King Abdullah Petroleum Studies and Research Center

Hyperspectral analysis of carbonate rocks provides a novel method to recognize not only calcite/dolomite alterations, but also to differentiate dolomite fabrics. Coupled with digital outcrop models, hyperspectral data provide an integrated representation of the geometric and mineralogical characteristics of exposed dolomite geobodies at sub-seismic resolution and over large (seismic-scale) extent. This facilitates the continuous, unbiased, and data-driven assessment of the spatial distribution of dolomites, dolomite types and properties. Here we integrate hyperspectral attributes, geochemical data, fracture analysis, tectonic, and thermal histories to constrain the process and timing of dolomitization and the compositional and textural heterogeneity at cm-scale. Our results suggest that the km-scale strata-bound dolomitized layers of the Arab-D member formed in an overall regressive system tract. near the surface (T ~ 30°C) by refluxing of slightly evaporated seawater (-1.0 to 0‰ SMOW). With undolomitized shallow transgressive mudstone/wackestone layers forming baffles restricting downward fluid flow, the dolomitization process apparently was repetitive and linked to high frequency cycles with preferential dolomitization of cycle-top grainstone facies. Multiple reflux events during high-frequency cycle deposition led to the alternating dolomite/calcite layering. Thus, a classical one-time-dolomitize-all end-of-sequence reflux system is not indicated. The early-formed metastable dolomites were then recrystallized during burial and finally overprinted by a hot (80°C or more) deep-seated fluid with a composition of up to 6.5‰ SMOW. This fluid was channeled by a NW-SE oriented regional fracture trend, which originated from a Late Cretaceous plate-wide structural event related to the Alpine I tectonic deformation. As the dolomite fabric was altered, porosity and permeability became enhanced. The temperatures derived from clumped isotope analysis, thermal history, and the Alpine I related fracture conduits consistently suggest a latest Cretaceous origin for the final burial dolomite maturation and textural overprinting.

The Paris Agreement's goal of limiting global warming to well below 2 °C, ideally 1.5 °C, places significant emphasis on Carbon Dioxide Removal (CDR) technologies. However, the global landscape for CDR deployment remains uneven, with significant disparities in technological capacity, economic readiness, and regional ambition. This study investigates how limited access to CDR technologies could exacerbate global economic inequality under a 1.5 °C pathway. Using the Global Change Analysis Model (GCAM v6.0), six scenarios ranging from unrestricted CDR availability to constrained deployment are evaluated. Our findings reveal that constrained CDR availability significantly increases median global carbon prices, rising from $588/tCO 2 under the full CDR portfolio scenario to $937/tCO 2 by 2055 in the most restrictive scenario. By 2100, some regions face prices exceeding $3000/tCO 2 , underscoring stark regional inequalities. These elevated carbon prices could deepen economic disparities—particularly in developing nations and fossil fuel-dependent economies. Furthermore, constrained CDR availability could also amplify inequalities in energy and food security, disproportionately affecting poorer regions. The study underscores the need for equitable CDR access to support a just global transition to a low-carbon future, offering valuable insights for policymakers designing more equitable climate strategies.

Study design:A retrospective comparative study.Background:The length of hospital stay and the setting to which patients are discharged after surgery can vary, impacting both healthcare resource utilization and the patient’s recovery process. Identifying the factors that affect these outcomes is essential for enhancing patient care and efficiently managing resources in elective spinal stenosis surgery.Objectives:This study aims to identify the predictors associated with extended hospital length of stay and 90-day reoperation in elective spinal stenosis surgeries, to optimize patients’ postoperative outcomes.Methods:We conducted a retrospective study at King Abdulaziz Medical City, Riyadh, Saudi Arabia, from January 2016 to January 2024. Statistical analyses, including multivariate analyses and neural networks, were performed using SPSS.Results:A total of 389 patients were included, with a median length of stay of 6 days (interquartile range [IQR] = 3), with 113 patients (≥75th percentile) in the extended hospital length of stay group (n = 113; 29.3%). Age ≥60 years (P = 0.021), reoperation within 90 days (P = 0.027), body mass index (BMI) ≥35 (P = 0.001), diabetes (P = 0.004), and American Society of Anesthesiologists (ASA) classification (P = 0.019) were found to be significant predictors of extended hospital stay. In the multivariate analysis, BMI ≥35 (P = 0.002) and diabetes (P = 0.030) remained significant predictors of extended hospital length of stay. ASA classification (P = 0.001) and prolonged hospital stay (P = 0.027) were significant predictors of 90-day reoperation risk in the bivariate analysis; however, these associations were not retained in the multivariate analysis.Conclusion:Our study highlights the need for preoperative optimization of comorbidities, BMI, and overall health to improve postoperative outcomes and reduce costs in elective spinal stenosis surgeries.

Summary Positive displacement motor (PDM) bottomhole assemblies (BHAs) are widely used in horizontal drilling due to their low cost and high rate of penetration (ROP) under rotary conditions. However, their limited-trajectory control ability often requires switching to sliding drilling for sharp corrections, which significantly reduces efficiency. Moreover, the quantitative relationship between build up rate (BUR) and controllable parameters such as revolutions per minute (RPM) and mud flow rate (MFR) remains unclear under rotary conditions. To address these issues, we propose a machine learning (ML)–based model predictive control (MPC) framework that regulates trajectory solely through real-time optimization of drilling parameters. A hybrid prediction model, integrating mechanical principles and ML, is developed to map weight on bit (WOB), RPM, and MFR to BUR, with a Lipschitz continuity constraint enhancing stability and robustness. The MPC objective function is improved by incorporating adaptive weighting, which accelerates convergence, reduces overshoot, and improves control consistency. The proposed method is evaluated across five simulation scenarios, which are analysis of control behavior, objective function enhancement, piecewise setpoint tracking, comparison with classical controllers, and robustness testing. The approach is validated using field data from a horizontal well in the Junggar Basin, northwest China. The results show that the ML-MPC strategy achieves accurate setpoint tracking under various conditions, maintains strong robustness to input noise, initialization variations, and measurement disturbances (with steady-state error < 0.2°/30 m), and meets real-time computation requirements (<20 seconds per control step). Analysis of the optimized control parameters provides data-driven insights into how WOB, RPM, and MFR influence BUR. Notably, a control inertia effect is observed: When the control objective aligns with the existing trend of variation, the system responds more efficiently; when it opposes the trend, the response becomes slower and more prone to overshoot. Overall, this work presents a practical framework for real-time trajectory control and contributes to the automation and intelligent drilling technologies in the oil and gas industry.

A female patient, 38 years old, G2+P0+A1, at 20 weeks plus six days of gestation, was admitted to the emergency with a complaint of watery vaginal discharge for the last 72 hours. There was a hind-water rupture of membranes, and the AmniSure test was positive. She had an OPD visit about a month back, and during that visit, her urine was sent for culture and sensitivity report, which showed E. coli, but she had not taken treatment as she did not come back. Her emergency room baseline labs were in the normal range, and high vaginal swab (HVS) and urine culture and sensitivity results returned negative later. She was asymptomatic for chorioamnionitis. A prophylactic antibiotic was started. Two days later, she developed a fever. She was counseled about septicemia and the risk of maternal death, but she refused. A few hours later, she expelled the fetus and went into hypotension. ERPC (Evacuation of retained products of conception) was done, and one unit of packed red cells was transfused. She was shifted to the ICU. Broad-spectrum IV antibiotics were started. All inflammatory markers were high during her illness. Chest X-ray showed bilateral pleural effusion. She stayed in the ICU for three days and was then discharged. The objective is to discuss a case of septicemia due to preterm premature rupture of membranes (PPROM) and chorioamnionitis, probably following a urinary tract infection (UTI) caused by E. coli, and its management.

A key pathway to achieve net-zero greenhouse gas emissions in the light-duty vehicle (LDV) sector is the complete transition to electric vehicles (EV). However, the EV transition is facing new economic, technological, and infrastructure challenges, leading some EV owners to revert to internal combustion engine vehicles (ICEVs). In this direction, we assess emerging barriers in fully electrifying the LDV sector. We utilize interpretive structural modeling, and cross-impact matrix multiplication on inputs from an expert survey to evaluate the hierarchies between identified barriers. The findings suggest that charging inconvenience, uncertainties about EV upfront prices, operational cost savings, and shifts in policy away from fully banning sales of new ICEVs are among the top ‘linkage’ barriers. They directly impact other ‘dependent’ barriers, such as the EV ownership discontinuance. Experts also rated supply constraints in the critical metals market and the potential for an oligopoly in the international battery materials market as significant ‘independent’ barriers that influence both the ‘linkage’ and ‘dependent’ barriers. Building on these findings, we provide key takeaways for policy and industry discussions by examining how the most impactful ‘linkage’ barriers to full electrification in the LDV sector are playing out across different regions and how various stakeholders are responding. • Evaluated emerging barriers to full electrification of the light-duty vehicle sector. • Identified barrier hierarchies through multi-criteria decision-making analysis. • Electric vehicle discontinuance identified as a key dependent barrier to adoption. • Battery material supply constraints and market oligopoly are independent barriers. • Shift from banning new combustion engine vehicle sales is a linkage barrier.