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.