Carbonate minerals (calcite and dolomite) and pyrite are important brittle minerals in organic-rich shales; however, the mechanism of their formation is still uncertain. This study examined the formation of pyrite and carbonate minerals (mainly calcite and dolomite) in the shales of Longmaxi (LMX) Formation through a combination of petrological and geochemical techniques. The results demonstrated that two types of carbonate minerals (calcite and dolomite) and multiple-types of pyrite were identified in the studied shales. The carbonate minerals comprise (1) subhedral to euhedral calcite and dolomite with large diameters (>10 μm), along with low Fe (<2%) and Mn (<0.2%) contents, and (2) calcite and dolomite cement, along with high Fe (>2%) and Mn (>0.2%) contents. The pyrite is primarily composed of framboidal pyrite, infilled framboidal pyrite, pyritic masses, and euhedral pyrite. This study suggests that depositional and diagenetic processes had significant influences on the formation of calcite-dolomite-pyrite in organic-rich shales. In the synsedimentary stage, the expansion of anoxic seawater contributed to the enrichment of organic matter in the shales of lower LMX Formation. In the early diagenesis stage, both the microbial sulfate reduction (MSR) and sulfate-driven anaerobic oxidation of methane (SD-AOM) would generate abundant CO₂, which together with the consumption of further contribute to the precipitation of carbonate minerals (calcite and dolomite). In addition, the MSR would also release ³²S-enriched H₂S and result in the formation of framboidal pyrite/infilled framboids/pyritic masses with low δ³⁴S values (<0‰). However, the SD-AOM would result in the precipitation of euhedral pyrite with high δ³⁴S values (>20‰) and high Co contents (>700 μg/g). In the late diagenesis stage, carbonate cements with high Fe and Mn contents formed due to the diagenetic fluids (high Mg²⁺, Fe²⁺, Mn²⁺ concentrations). Overall, the sulfate-driven anaerobic oxidation of methane (AOM) during early diagenesis in sediments constitutes a key process influencing methane release, in which case extensive methane release during this period may have contributed to Early Silurian global warming.

This study explores the depositional and diagenetic heterogeneities of the Sarvak Formation, a paleokarst carbonate reservoir in the Zagros Basin, Iran, using sedimentological, diagenetic, and statistical analyses. Petrographic studies and well log data, combined with methods like Winland rock type classification, hydraulic flow unit analysis, and Lorenz zonation, were used to assess reservoir properties at multiple scales. Two heterogeneity scales were identified: small-scale (millimeter to centimeter), driven by facies variations and diagenesis, and large-scale (meter), linked to facies transitions and sequence stratigraphy. Large-scale heterogeneities, particularly those tied to sequence stratigraphic boundaries, are traceable within third-order sequences and systems tracts. The highest-quality reservoir intervals, characterized by rudist-rich zones and dissolved shoal facies, are found beneath the Cenomanian-Turonian boundary, preserving youthful karst features. In contrast, non-reservoir intervals beneath the mid-Turonian disconformity exhibit collapsed features from prolonged subaerial exposure, forming an over-mature karst profile. Small-scale heterogeneities require further investigation within lower-order sequences for accurate prediction. This research integrates various analytical methods, providing a robust framework for understanding how depositional and diagenetic factors control reservoir quality, enhancing predictability in karstified carbonate systems for hydrocarbon exploration and development.

Surfactant flooding is one of the widely applied enhanced oil recovery methods. However, the application of a single surfactant is often insufficient to meet the demand for oil recovery. Consequently, a variety of surfactant mixtures have been employed to enhance oil recovery efficiency. A comprehensive understanding of the interactions between surfactant mixtures, crude oil, formation water and rock is essential for the effective design and optimization of the surfactant flooding procedures. In this work, anionic-zwitterionic and anionic-nonionic surfactant mixtures with varying compositions were investigated using different microfluidic devices. Single dead-end pore structure that was captured from a real rock pore throat structure was initially employed for a quick screening of the optimum surfactant composition, considering the effect of the presence of connate brine. The selected surfactant compositions were tested using a micromodel with heterogeneous pore-throat network. The results indicate that 0.4 wt% sodium dodecyl benzene sulfonate with 0.6 wt% octadecyl dimethyl betaine and 0.4 wt% sodium dodecyl benzene sulfonate with 0.6 wt% hexyl D-glucoside surfactant mixtures resulted in higher recovery rate in the dead-end pore structure. In the subsequent micromodel experiments, 0.4 wt% sodium dodecyl benzene sulfonate with 0.6 wt% octadecyl dimethyl betaine showed better oil recovery performance. To further explain the oil recovery performance of the surfactant mixtures, interfacial tension, viscoelasticity and critical micelle concentration values were measured and calculated to provide an insight into their interfacial activities. It was observed that anionic-zwitterionic and anionic-nonionic surfactant mixtures displaced oil in different manners attributing to their distinct interfacial properties. The anionic-zwitterionic surfactant mixture primarily displaced the crude oil through solubilization and emulsification due to its lower interfacial tension and critical micelle concentration of the surfactant mixture, whereas the anionic-nonionic surfactant mixture displaced oil through peeling and mobilization as a result of relatively higher interfacial tension and interfacial viscoelasticity.

Mathematical techniques continue to become more advanced in developing more complex solutions in different fields, especially in oil reservoir simulation, where various physical parameters are still unknown. Studying different simulation cases that improve predictability is required to comprehend the wellbore behavior under production properly. This article proposes a new analysis method that utilizes the first-order asymptotic expansion method to address single-phase oil flow in multilayered reservoirs with permeability-pressure dependent and source terms. The classical techniques, like pseudo-pressure functions, although offering small linearization of the diffusivity equation, are found to be insufficient and thus require more efficient techniques like the Perturbation Method. The proposed model allows detailed analysis of different reservoir properties and pressure-dependent permeability curves, therefore allowing for more effective optimization and understanding of the flow behavior in complex geological conditions.

Shale reservoirs are characterized by low permeability and a weak capacity for natural seepage. These reservoirs are commonly developed using a combination of horizontal well drilling and fracturing. The production performance of shale oil wells is evaluated through single-well production decline analysis, with the estimated ultimate recovery (EUR) used as an important indicator of the development outcome. This study predicted the EUR of the X233 well zone in the Qingcheng Shale Oil Field in the Ordos Basin. It reviewed the applicability of production forecasting methods to unconventional reservoirs and put forward a correlation-based approach to EUR prediction. The latter was developed by integrating the characteristics of Qingcheng shale oil production with EURs predicted using empirical methods intended for typical wells. The correlation-based approach was also designed on the basis of the relationship between cumulative oil production and the empirically derived EURs. The results suggest that the proposed method rapidly predicts the EUR of a single well using cumulative oil production from the first one, two, and three years of production. The predictions closely align with the empirically derived values, demonstrating the strong practical effectiveness of the approach put forward in this work. To determine its applicability and limitations, this study examined the EUR distribution characteristics of different well classes (Classes I to III). This research provides a simple and effective approach to rapidly predicting the EURs of shale oil wells, significantly reducing computational time and labor costs.

Fines migration is a formation damage mechanism that occurs in sandstone formations due to the incompatibility between injected fluids and in situ fluids during various operations such as water flooding, drilling, and completion. Fines migration can lead to production problems such as pore blockage and reduced permeability. Parameters such as salinity, pH, and flow rate affect the forces between fines and the rock surface, potentially triggering fines migration. Therefore, predicting the conditions that lead to fines release and migration is critical for ensuring safe operations. Enhanced oil recovery methods, such as low-salinity water injection and alkaline flooding, alter the salinity and pH within the formation, affecting the electrostatic interaction forces between fines and the rock surface. Hydrodynamic forces also influence fines release. Therefore, predicting the initiation of fines migration requires considering and analyzing all these forces. In this work, a modified method based on DLVO theory was developed to predict the critical pH for fines release. The model estimates the onset of fines migration by evaluating electrostatic and hydrodynamic repulsive and attractive forces. For example, the model predicted critical pH values of 8 and 10 at salinities of 0.2 M NaCl and 0.25 M NaCl, respectively. Several core flooding tests were conducted to measure the critical pH for fines release, validating the developed model. The experimental observations showed good agreement with the model, with an error of approximately 13% in predicting the critical pH. The developed approach enables the identification of optimal brine conditions to mitigate fines migration and formation damage, which is a significant finding for enhanced oil recovery and production operations.

Rock surface wettability plays a crucial role in gas flooding for oil recovery. Natural rock surfaces are usually oleophilic, resulting in high flooding resistance and low recovery in gas flooding. This study explores the regulation of the wettability of sandstone rock surfaces by modifying them with 1H,1H,2H,2H-perfluorodecyltrimethoxysilane or its mixtures with hexyltrimethoxysilane to achieve oil contact angles ranging from 0° to 93.2°. Simulated gas flooding experiments, with N₂ as model gas, reveal that reducing the oleophilicity of rock surfaces improves gas flooding efficiency. Compared to the unmodified superoleophilic rocks, the final oil recoveries in the cores with neutral oil wettability significantly increases and the pressure differential for gas flooding significantly decreases. In cases of cores with permeability of 22 mD, 0.88 mD and 0.09 mD, the increases in oil recovery are from 28.5% to 37.5%, from 20.2 to 27.3% and from 9.1% to 15.4%, respectively; and the reductions of pressure differential for gas flooding are from 53 kPa to 41 kPa, from 323 kPa to 272 kPa, from 1054 kPa to 935 kPa, respectively. These results highlight the critical influence of wettability regulation on gas flooding efficiency, offering valuable insights for optimizing oil recovery strategies.

In oil and gas production operations, wellbore instability is primarily caused by shale hydration. Thus, a thermally stable inhibitor is essential to prevent shale hydration. This research focuses on synthesizing a copolymer of maleic acid and acrylic acid/graphene oxide (poly-(AAMA)-GO) via free radical polymerization as an effective and low-cost inhibitor. The combination of the copolymer and graphene oxide aims to produce a fluid inhibitor with high thermal stability. Additionally, the unmodified copolymer of acrylic acid and maleic acid poly-(AAMA) was synthesized for comparative analysis. The inhibitory properties of both compounds were evaluated using anti-swelling, immersion, and shale recovery tests, with potassium chloride (KCl) serving as the industry benchmark. Poly-(AAMA) and poly-(AAMA)-GO effectively inhibited sodium bentonite swelling, achieving anti-swelling ratios of 75% and 78%, respectively, compared to 58% for KCl. Characterization revealed that poly-(AAMA) and poly-(AAMA)-GO adhered to sodium bentonite particles through electrostatic interactions and hydrogen bonding, forming a thin film that enhanced hydrophobicity and limited water penetration into the interlayers. Poly-(AAMA)-GO exhibited excellent thermal stability up to 250 ℃, demonstrating its potential for high-temperature well fluid. Despite their structural differences, both compounds exhibited similar inhibitory effectiveness due to their shared chemical characteristics.

Core extraction and analysis are fundamental components of reservoir development. Core samples provide continuous vertical sections of the subsurface, enabling detailed visual assessment of depositional sequences and variations in reservoir characteristics. A thorough examination of these cores yields critical information significantly contributing to understanding and optimizing reservoir development. This data can include direct evidence of hydrocarbons' presence, quantity, spatial distribution, and production potential. The analysis of core samples is vital for understanding the pore system properties within potential reservoir intervals. Insights derived from core data improve the ability to predict reservoir performance and inform the selection of strategies to maximize cost-effective hydrocarbon recovery. This study employs a representative dataset from the Arabian Peninsula, specifically within the United Arab Emirates, using the Costa model as a supporting framework. The analysis incorporates the largest and smallest available sample sizes to ensure the final results remain within an acceptable range of variation. Each input parameter—core length, diameter, weight, fluid volumes, oil density, brine salinity (ppm), and brine density—is systematically varied to evaluate its impact on key reservoir properties, including saturation, porosity, permeability, and grain density. Additionally, the study investigates the effects of various combinations of incorrect input parameters to assess their influence on overall measurement accuracy. This methodology underpins a novel, AI-driven approach to uncertainty estimation in reservoir analysis. This approach enables more accurate assessment and informed decision-making in reserve estimation and reservoir management by quantifying the uncertainty in core-derived measurements.

The capture, storage, and utilization of carbon dioxide (CO₂) are pivotal in combating climate change and mitigating greenhouse gas (GHG) emissions. This review offers a comprehensive and critically focused analysis of post-capture CO₂ handling methods, including storage, adsorption, and utilization, with a particular emphasis on emerging and innovative technologies. Distinctively, this study highlights advanced CO₂ conversion pathways—such as photocatalytic and photoelectrochemical reduction, membrane-based separations, and mineralization—by analyzing their mechanisms, energy demands, techno-economic feasibility, and integration potential with renewable energy sources. The review further explores the role of natural systems, especially microalgae and porous materials, in CO₂ biofixation and adsorption under industrial conditions. Unlike conventional reviews, this work provides an in-depth comparative assessment of major capture methods (e.g., cryogenic separation, chemical absorption, and mixed-matrix membranes), emphasizing recent advancements and material innovations that enhance selectivity, thermal stability, and operational efficiency. By integrating perspectives from material science, process engineering, and environmental policy, this review presents a novel synthesis of current challenges and future opportunities in circular carbon management. These insights aim to support the development of scalable, cost-effective, and sustainable CO₂ mitigation strategies for industrial deployment.

Water saturation is one of the primary and influential parameters in evaluating the reservoir quality of hydrocarbon formations and is typically calculated by the Archie equation. This equation employs porosity, cementation factor, saturation exponent, tortuosity factor, formation water resistivity and true reservoir resistivity for water saturation calculation. It should be noted that uncertainty in these values can directly affect water saturation calculation, so the correct water saturation calculation in hydrocarbon reservoirs is essential, and any mistakes in its calculation leads to optimistic or pessimistic consideration of interval of interest. In this study, the Monte Carlo simulation method based on well-logging data were used to investigate the uncertainty of effective parameters in water saturation in the framework of Archie's equation. In this regard, the water saturation is calculated in three wells for the Sarvak reservoir in one of southwestern Iran's giant oilfields. The Monte Carlo simulation was implemented using MATLAB software for each parameter causing uncertainty in the water saturation model, and the distribution function is obtained for each parameter. Water saturation is as a distribution for each depth by investigating the uncertainty of each parameter affecting the Archie equation and is calculated in optimistic, probable and pessimistic cases. The results show that using well-logging data, the mentioned method can calculate the uncertainty of water saturation.

This study presents a fast method that can model the pressure depletion around hydraulically fractured wells accurately. The Gaussian Pressure-Transient (GPT) method has infinite resolution, because no discrete-grid computations are required − the method solves the pressure field using closed-form solutions. After extensive testing on field cases and calibration against other (independent) solutionmethods, it appeared that the pressure interference of multiple fractures represented by pressure transients − in the early phases of pressure advance − behaves according to the traditional superposition-method. However, when pressure interference advances, the traditional superposition method gives increasingly unrealistic and inaccurate results. Therefore, the traditional superposition method typically used for steady-state, needs adjustment when applied to transient systems, modeled here with Gaussian pressure-diffusion probability-density functions. The new solutions are valid for the advance of pressure changes both in the early and late stages of the well-life. A second new insight presented here is that the superposition of multiple fractures using Gaussian solutions (together with pressure-gradient computations and application of Darcy's) can accurately forecast the future production rate and cumulative production of the well over the economic well-life, accounting for the impact of fracture-spacing changes. The new method is validated by history-matching of our Gaussian forecasts using real well-data to constrain specific reservoir properties (hydraulic diffusivity) and completion parameters (fracture half-lengths), and then was benchmarked against independent simulations with CMG-IMEX.

The exploitation of low-flow oil wells following hydraulic fracturing is often hindered by residual proppant and proppant production during early production phases, which can infiltrate pumps and drastically shorten their service life. Managing proppant reliably and cost-effectively in these wells remains challenging due to the limited understanding of proppant transport mechanisms from the well bottom to the pump intake. This manuscript addresses this critical knowledge gap by presenting novel experimental insights into proppant transport in low-flow oil wells, marking the first elucidation of the underlying mechanism. Through innovative laboratory experiments simulating post-fracturing well conditions, we demonstrate that proppant invariably ascends to the pump intake, driven by a previously unrecognized process: droplets of immiscible hydrocarbon fluids capture and transport proppant particles upward. Comparative analyses using fluids of varying viscosities reveal, for the first time, that the viscosity of the immiscible phase has negligible impact on droplet transport capacity. Additionally, our studies establish that proppant concentration during transport is independent of well flow rate, challenging conventional assumptions. A key novel finding is the pivotal role of free gas bubbles in enhancing proppant transport probability, as they facilitate capture by oil and kerosene droplets; higher gas content correlates directly with elevated proppant concentrations in the flow. These experimental discoveries are validated by field trial data from wells with high free gas content under bottomhole conditions, where reduced pump operational times were observed, confirming the mechanism's real-world relevance. By unveiling this transport mechanism, the manuscript provides a foundational basis for developing targeted proppant management strategies, offering new pathways to enhance pump longevity and operational efficiency in low-flow oil wells.

Despite over $40 billion invested globally, Carbon Capture and Storage (CCS) captures and stores less than 0.1% of annual global CO₂ emissions, raising serious questions about its efficacy as a climate solution. This review addresses the critical disconnect between modelled expectations and empirical outcomes of CCS, offering a comprehensive, evidence-based reassessment of its technical, economic, and strategic performance. The analysis integrates data from peer-reviewed literature, international reports (IPCC, IEA, Carbon Tracker), and investigative journalism, evaluating both failed and superficially successful projects, including Petra Nova, Gorgon, Sleipner, and In Salah. These case studies expose recurring patterns of cost overruns, suboptimal capture rates, geological uncertainties, and public liability transfer. Projects hailed as "successful" often fall short when scrutinized against durability, scale, and emissions offset claims. While these findings raise valid concerns, this review does not categorically dismiss CCS. Instead, it emphasizes the need for strategic deployment in specific, hard-to-abate sectors where alternatives are limited such as cement production or legacy infrastructure retrofits. Beyond critique, the review explores proven alternatives: renewables, bio-based removals, mineralization; that offer higher scalability and permanence with fewer systemic risks. A predictive evaluation framework is introduced to match/compare capture and mitigation/avoidance technologies to emission contexts using criteria such as CO₂ concentration, energy demand (2.5-8 GJ/tCO₂), cost ($50-600/tCO₂), scalability, and permanence. Ultimately, the findings underscore that continued reliance on CCS is a high-cost gamble that risks delaying the deployment of truly effective climate solutions; yet, if strategized properly, 2026-2030 could still mark a decisive turning point in determining its role in the energy transition.

The growing demand for sustainable energy has intensified interest in biohydrogen production under extreme environmental conditions. This study presents a numerical simulation of hydrogen production in a high-temperature, high-pressure batch bioreactor system designed to mimic depleted hydrocarbon reservoir environments. The model captures microbial fermentation dynamics using dual substrates glucose and n-hexadecane under varying temperature (35-70 ℃), pressure (1-40 atm), salinity (0-50 g/L NaCl), and surfactant-enhanced availability (α = 1-12). Monod kinetics with product (Hydrogen) and salinity inhibition terms govern biomass growth and substrate consumption. Gas accumulation is modeled using Henry's and ideal gas laws, while thermal behavior is represented using lumped heat transfer and Arrhenius-based growth scaling. Initial conditions include 10 mmol/L glucose, 1 mmol/L hexadecane, and 0.1 g/L biomass in a 1.0 L batch system at 55 ℃. Over 24 h, the model predicts a maximum hydrogen accumulation of 4.34 L, with a final H₂ purity of 89.6% and biomass concentration of 1.20 g/L. Parametric simulations reveal that 55 ℃ yields the highest hydrogen output (3.31 mol/mol substrate), while increasing salinity and pressure reduce biomass and gas evolution. Surfactant addition significantly improves hydrocarbon utilization, reducing hexadecane from 1.0 to 0.12 mmol/L at α = 12, with up to 72.8% yield enhancement. Glucose drives early hydrogen evolution, while hexadecane contributes to sustained production under co-metabolic conditions. All equations were solved using Python with parametric looping and ODE integration. This simulation framework provides a robust tool for evaluating microbial-enhanced hydrogen recovery (MEHR) and optimizing reactor design conditions for biohydrogen generation in repurposed subsurface reservoirs.

Water saturation is one of the most critical yet often underappreciated petrophysical parameters in reservoir characterization. A wide range of petrophysical and reservoir engineering computations that lead to crucial field development decisions, including reserve estimation, waterflooding efficiency calculation, and capillary pressure deduction, rely on its accurate determination. This study demonstrates how machine learning techniques can forecast reservoirs' fluid saturation and pressure distribution. This study describes a deep learning-based proxy modeling technique for accurately predicting reservoir pressure distribution and fluid (oil, water, and gas) saturation during water flooding in single-layer heterogeneous reservoirs. This study used recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to build a proxy model. This work indicates that compared to simulation outcomes, computer-based machine learning algorithms can accurately predict fluid (oil, water, and gas) saturation and pressure distribution. The stated accuracy was evaluated numerically and graphically, and error analysis between various machine learning approaches and simulated results was utilized.

Accurate prediction of oil-based drilling fluid (OBD) behavior under high-temperature, high-pressure (HTHP) conditions in ultra-deep wells is essential for ensuring well control safety. However, research on the volumetric expansion of OBD in HTHP wellbores remains limited. This study develops a mathematical model to predict the expansion characteristics of OBD in HTHP wellbores, incorporating temperature- and pressure-dependent variations in rheological properties, thermophysical parameters, and density. The model is solved using discretization and the successive over-relaxation (SOR) method. Laboratory experiments were conducted to measure the rheology and density of OBD at different initial densities under varying temperature and pressure conditions. Binary mathematical models describing density and rheology as functions of temperature and pressure were established. The results indicate that the rheological properties of OBD exhibit consistent trends across different initial densities. Apparent viscosity (AV) and plastic viscosity (PV) decrease with increasing temperature and gradually stabilize, while both increase with rising pressure. The density of OBD decreases due to thermal expansion at elevated temperatures and increases due to thermal contraction at lower temperatures. Temperature exerts a greater effect on OBD density than pressure. Model validation using field data from an ultra-deep well confirms the accuracy of the proposed approach. During drilling and tripping operations, changes in mud pit volume occur due to temperature fluctuations; however, these changes are relatively slow compared to the rapid volume changes associated with wellbore influx. It is recommended to suspend the use of drilling fluid cooling devices before pulling out of hole (POOH) operations to minimize mud pit volume fluctuations. These findings provide theoretical guidance for accurate bottom-hole temperature prediction and improved drilling strategy optimization.

To meet the operational requirements of slip-type packers within TP140V casing in ultra-deep wells and enhance the anchoring performance and load-bearing capacity of the packer's anchoring mechanism during setting, this paper focuses on the study of tooth-embedded slips. The Abaqus finite element software was used to analyze the stress and contact pressure distribution patterns in key components such as the embedded teeth, casing, and slip base during the anchoring process of tooth-embedded slips. An orthogonal experimental method was employed to optimize the structural parameters of segmented tooth-embedded slips. Based on the results of the orthogonal experiments, the optimal combination of slip structural parameters was obtained: the front-end distance was 12 mm, the tooth installation spacing was 24 mm, the dip angle of the groove was 76.5°, the groove depth was 6.8 mm, and the slip wedge angle was 13°. After optimization, the maximum Von Mises stress in the slip base was reduced from 1080 MPa to 512.2 MPa, the embedding depth decreased from 0.2156 mm to 0.1338 mm, and the maximum contact pressure at the tooth tips dropped from 4247 MPa to 2965 MPa. Compared to the pre-optimization values, these reductions were 52.6%, 37.9%, and 30.2%, respectively. Moreover, the optimized casing bite depth and tooth-tip contact pressure exhibited a more uniform axial distribution. Additionally, physical prototypes of the optimized slips were manufactured and subjected to laboratory tests, which demonstrated that the axial bearing capacity of the slips could reach 450 kN, capable of withstanding axial loads in a 152.5-mm inner diameter casing under a downhole pressure environment of 100 MPa. The research findings in this paper provide a theoretical reference for optimizing the structural parameters of tooth-embedded slips within TP140V steel grade casing.

This study aims to develop a low-cost and environmentally friendly multifunctional thinning agent for water-based mud (WBM) from tea leaf extract (TLE). Matured tea leaves that are not used for tea production were collected, processed, and boiled in distilled water to release the extracts and prepare the liquid thinning agent. The resultant TLE was characterized using Fourier Transform Infrared spectroscopy (FTIR). Rheological properties were measured at two temperature conditions (35 ℃ and 70 ℃) with varying concentrations of TLE, and the findings were compared with the widely used thinning agent potassium lignite (PL) under all test conditions. In addition, the density, pH, filtration control, and lubricating properties were evaluated under ambient conditions. The findings revealed that TLE, under all test conditions, exhibited better thinning performance than potassium lignite. At the temperature of 35 ℃, when used at 1% concentration, TLE reduced apparent viscosity, plastic viscosity, yield point, initial and final gel strength by 23.94%, 21.57%, 27%, 76.99%, and 24.44%, respectively, compared to 12.89%, 16.34%, 8.44%, 39.82%, and 7.94% using the same concentration of potassium lignite. Compared to the ambient condition, the effectiveness of TLE in controlling rheology was found to be more pronounced at higher temperatures. Moreover, TLE at different concentrations significantly improved the filtration and lubricating properties of the mud. The addition of TLE reduced the mud cake thickness by a maximum of 44.44% and the coefficient of friction by a maximum of 40.7%. The comprehensive investigation and comparison with potassium lignite suggest that TLE can be effectively used as a multifunctional, non-toxic, and low-cost thinning agent for water-based drilling fluids.

Geopolymers are promising alternatives to Portland cement due to their lower carbon footprint, superior mechanical strength, durability, and reduced shrinkage. However, inconsistent data and significant variations in experimental results highlight the uncertainty surrounding the reaction mechanisms of clay-based geopolymers (CBGs) in high-temperature oil wells. This review provides critical insights into these mechanisms and summarizes CBG synthesis methods. It explores the reaction processes of CBGs under high-temperature oil well conditions and analyzes the variables influencing these mechanisms. This review also examines the application of CBGs in oil wells, focusing on the challenges and potential solutions. Laboratory studies show that Portland cement degrades at temperatures above 300 ℉ (150 ℃), while CBGs maintain their integrity and strength at temperatures up to 1500 ℉ (815 ℃). This makes them particularly suitable for high-temperature geothermal wells and high-pressure, hightemperature oil well cementing. The dominant reaction mechanisms of CBGs include chemical reactivity, dissolution, gelation, polymerization, and growth. These findings highlight the potential of CBGs in addressing the challenges of high-temperature oil wells, thereby paving the way for further research in this area.

To maintain the integrity of internal floating roof tanks, curb environmental pollution and guarantee the safety of tank farms, it is important to determine the oil flow features that impact the floating roof during the oil receiving process, especially when the floating roof falls to the bottom of the tank. In this study, the finite volume, volume of fluid (VOF), and realizable k-ε turbulence models and the pressure implicit with splitting of operators (PISO) algorithm were adopted to establish a three-dimensional physical model of an internal floating roof tank and simulate the oil receiving process. A pressure test platform was then set up to validate the model's viability. The flow field characteristics and influencing properties of the impact of the liquid against the floating roof under different oil inlet velocities, initial liquid levels, medium densities, and tank diameters were analyzed and compared. The results showed that increasing the oil inlet velocity and medium density led to the maximum pressure and the impact force at different moments of the liquid against the floating roof also increasing. An increase in the oil inlet velocity, led to greater turbulence on the opposite the inlet, and higher pressure at the edge of the floating roof in the upper area opposite the inlet. At an oil inlet velocity of 4.0 m/s, the maximum pressure was 135.5 kPa and the maximum impact force peak was 13.967 MN. The initial liquid level increase had little influence on the liquid's impact on the floating roof, and the high pressure zone was at the edge of the floating roof opposite the inlet. When the initial level was 0.5 m, the maximum pressure was 135.81 kPa and the maximum impact force peak was 14.021 MN. As the density of the medium increased, the greater the turbulence on the inlet relative side and the higher pressure at the edge of the floating roof in the upper area of the inlet relative side. When diesel was the medium, the maximum pressure was 154.9 kPa and the maximum impact force peak was 15.999 MN. As the diameter of the tank increased, the maximum pressure of the floating roof impacted by the liquid reduced, but the impact force increased. The slower the turbulence on the inlet relative side, the greater the maximum pressure zone of the floating roof. When the tank volume was 1000 m³, the maximum pressure was 135.5 kPa. When the tank volume was 10,000 m³, the maximum impact force peak was 81.978 MN. Considering the above, a low velocity, a low-density light oil, and an internal floating roof tank with a small diameter are recommended for engineering operations that involve receiving oil. Further increased safety precautions at the edge of the floating roof opposite the inlet are needed. In sum, the findings of the study offer theoretical justifications and a technical reference for optimizing the structural design of floating roofs and the oil receiving and delivering process.