. The effectiveness of the proposed dynamic simulation model is verified by the total completion time of the production plan, the transfer time of the heat among the different processes, and the frequency of ladle turnover. The simulation's results indicate that the dynamic simulation model has a satisfactory effect in simulating the actual production process. On this basis, the application effects of different schedules are compared and analyzed. Compared with a heuristic schedule, the optimized schedule based on the "furnace-machine coordinating" mode reduces the weighted value of total completion time by 8.7 min, reduces the weighted value of transfer waiting time by 45.5 min, and the number of rescheduling times is also reduced, demonstrating a better application effect and verifying the optimizing effect of the "furnace-machine coordinating" mode on the schedule.

IoT-ENHANCED SHALE OIL AND GAS GREEN DEVELOPMENT: STATUS AND FUTURE

Shale oil and gas, deemed as vital alternative resources to conventional oil and gas, have spurred significant interest in information-based exploration and development technologies within the industry in recent years [1]. China boasts extensive shale oil and gas reserves, with the International Energy Agency (EIA) estimating China's shale oil technology capable of extracting approximately 4.393 billion tonnes, ranking third globally. Additionally, China holds shale gas geological resources of about 134.4 trillion cubic meters, ranking first worldwide, showcasing immense development potential. Shale oil and gas, being unconventional resources, are distributed within the pore and fracture systems of organic-rich shale formations. Compared to conventional oil and gas, shale oil and gas exhibit weaker trap control and stronger distribution continuity, typically characterized by poor reservoir physical properties. Conventional extraction methods often struggle to yield industrial oil and gas flow from shale oil and gas resources, necessitating unconventional development technologies such as reservoir monitoring, directional wells, horizontal wells, and segmental fracturing [2]. However, these technologies come with challenges and risks, including complex production operations, high production data density, and environmental concerns such as surface ecological damage and pollutant emissions, further impacting shale oil and gas production. To facilitate the upgrading process of China's shale oil and gas industry, this paper presents the current status of Internet of Things (IoT) application technologies for shale oil and gas development and proposes a research outlook based on the concept of green development. Against the backdrop of energy transformation, it offers a reference framework for the sustainable development of China's extensive shale oil and gas resources.

Pengaruh Work Motivation, Work Discipline dan Training Terhadap Employee Work Productivity

The success of a company is measured by how optimally it uses existing resources to achieve high productivity. This research aims to find out how much influence work motivation, work discipline and training have on the work productivity of PT employees. IEI. This type of research is quantitative research analyzed using statistics. The author is an employee of PT. IEI in the production operator section has a total population of 62 employees. The sample size uses a saturated sample by means of all members of the population being sampled. Data analysis used SPSS Version 25. This research resulted that the variables of work motivation, work discipline and training had a positive and simultaneous effect on the work productivity of PT employees. IEI. This is proven by the results of F calculated 59.747>2.76 (F calculated>F table) so that Ho4 is rejected and Ha4 is accepted. This means that there is a positive and significant influence between work motivation, work discipline and training simultaneously on the work productivity of PT employees. IEI.

Multi-agent reinforcement learning for integrated manufacturing system-process control

The increasing complexity, adaptability, and interconnections inherent in modern manufacturing systems have spurred a demand for integrated methodologies to boost productivity, improve quality, and streamline operations across the entire system. This paper introduces a holistic system-process modeling and control approach, utilizing a Multi-Agent Reinforcement Learning (MARL) based integrated control scheme to optimize system yields. The key innovation of this work lies in integrating the theoretical development of manufacturing system-process property understanding with enhanced MARL-based control strategies, thereby improving system dynamics comprehension. This, in turn, enhances informed decision-making and contributes to overall efficiency improvements. In addition, we present two innovative MARL algorithms: the credit-assigned multi-agent actor-attention-critic (C-MAAC) and the physics-guided multi-agent actor-attention-critic (P-MAAC), each designed to capture the individual contributions of agents within the system. C-MAAC extracts global information via parallel-trained attention blocks, whereas P-MAAC embeds system dynamics through permanent production loss (PPL) attribution. Numerical experiments underscore the efficacy of our MARL-based control scheme, particularly highlighting the superior training and execution performance of C-MAAC and P-MAAC. Notably, P-MAAC achieves rapid convergence and exhibits remarkable robustness against environmental variations, validating the proposed approach’s practical relevance and effectiveness.

Technology Focus: Reservoir Surveillance (September 2024)

In today’s oil and gas operations, surveillance technologies have undergone a revolution, reshaping how facilities, wells, and reservoirs are monitored. These advancements not only have increased the scale at which these technologies are deployed but also have led to an unprecedented influx of data. The sheer volume of data, however, poses a significant challenge to traditional analytical methods, overwhelming their capacity to derive actionable insights effectively. To address this challenge, the industry is rapidly advancing toward automated solutions powered by artificial-intelligence (AI) -driven analytics. These systems automate data ingestion and use machine-learning algorithms to sift through massive data sets, identifying anomalies and prioritizing actionable insights. By automating routine surveillance tasks, engineers can focus on critical actions that deliver substantial operational benefits. For example, in paper IPTC 23912, operators successfully optimized production operations by harnessing real-time field data through smart systems, effectively managing operations with complex near-critical fluids. Similarly, in paper SPE 218470, researchers proposed a novel workflow integrating virtual flowmetering and permanent downhole gauge data for pattern recognition to enhance real-time monitoring and decision-making in petroleum and geothermal industries. Nevertheless, ensuring effective surveillance of operational assets requires a strategic approach. A valuable tool in crafting such strategies is the value of information (VOI) assessment. This method systematically evaluates how acquiring specific information can influence decision-making and operational outcomes. For instance, paper SPE 215318 highlights a field operator’s systematic approach to VOI assessment, aiming to optimize daily operations and guide future development activities. In essence, while surveillance technologies have inundated operators with unprecedented data flows, advancements in automation and AI-driven analytics offer the promise of unlocking this data’s true potential. By embracing these technologies, the oil and gas industry can navigate the complexities of the modern energy landscape with greater agility, precision, and cost-effectiveness. Recommended additional reading at OnePetro: www.onepetro.org. SPE 215119 Surveillance, Analysis, and Optimization During Active Drilling Campaign by Yanfen Zhang, Chevron, et al. OTC 35413 New Opportunities in Well and Reservoir Surveillance Using Multiple Downhole Pressure Gauges in Deepwater Injector Wells by Piyush Pankaj, ExxonMobil, et al. OTC 34863 Digital Twin for Oil-Rim Management Using Early Warning System and Exception-Based Surveillance, Offshore Malaysia by M. Mahamad Amir, Petronas, et al.

Heterogeneous products operation decisions of online dual-channel supply chain considering online reviews under different financing modes

This research examines the operation decisions of the online supply chain for heterogeneous products under different financing modes: e-commerce platform financing or bank financing, when manufacturers face funding constraints. Considering the manufacturer’s adoption of differentiated channel strategies when providing heterogeneous products is also considered, and combined with the impact of online reviews on consumer utility, an e-commerce platform online dual-channel financing model is constructed. The research findings are as follows: (i) when the effectiveness of online reviews differs within a certain range, the equilibrium solution exists. If the relative interest rates of the e-commerce platforms and bank change within a certain range, the same financing mode can bring mutual benefits to both the manufacturer and e-commerce platform, resulting in a “win–win” situation. (ii) If the interest rates under both financing modes are the same, the e-commerce platform financing mode has a higher wholesale price, but the difference in retail prices of distribution products depends on the costs difference between the two products, and at this point, the manufacturer will select e-commerce platform financing mode. (iii) At the optimal interest rate, when the cost of heterogeneous products is the same, the e-commerce platform consistently offers a more favorable interest rate compared to the bank’s optimal rate. when the e-commerce platform’s commission and the positive difference in product reviews is large, the manufacturer will choose e-commerce platform financing mode. Under the e-commerce platform financing mode, both the manufacturer and e-commerce platform are willing to provide lower retail prices to attract more consumers.

Enhancing subsurface multiphase flow simulation with Fourier neural operator

Accurate numerical modeling of multiphase flow in subsurface oil and gas reservoirs is critical for optimizing hydrocarbon recovery. However, traditional physics-based algorithms face substantial computational hurdles due to the need for fine grid resolution and the inherent geological heterogeneity. To overcome these challenges, data-driven surrogate models solving the flow governing partial differential equations (PDEs) offer a promising alternative to enhance the efficiency of hydrocarbon production operations. In this study, we employ the Fourier Neural Operator (FNO) to extract spectral information from the reservoir properties, thereby facilitating the solution of coupled porous flow PDEs. Our focus is on two-phase flow dynamics, specifically exploring how water injection enhances reservoir pressure and displaces oil. This scenario involves solving a set of nonlinearly coupled PDEs with highly heterogeneous coefficients. Numerical results demonstrate that the developed FNO accurately predicts the reservoir pressure distributions. We further observe that the FNO's zero-shot super-resolution capability is sensitive to abrupt local changes in the reservoir pressure near injection and production wells. To enhance its accuracy, we propose a multi-fidelity FNO model that exhibits better adaptability across various grid configurations. After moderate training on graphics processing units (GPUs), the FNO achieves a speedup of three orders of magnitude compared to traditional numerical PDE solvers. Our experiments confirm the FNO's potential to replace repetitive physics-based simulations, significantly advancing computational efficiency in the uncertainty quantification of reservoir performance.