Properties of rock, such as effective porosity, permeability and pore size distribution (PSD), are generally referred to as petrophysical properties. These properties are among the most significant for reservoir evaluation. Acid stimulation treatments are usually used in sandstones to mitigate the impact of formation damage, with the aim of restoring or enhancing the natural matrix permeability and consequently boosting the well productivity. Hydrochloric acid (HCl) is commonly used in the preflush stage to remove calcium and other metal ions, preventing the development of calcium fluoride (CaF₂) and other silicate precipitates that could block the pore throats, while an acid mixture (HF–HCl combination) is usually preferred as the main stimulation fluid for the removal of quartz and remaining metal ions. However, sometimes the application of these acids can lead to other problems, including fast reactions, corrosion of pipes, environmental hazards, precipitation reactions and formation damage due to the incompatibility of HCl with clay minerals, so chelating agents have been proposed as an alternative for matrix stimulation fluids. In this study, three different chelating agents, ethylenediaminetetraacetic acid (EDTA), N-(2-hydroxyethyl) ethylenediamine-N,N′,N′-triacetic acid (HEDTA) and N-acetyl-l-glutamic acid (GLDA), have been used to stimulate Berea sandstone, Colton tight sandstone and Guelph dolomite samples. Core flood experiments were conducted on 1.5 × 3 (in²) core plugs, at a temperature below 180 °F. A slow injection rate of (1–0.5 cc/min) was chosen for the treatment fluid, promoting the dissolution of ions by increasing the contact time between the fluid and the rock. Furthermore, nuclear magnetic resonance, wettability and micro-computed tomography (CT scan) analyses were employed to evaluate the effect of the acid treatment on formation properties such as porosity, PSD, pore topology, wettability and pore structure. After exposing the samples to HEDTA, large wormholes were detected in their pore network, demonstrating that HEDTA has the highest potential to create new pore spaces when compared to GLDA and EDTA when reacted with both types of samples.

Hydraulic fracturing, natural fracture reactivation and resulting induced microseismicity are interconnected phenomena involved in shale gas exploitation. Due to their multi-physics and their complexity, deep understanding of these phenomena as well as their mutual interaction require the adoption of coupled mechanical and fluid flow approaches. Modeling these systems is a challenging procedure as the involved processes take place on different scales of space and also require adequate multidisciplinary knowledge. An extensive literature review is presented here to provide knowledge on the modeling approaches adopted for these coupled problems. The review is intended as a guide to select effective modeling approaches for problems of different complexity.

Summary: A geomechanical and fluid-flow coupled model was developed to simulate natural-fracture-network reactivation during hydraulic-fracturing treatments in shale gas reservoirs. The fractures were modelled using the continuum approach in a commercial finite-difference code, labeled the “softening ubiquitous joints” model, with randomly distributed strength parameters to describe heterogeneity along the fracture plane. The models allow for intersecting fractures to represent realistic scenarios. The permeability values in the fractures are dynamically updated during the simulations together with the reversible tensile opening because of elastic response and irreversible shear opening caused by plastic deformations. The reactivation of the fracture network resulted in high permeability along these fracture planes. The developed model can predict microseismic events caused by slip on the fracture planes. The magnitude levels of these microseismic events are comparable with the levels observed in events monitored by use of geophone arrays during hydraulic-fracturing treatments for different shale gas reservoirs.

Predicting post-acidizing porosity and permeability in sandstone reservoirs remains a fundamental challenge due to complex, nonlinear acid–rock interactions. This study presents a physics-augmented machine learning and deep learning (ML/DL) framework that integrates 24 laboratory core-flooding measurements from the Lower Indus Basin, Pakistan, with 500 synthetic training samples generated by the Panga–Lemerle–Balakotaiah (PLB) two-scale continuum simulator. Eight ML/DL architectures are benchmarked on the augmented test set (n = 75): Support Vector Regression (SVR) achieves the highest porosity R $$^{2}$$ = 0.927 and Random Forest the highest permeability R $$^{2}$$ = 0.907. A systematic domain-gap analysis reveals that naive median imputation of the unknown HCl concentration causes catastrophic external validation failure (porosity R $$^{2}$$ =−2.34). Five targeted remediation strategies are evaluated. Strategy S4—Optuna-tuned LightGBM with Leave-One-Out cross-validated joint augmented+experimental training—raises external R $$^{2}$$ to 0.918 (porosity) and 0.841 (permeability), representing improvements of +3.25 and +2.18 R $$^{2}$$ units over baseline. SHAP interpretability analysis identifies HCl concentration, initial permeability, and the Damköhler number as collectively governing >75% of prediction variance. The framework provides a physically interpretable, calibration-ready forecasting tool for pre-treatment petrophysical prediction aligned with sustainable low-concentration acidizing design in accordance with UN SDG 7 and SDG 12.

Oil and gas development alters habitat in some of the world’s most biodiverse regions, and the ecological mechanisms behind these impacts are now well described. Recent syntheses argue that the binding constraint on better outcomes is no longer ecological knowledge but the translation of that knowledge into enforceable and durable protection. This claim has rarely been tested with data. We combine a critical review of the literature through mid-2026 with four original analyses of openly published datasets. First, using 1,169 listing and recovery documents for 608 Canadian species at risk (CAN-SAR database), we find that oil and gas drilling is identified as a threat for 18.9% of species (95% CI 15.7–22.5%), rising to 41.5% for mammals and 33.3% for birds. Recognition depends strongly on assessment protocol: reports that used the standardized IUCN-CMP threats calculator were 4.6 times more likely to record the threat (95% CI 2.3–9.1), which fully explains its apparent rise over time. The severity of this threat was scored as unknown less often (13.0%) than for other threats (17.8%). Second, only 5 of 313 recovery strategies (1.6%) met the statutory deadline, and strategies for oil-and-gas-exposed species were more than twice as likely to leave critical habitat unidentified (35.5% versus 18.2%; odds ratio 2.48, 95% CI 1.34–4.56), a difference explained largely by the wide-ranging vertebrates that dominate this group. Third, propagating the national boreal caribou demography model shows that the expected growth rate falls below replacement at about 8.5% anthropogenic disturbance when fire is negligible, and that the probability of a stable or growing population at the policy benchmark of 35% disturbance is about 0.14. Fourth, the U.S. protections governing three indicator species changed direction at least once between 2023 and 2026, with federal listings lasting 2.1–2.4 years. Together these results show that knowledge of oil and gas threats is adequate for action, and that losses accumulate in the steps that follow: planning, threshold setting, and the durability of protection. We close with recommendations aimed at each of these steps.