Petroleum Industry Research Articles

Advancing Industries with AI: The applications being transformational include: Healthcare, powering the Petroleum industry, Handling fraud, Cybersecurity, and Conversational AI

Today, AI is becoming integrated into more and more sectors of human activity, providing new approaches to a number of traditional problems, and improving multiple aspects of business and industrial processes, decision-making, and customer interactions. This paper explores the applications of AI across five key sectors: health care, petroleum, fraud detection, cybersecurity, and conversational designing. Using AI in healthcare, diagnostics and treatment have been transformed together with improvement of patients’ care, early disease identification and better clinical outcomes. AI is applied in enhancement of exploration, production and refinery of petroleum products in the industry while enhancing sustainability. In fraud detection, AI systems thus offer capability to analyze large datasets in real time to reduce and prevent frauds in banking and insurance industries as well as e-commerce businesses. AI in cybersecurity strengthens threat identification, supports automatic management of preventive measures, and offers risk prognosis, with improved protection measures against cyber threats. And, lastly, conversational AI is emerging in the shape of technologies such as Chatgpt that help enhance customer experience and generate content. However, the increasing number of users of artificial intelligence technologies has a number of problems related to advisor ethics, data protection, system openness, and the dynamic nature of threats. While AI technologies mature, their adoption in these industries raises important questions about ethics, security and legal requirements with a view of mainstream proper and democratic development of these technologies. Raising the question of how to advance AI in the face of its problems, the future of AI remains in the effectiveness of using it as a tool for improving technology’s capabilities and for providing solutions which will make the technology more effective, secure, and more accessible in a number of years.

Unlocking Non-Associated Gas Reservoirs to Power Future Energy Demands in Nigeria

Abstract: Nigeria faces a significant energy crisis characterized by a substantial gap between energy supply and demand, driven by rapid population growth, urbanization, and industrial expansion. With a projected electricity demand exceeding 50,000 MW by 2030, the country must explore underutilized resources to meet its energy needs. Non-Associated Gas (NAG) reservoirs present a viable solution, offering a cleaner and more stable fuel source than traditional fossil fuels. This paper examines the historical context of Nigeria's gas policies, highlighting key legislation such as the Petroleum Act, the Associated Gas Re-Injection Act, and the recent Petroleum Industry Act (PIA) of 2021. It discusses the challenges faced in gas utilization, including infrastructure deficits and investment barriers, while emphasizing the economic, environmental, and social benefits of developing NAG. The study also outlines recommendations for stakeholders to enhance investment in NAG infrastructure and create a favourable regulatory environment. By leveraging NAG, Nigeria can not only address its immediate energy crisis but also promote sustainable economic growth and improve energy security for its citizens.

Bioconvection flow of Carreau nanomaterial invoking Soret and Dufour impacts

Background and objectiveThermal transport enhancement through nanomaterial flow has gained much attention of the investigators recently. Such attention is for applications in engineering, petroleum industries, geothermal engineering and pharmaceutical developments such as X-ray imaging, welding process, semiconductor material production, thermal storage system, electric cooling devices, drug delivery, magma solidification etc. In view of such interest here we analyze the bioconvective stretched flow of Carreau nanoliquid. Thermal, microorganism and mass convective constraints are considered. Gyrotactic microorganisms within presence of chemical reaction is considered. Variable thermal conductivity is considered. Dufour and Soret features are under consideration. Thermophoresis diffusion and random movement features are considered. Energy relation comprises heat generation and radiation. MethodologyNonlinear dimensionless expressions are developed by employing suitable transformations. Numerical computations are obtained by implementation of Newton built in-shooting technique. ResultsPerformance of liquid flow, concentration, microorganism field, and thermal field is studied. Numerical values of quantities of engineering performance via influential parameters are explored. Larger Weissenberg number yield velocity increase. Reduction in velocity through bioconvection Rayleigh number is witnessed. Concentration and thermal field through solutal thermal Biot numbers have similar effect. An increment in thermal distribution and Nusselt number for Dufour number is detected. Increasing values of Soret number and chemical reaction correspond to decay in concentration. An augmentation in thermal transfer rate and temperature through radiation is noticed. An increment in density number against Peclet and bioconvection Lewis numbers is noticed.

The effect of laser shock peening on the fatigue performance of 25Cr2Ni4MoV alloy

High-strength alloy steel, 25Cr2Ni4MoV, finds extensive application in the nuclear and petroleum industries. Experimental investigations and numerical simulations on the influence of laser shock peening on the fatigue characteristics of 25Cr2Ni4MoV alloy are conducted to examine the effect of this strengthening technique. The fatigue dangerous spots are all located at the excessive arc in the left side of the model, according to numerical fatigue simulations; With laser shock peening, the stress amplitude caused by the external load may be greatly reduced, increasing the fatigue life and improving the accuracy of the fatigue simulation model in predicting the potential fatigue fracture site. The fatigue test results indicate that after laser shock peening treatment, the number of fatigue cycles of the specimens was increased from 177,185 to 309,918, representing a significant improvement of 74.9 %. The laser shock peening can markedly enhance the fatigue resistance of 25Cr2Ni4MoV. The fatigue fracture morphology of the specimen exhibits multiple crack initiation sites. Following laser shock peening treatment, the mean distance between the fatigue striations in the fatigue propagation area is reduced. The striation spacing has decreased from 1.34 μm to 1.03 μm, resulting in a decrease of approximately 23.1 %, the rate of fatigue crack growth is reduced, and the area where the crack propagates is significantly increased.

GIS-based modeling of the environmental vulnerability of the Amazon region to the upstream oil and gas activities

ABSTRACT To improve the security of investors in the petroleum industry and to bestow transparency and reliability to its licensing process, the Brazilian Government has recently implemented a Strategic Environmental Assessment (SEA) to evaluate the potential environmental effects of oil and gas exploration and production activities before the concession of areas. To provide robustness to SEA’s analysis stage, this paper aims to propose an innovative methodological approach to map the environmental vulnerability of the area under study, considering selected environmental receptors. This approach combines two techniques, one analytical and the other spatial: environmental risk analysis and spatial analysis. To better illustrate it, this paper presents a case study of the Solimões Sedimentary Basin. Findings show that the approach was able to indicate the more suitable areas inside the SEA region for the development of the O&G upstream activities, as well as to map the vulnerability of each analyzed environmental receptor, to the complete chain of activities, from the exploration for resources until the decommissioning of facilities. This hybrid technique proved to be suitable to the levels of detail required by SEAs and may allow the Government’s goals with it to be achievable, including the environmental issues earlier, in its sectorial PPPs.

Five-stage dynamics of discontinuous oil phase migration in the constricted microchannel: A quantitative analysis of the Jamin effect

The transport of discontinuous oil phases in constricted microchannels is a significant issue within the domains of the petroleum industry, bioengineering, and other industrial processes. During the transport, there is a pressure obstructing the drop flow through the narrow throat, which is named the Jamin effect. In this study, computational fluid dynamics technique is employed to observe and analyze the pressure drop of the discontinuous oil phase during its passage through a constricted microchannel and provides a quantitative analysis for the Jamin effect. The results show that the discontinuous oil phase undergoes five stages during its migration in the constricted microchannel, namely, the preparatory stage, the development stage of the forward meniscus, the quasi-stable stage, the regression stage of the rear meniscus, and the stable stage. Moreover, the composition of resistance in the Jamin effect is not solely due to capillary pressure, as traditional scholars believe. The analysis indicates that the equivalent viscosity of the discontinuous oil phase remains consistent during both the preparatory stage and the stable stage, demonstrating that the magnitude of the equivalent viscosity is unaffected by the profile of the oil phase. Additionally, the effects of oil phase saturation, viscosity, and capillary number on the Jamin effect also have been discussed.

Investigating the Impact of Health-Protective Behaviors on Morbidity and non-Morbidity COVID-19 among Workers in Oil Refining Industry Using Machine Learning algorithms

Introduction: The prevalence of COVID-19 has significantly impacted work environments and the workforce. Therefore, identifying the most important preventive and control strategies, as well as assessing their effectiveness, is of paramount importance. Various studies have shown that machine learning algorithms can be used to predict complex and nonlinear issues, including predicting the behavior of various diseases such as COVID-19 and the parameters affecting it, and can be beneficial. The purpose of this study has been to examine the importance of preventive measures and hygiene behaviors in preventing COVID-19 in the oil refining industry using various machine learning models. Material and Methods: For this purpose, demographic information and health behaviors of individuals were collected. Subsequently, a multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models were compared to enhance the analysis of the effects of preventive measures on COVID-19 infection. Finally, the most influential factors affecting the likelihood of COVID-19 infection were determined using sensitivity analysis. Results: The results showed that the accuracies achieved in predicting the impact of preventive measures and health behaviors on COVID-19 in occupational settings were 78.1%, 81.2%, and 78.1% by MLP, RBF, and SVM respectively. The RBF model was identified as the most accurate model for predicting the impact of health behaviors on COVID-19 disease Additionally, the level of social distancing with customers, handwashing frequency and disinfection, the availability of cleansing and disinfecting agents for hands and surfaces in the workplace, and gatherings for eating meals and snacks were identified as the most significant health behaviors influencing the prevalence of COVID-19 in the workplace. Conclusion: Studies of this nature can underscore the importance of attention to preventive measures and health behaviors in unprecedented circumstances. Furthermore, the utilization of artificial intelligence models and tools such as DSS (Decision Support Systems) can serve as powerful tools for optimizing control measures in work environments.

A study on fatigue life evaluation of 42CrMo steel under cyclic loading based on metal magnetic memory method

Accidents involving drilling tool fractures in the process of oil and gas resource exploration and development occur frequently, and there are few detection methods for detecting the residual fatigue life of drilling tool joints with irregular surfaces. Therefore, developing a quick and effective method to evaluate fatigue damage in drilling tool joints is particularly important. In this study, a fatigue life evaluation method for ferromagnetic materials was established based on metal magnetic memory (MMM) detection technology, which can be used in the petroleum industry to evaluate the remaining life of drilling tool joints. First, the relationship between the impact load and specimen fatigue life was determined through a simulation, followed by a fatigue test, and the magnetic signals on the surface of the specimen under different fatigue loads were collected. The effects of the three evaluation methods were compared, and it was found that dynamic time warping (DTW) was more accurate for the evaluation of metal fatigue. Finally, a fatigue damage evaluation model for ferromagnetic metallic materials was established, and the feasibility of the method was verified using test data. This method can effectively predict the remaining fatigue life of threaded specimens, and can be applied in the petroleum industry to evaluate the remaining fatigue life of drilling tool joints. This study provides guidance for the petroleum industry in ensuring the safety of drilling tools.

Optimizing seismic-based reservoir property prediction: a synthetic data-driven approach using convolutional neural networks and transfer learning with real data integration

Reservoir characterization through seismic data analysis is essential for exploration and production in the petroleum industry. However, seismic-to-well tie discrepancies, limited availability of high-quality well data, and resolution constraints pose a reliability challenge. While previous studies offer valuable insights, they still struggle to achieve high-resolution predictions in a complex geologically environment given high reliance on well data. This study integrates synthetic data-driven techniques with real data, including convolutional neural networks (CNN) and transfer learning, to improve seismic reservoir characterization. We utilize nearby well statistics and a rock physics model (RPM) to simulate pseudo wells representing various geological scenarios. Synthetic seismic gathers are generated from these pseudo wells, which are based on RPM and local well control, to train the CNN. Transfer learning is then applied to adapt the CNN to better distinguish between real and synthetic data, enhancing reservoir predictions. A comparative analysis of P-impedance predictions from three methodologies: theory-driven Pre-Stack-Seismic-Inversion (TDSI), Deep-Neural-Network (DNN), and our CNN approach, showed that CNN achieved nearly 97% prediction accuracy with low error rates, compared to relatively lower prediction accuracy rates of DNN (86.2%) and TDSI (81.5%) with high error rates, according to robust metrics including R-square, RMSE, MSE, and MAE. These results indicate that CNN not only enhanced resolution but also closely aligned with well data and superior lateral continuity, even in blind well scenarios. This study effectively integrates synthetic data-driven techniques with CNNs and transfer learning to advance seismic reservoir property prediction, offering a robust solution to overcome limitations in traditional and DNN-based approaches.

Investigating the effect of hole size, bottom hole temperature, and composition on cement bonding quality of exploratory wells in Iran

The oil and gas industry’s reliance on well cementing practices to ensure well integrity and productivity is a pressing concern, particularly in exploratory wells where unpredictable conditions can lead to subpar cement bonding efficiency. This study addresses the pressing issue of optimizing cementing practices in Iran’s exploratory wells, ensuring better well integrity and productivity. The preliminary objective is to investigate the impact of hole size and bottom hole temperature variations on cement bonding efficiency in these wells. We analyzed cement quality logs from 21 exploratory wells in reservoir zones between 2012 and 2022 to achieve this. The study employed a systematic approach, using CBL/VDL and cement formulation to assess the quality of each wellbore section separately. We designed a cement formulation based on changes in bottom hole temperature and gas migration control and implemented it using three different contractors, each with unique additives. It allowed us to compare the quality of cement resulting from different phases and additives. Our results show that cement designed with dominant liquid additions in the 5-inch liner phase exhibits better bonding than formulations with increased powder form additives, specifically those containing a liquid dispersant, liquid fluid loss controller, liquid anti-gas migration, and liquid H.T. retarder for LNR 5 inch. Additionally, smaller hole sizes demonstrate better cement bonding quality due to reduced fluid flow and less turbulence. These findings have significant implications for optimizing cementing practices in the petroleum industry, particularly in Iran’s exploratory wells. Overall, this investigation provides valuable insights for improving wellbore integrity and productivity by optimizing cementing practices, which can be applied to future drilling operations in Iran’s exploratory wells.

A SHACL-Based Approach for Enhancing Automated Compliance Checking with RDF Data

Automated Compliance Checking (ACC) has emerged as a critical tool for enforcing legal regulations across various domains. This paper contributes to ongoing research in Semantic Web technologies, particularly focusing on the execution of SHACL-SPARQL rules on RDF data. The RDF, being one of the most widely used knowledge representation (KR) formats, serves as the foundation of our approach, ensuring compatibility with existing standards and enhancing interoperability. Our research enhances the aggregate and temporal aspects of ACC by addressing the limitations of traditional ACC methodologies, which often fall short in managing the nuanced temporal and aggregate requirements essential for legal reasoning. Through a case study analysis of selected regulations with aggregate and temporal facets in LI 2204, which regulates local content and participation in Ghana’s upstream petroleum industry, this paper demonstrates the effectiveness of the proposed solution in achieving these dimensions of ACC. The findings underscore the potential of Semantic Web technologies to transform ACC practices by moving towards standardized, interoperable solutions. All source codes are freely available online together with instructions to locally reproduce the simulations.

Numerical simulation of temperature distribution and residual stress in TIG welding of stainless-steel single-pass flange butt joint using finite element analysis

Abstract Controlling defects such as deformation in the weld joint and the residual or superfluous stresses due to tungsten inert gas (TIG) welding or arc welding is a major concern for many industries like aeronautical, automobiles, nuclear or atomic power plants, crude oil or fossil fuel industries where pipes are in use and circumferential welding is done. Arc welding is a metal joining process, and TIG welding is applied to many industrial sectors that require high-quality welding. Simulation has been done on single-pass TIG welding on the Flange pipe of SS316 to evaluate transient temperature, residual stresses, and distortion. First, a 3D model is developed and assembled in SolidWorks. Second, in an MSC Patran, preprocessing of the FE model is done. Finally, in MSC Marc, thermal and mechanical simulation is performed. Based on this simulation, the accuracy of welding of the flange–butt joint made of SS316 is validated. In this study, the information regarding simulation of temperature dispensation and residual or superfluous stresses is done on the flange–butt joint, and it found the stresses are compressive at the weld bead area, and along the transverse direction, stresses changed to the tensile. The experimental data show that the steep curve at 0.00 mm represents a maximum temperature near the weld path at approximately 2,352°C, and the slant curve shows the far away points from the weld path. Comparing it with FE analysis, the maximum temperature attained was around 2,539°C. An approximate deviation of 7.365% was observed. The results of the study will provide experimental and simulation analyses for the welding of pipes of stainless steel for the transportation of oil and gases in the petroleum industries.

A novel anti-scaling and protection coating based on hollow periodic mesoporous organosilica encapsulated with scale inhibitor

Scaling and corrosion are common issues in the petroleum industry that compromise production safety. Currently, organic coatings are considered the most effective method for scaling inhibition. This paper presents the synthesis of hollow periodic mesoporous organosilica (HPMO) nanocontainers with a high specific surface area and a uniform, ordered mesoporous structure, using a hard template method. The morphology of HPMO was controlled by adjusting the water-to-ethanol ratio and precursor amount. Characterization of HPMO morphology and structure was conducted using scanning electron microscope, transmission electron microscope, X-ray diffraction, and nitrogen adsorption-desorption analysis. To enhance loading capacity, HPMO was modified with 3-aminopropyl triethoxysilane, increasing the encapsulation of the scale inhibitor ethylenediamine tetramethylphosphonic acid (EDTMP) from 3.6 % to 13.8 %. The modified HPMO, loaded with the scale inhibitor, significantly improved the protective and anti-scaling properties of epoxy resin coatings. Compared with pure epoxy coatings, the scale inhibition rate of the composite coating increased by 42 % under static conditions and 90 % under dynamic conditions. The crystalline morphology of calcium carbonate scale was also influenced by the scale inhibitor; EDTMP promoted the formation of acicular aragonite and inhibited cuboid calcite growth in static conditions. In dynamic conditions, aragonite was not observed, and the calcite crystals were smaller than those formed in static solutions.

Implementation of Embedded PID Controller to Water Level Control System

In chemical and industrial processes, the level control of liquids in tanks is significant and widely used, especially in power stations and petroleum industries. The liquid level can be controlled by the most common method, PID control due to its efficiency and performance in achieving the desired set point. In this paper, the water level in tank control using a level sensor and PID controller system will be designed and implemented based on the PID Arduino microcontroller algorithm that controls the change in flow rate by controlling a DC water pump with the help of a MOSFETs interface circuit. Tuning the specific PID level tank controller will be implemented to increase the system accuracy with no error and overshoot when there is a disturbance (drawn or excess water).

Politics of Economic Rents and Petroleum Resources Taxation in Oil and Gas Industry

This paper argues the politics of economic rents and petroleum resources taxation in oil and gas industry. It does this through the understanding concept of economic rent, royalties, corporation tax, resource rent tax and brown tax. It surveys the comparative merits and demerits of royalties, corporation tax, resource rent tax and brown tax. The paper discusses the concept of tax structure and proposes that it should be progressive and fair to both government and investors. The principles of a good tax structure should include simplicity, convenience, ability to pay, economical, equity and certainty. While the characteristics of a good tax structure should include proportionality, it should ensure fairness and acceptability by both the government and the citizens together with foreign investors. This way, it enhances the longevity of that particular tax system and rewards will be very high. Although taxation is mandatory to all income earners and profits gainers, there are key economic considerations in designing a tax structure for new petroleum producing countries. The paper argues these economic considerations to include ownership of petroleum resources, good investments laws, strong, accountable and transparent economic institutions, good fiscal and monetary policies, land ownership, strong private banking industry, market driven insurance policies, market for consumption of petroleum products, dynamic and friendly technology, strong regulatory environment and affordable trained workforce. As the taxation is implemented, the concept of source and residence taxation become very handy and the paper argues that source of the income and residence of the investor should be taken into the consideration from the onset of the designing of tax system and particularly in oil and gas industry. While the source of income may be in the country and residence outside the country, the tax system should carefully consider this so that the investor and the tax agency (the government) consent and each party plays it role conveniently. Although advantages and disadvantages of source tax and residence tax stand, the main rules in determining sources of investment income remains of source rules. The paper notes interest-based politics has let to unsuccessful implementation of tax system in oil and gas industry. While the paper reviews copies of empirical literature, it uses comparative method and process-tracing in understanding politics of economic rents and petroleum resources taxation in oil and gas industry. It concludes that incremental sliding scale taxation is appropriate in oil and gas industry and politics of greed affects the tax regime implementation in petroleum industry. Keywords: politics, economic, rents, petroleum, resource, taxation, structure, oil, gas, industry

High-efficient nanoemulsions separation by surface manipulation of sands

Oil-in-water (O/W) nanoemulsions, prevalent in the cosmetics, pharmaceutical, and petroleum industries, present significant threats to aquatic ecosystems and human health upon their inadvertent release into the environment. However, the nanoscale droplet size and robust interfacial film of nanoemulsions confer exceptional stability, rendering their separation a formidable challenge. Developing an economical and efficient method to remove nanoemulsions is crucial, offering a cost-effective and energy-saving alternative to traditional techniques. Here we present a sand filtration system integrating oleophilic and oleophobic sands, achieving cost-effective, high-flux, and efficient separation of nanoemulsions. The synergistic effect of hydrophobic silane treatment and a rough surface structure endows the oleophilic sands with exceptional oil absorption capacity. Meanwhile, the hydration layer on the oleophobic sands confers strong anti-oil adhesion properties in aqueous environments, facilitating swift water permeation. Remarkably, under gravity alone, the mixed rough sand filter (MRSF) achieves a separation flux exceeding 2519 L.m−2.h−1 with a separation efficiency of up to 98.7 %. This work offers a promising approach for achieving high-flux, efficient nanoemulsion separation.

First field application of functionalized nanoparticles-based nanofluids in thermal enhanced oil recovery: From laboratory experiments to cyclic steam stimulation process

The increasing global demand for fossil fuels and the depletion of light crude oil reserves have driven the petroleum industry to focus on exploiting heavy crude oils, which present significant challenges in recovery and processing. To address these challenges, enhanced oil recovery (EOR) technologies are being developed, with a strong emphasis on advances in catalysis and nanomaterials science. This research significantly contributes to developing new technologies for petroleum exploitation by introducing a novel nanofluid designed to facilitate in-situ upgrading of heavy oil, improving its quality for downstream refining and fuel production. The nanofluid, engineered to enhance the productivity of a heavy oil reservoir under cyclic steam stimulation, targets improvements in oil recovery and fuel-quality indicators such as API gravity and viscosity. Laboratory tests demonstrated the nanofluid’s capability to reduce oil viscosity, improve oil mobility, and selectively interact with heavy oil fractions like resins and asphaltenes. Displacement tests simulating steam injection conditions showed an improvement in oil recovery, increasing from 56 % to 76 % after nanofluid application. The treatment also led to a notable increase in API gravity, from 11.6° to 29.2°, and a significant reduction in viscosity, from 39,987 cP to 104 cP, indicating enhanced crude oil quality, critical for refining and fuel production. Field trials in two wells in Colombia demonstrated the nanofluid’s practical effectiveness, with production increases averaging 97 % and incremental yields of 11,966 barrels in well A and 3213 barrels in well B. Post-treatment, the crude oil exhibited sustained improvements in quality, with API gravity increasing from 11.6° to 13.4° and viscosity decreasing from 39,987 cP to 11,734 cP. These results confirm the long-term durability of the nanofluid’s effects and its potential to enhance fuel production from heavy oil reservoirs. Additionally, the field trial indicated a 48 % reduction in operational costs, primarily due to decreased steam generation and lower CO2 emissions, highlighting the environmental and economic benefits of nanofluid technology for petroleum exploitation.

Surface Nanocrystallization and Improvement of the Mechanical and Tribological Properties of AISI 304 Steel Using Multi-Pass Nanostructuring Burnishing.

Owing to their high producibility and resistance to corrosion, austenitic chromium-nickel steels are widely used in the chemical, petroleum, and food industries. However, their significant disadvantage lies in their poor structural performance, which cannot be improved by heat treatment. This significantly limits the usability of these steels in parts of machines that operate under friction loads. Hardening can be achieved by decreasing the size of grains and applying deformation-induced martensitic transformation. Nanostructuring burnishing (NSB) may be one of the technologies suited for producing parts of tribological assemblies with enhanced operating characteristics. Nanostructuring burnishing using a sliding indenter is being developed as a method of industrial surface nanocrystallization through severe plastic deformation used in the mechanical machining of various types of parts. This article investigates the possibility of enhancing the mechanical and tribological properties of nanocrystallized surfaces of austenitic steels, which are formed through nanostructuring burnishing using a tool with a natural diamond spherical indenter and a change in sliding speed from 40 to 280 m/min with one, three, and five passes. Increasing the tool sliding speed makes surface nanostructuring machining of big parts highly effective. This paper aims to establish the influence exerted by the sliding speed and number of indenter passes on the formation of a nanocrystalline structure, as well as on the modification of microhardness and residual stresses, texture, and tribological properties of the surface layer in the nanostructuring burnishing of AISI 304 steel. Transmission microscopy and microdurometry, 3D-profilometry, and tribological tests of surfaces nanocrystallized with the "ball-on-disk" scheme with dry and lubricated friction established the optimal values of speed and number of passes for a spherical indenter in nanostructuring burnishing.

Groundwater quality assessment and pollution sources identification using statistical analyses at Missan Governorate, Southeast Iraq

The shortage in surface water quantities led to a shift in dependence on the groundwater as an alternative water source in southern parts of Iraq. The groundwater is decreasing in quantity and water quality is degrading due to different factors. Therefore, it is important to assess the groundwater quality of the Missan Governorate of the country by analyzing the physicochemical parameters and distinguishing the probable sources of contaminants in the area. The present study used water quality diagrams and statistical methods such as factor analysis and agglomerative cluster analysis to determine the sources of chemical ions in the forty-four groundwater samples collected from wells in the study area. In addition, the Water Quality Index (WQI) was calculated to evaluate the groundwater quality by combining the parameters into a single index. The WQI results varied from − 66.27 to 1064.13 indicating that the groundwater is unsuitable for drinking at various places and good in quality at most of the wells of the area. The main factors that contribute to the differences in the groundwater quality include the variation in geological materials, influence of climate such as the high rate of evaporation, and anthropogenic factors such as industrial activities in central areas such as oil production in the Bazergan oil field. The most affected wells by the petroleum industry that were clustering in cluster R1which show high levels in most water chemistries. The results have implications for the management of the groundwater resources, which are expected to be under more risk of contamination in near future.

Analysis of Microscopic Remaining Oil Based on the Fluorescence Image and Deep Learning.

Fossil fuels like oil and natural gas continue to be the primary sources of global energy. Enhancing hydrocarbon recovery from exploited reservoirs has been a major scientific concern in the petroleum industry. Following extended exploitation, the reservoir's oil-water dynamics become intricate, thereby complicating petroleum and natural gas extraction. Pore-scale analysis of microscopic remaining oil (micro-remaining oil) offers theoretical underpinning for enhancing production from high-water-cut oil reservoirs. Fluorescence thin-section analysis allows for the direct evaluation of reservoir oil-bearing properties using oil-containing samples, providing insights into the occurrence and distribution patterns of micro-remaining oil without requiring time-consuming core displacement experiments. The high resolution of fluorescence images further establishes this technique as a representative method for studying micro-remaining oil. However, conventional fluorescence image analysis methods are often subjective and labor-intensive. To address this limitation, we trained four deep learning networks-U-Net, ResU-Net, ScSEU-Net, and Unet++-and applied them innovatively to automate fluorescence image segmentation. Evaluation of network performance via statistical metrics and visual observation indicated that all four networks achieved high segmentation accuracy, particularly ResU-Net, which showed robustness against over-segmentation, under-segmentation, and image noise. Finally, leveraging optimal segmentation results, we conducted quantitative analyses of oil saturation, micro-remaining oil patterns, and pore occupancy. The study demonstrated that ternary composite agents substantially decreased the presence of cluster, film, and adsorbed oils byreducing the oil-water mobility ratio and lowering oil-water interfacial tension. Primarily, these agents displaced crude oil from pores larger than 60 micrometers in an equivalent radius, leading to a significant reduction in their content. Nevertheless, substantial quantities of micro-remaining oil are still confined in pores smaller than 50 micrometers in an equivalent radius, emphasizing the need for attention during subsequent development adjustments. Our research has notably improved the efficiency and accuracy of fluorescence image analysis, effectively supporting the enhancement of recovery in high-water-cut oil reservoirs.