Abstract Cement plugs are widely accepted as the primary barrier for plug and abandonment (P&A) operations. However, post placement validation is often limited to tagging and short duration pressure tests conducted immediately after plug setting, providing limited insight into long-term integrity. Cement plug failures driven by in-situ wellbore changes and pore-pressure evolution remain insufficiently evaluated, and existing industry models largely neglect poromechanical coupling within the plug and its impact on abandonment failure. This work presents a novel poromechanical analysis framework that identifies the drivers responsible for cement plug failure resulting in significant financial impact and weeks of downtime for an offshore operator in Malaysia. Furthermore, the analysis method provides a basis for tailoring cement properties and placement strategies to maintain long-term integrity. The evaluated abandonment plug spanned both a cased-hole and an open-hole sections of the well and was successfully subjected to pressure and inflow tests of approximately 1,800 psi and 5,000 psi, respectively. However, a subsequent reduction in equivalent mud weight (EMW) to 8.1 ppg triggered an inflow event, forcing well shut-in operations. Remedial drillout operation revealed alternating intervals with negligible weight on bit (WOB) and hard cement, indicating intermittent structural integrity of the plug. Conventional three-dimensional near-wellbore stress analysis assuming the cement as a non-porous solid failed to explain the observed failure mode. As a result, our research efforts highlighted the need for a poromechanical framework capable of modeling a coupled near-wellbore system comprising an abandonment plug. The proposed method can model the effects of thermal, structural and hydraulic loads and predicts the evolution of stresses and pore pressure within both the abandonment plug and surrounding formations. Conventional three-dimensional near-wellbore stress analysis assuming the cement as a non-porous solid failed to explain the observed failure mode. The proposed method successfully identified that shrinkage coupled with pore pressure induced tension are the reasons for the observed failure of a conventional cement plug during the mud weight reduction, while pressure and inflow tests alone were insufficient to induce such a failure, consistent with well observations. The framework further predicted low failure risk for a remedial mechanically enhanced cement system, accurately reproducing the successful re-abandonment outcome. The proposed method enables comprehensive assessment of long-term plug integrity and provides a quantitative basis for tailoring cement poromechanical properties. Additionally, the framework allows evaluation of fluid-influx risk through porous plug material, extending beyond conventional integrity assessments.
Background: Mixed matrix membranes (MMMs) containing three components consisting of a polymeric continuous phase, a solid inorganic material, and an ionic liquid are widely explored for CO2 removal from natural gas to increase energy content, reduce corrosion, and enable safer utilization. However, most of the previous studies have relied on physically blended or impregnated ionic liquids (ILs), which suffer from leaching and membrane instability, ultimately limiting their separation performance. Moreover, experimental methods alone cannot fully explain gas transport mechanisms or interactions between polymers, fillers, and gases with sorption sites. Methods: This work employs a grafting strategy to covalently support 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([EMIM][Tf2N]) onto silica surface (IL-Si), ensuring long-term stability, uniform dispersion, improved compatibility between polymer and filler, and enhanced gas separation performance. Hybrid membranes with filler contents ranging from 5 to 20 wt.% were experimentally fabricated and analysed from an atomistic perspective using molecular dynamics (MD) simulations. Significant Findings: The IL-functionalized filler enhanced interfacial adhesion, as evidenced by increased thermal stability with delayed degradation and improved glass transition temperature (T-g) from 181.5 to 189.46 degrees C, reflecting stronger polymer-filler interactions. At 10 wt.% IL-Si, the membrane achieved a CO2 permeability of 25 Barrer and CO2/CH4 selectivity of 37, representing 246% and 208% improvements over neat polysulfone (PSF). Compared to non-modified silica/PSF, the permeability and selectivity improved by 140% and 40%, respectively. MD simulations, with <10% deviation, confirmed [Tf2N](-) anions enhance CO2 sorption while [EMIM](+) cations strengthen filler dispersion and compatibility. Based on this, future work needs to focus on testing functionalized ILs, scaling up fabrication, assessing long-term stability under harsh conditions, and expanding membrane studies to other relevant gas pairs.
As the industry accelerates carbon capture, use, and storage initiatives, modeling innovations for CO2 injection and enhanced oil recovery (EOR) have become critical for optimizing recovery and ensuring secure storage. Recent studies highlight a shift toward data-driven and hybrid approaches that combine computational efficiency with operational practicality. Paper SPE 227168 introduces a deep-learning framework using the temporal fusion transformer (TFT) to optimize sequestration efficiency and oil recovery. Unlike conventional machine learning, TFT captures temporal dependencies and uncertainty, enabling dynamic optimization under varying reservoir conditions. This approach supports real-time decision-making while reducing computational demands compared with full-physics simulations. Meanwhile, Paper SPE 221978 focuses on fast predictive models for mature oil fields, using surrogate techniques such as artificial neural networks and autoregressive models. These proxies significantly accelerate scenario evaluation, making them ideal for screening and feasibility studies where time and resources are limited. The inclusion of uncertainty quantification enhances reliability for planning under variable conditions. A third reviewed paper, IPTC 25000, addresses well-network design for CO2 EOR and storage through an integrated modeling approach that couples reservoir simulation with network-optimization algorithms. Applied at field scale, this method improved sweep efficiency and reduced CO2-breakthrough risk, demonstrating scalability for heterogeneous reservoirs. These advances mark a paradigm shift toward intelligent, integrated modeling frameworks in EOR. Future efforts should aim to unify deep learning, surrogate modeling, and network optimization into holistic platforms, enabling end-to-end optimization across reservoir and infrastructure scales. Such innovations will be pivotal in achieving dual objectives: maximizing oil recovery and ensuring secure CO2 storage in the energy-transition era. SPE 227168 - Deep-Learning Technique Optimizes Sequestration, Oil Production in CCUS Projects by Ahmed Wagia-Alla, SPE, Mohamed Alghazal, and Turki Alzahrani, SPE, Saudi Aramco. SPE 221978 - Fast Predictive Models Developed for CO2 EOR and Storage in Mature Oil Fields by Yessica Peralta, Ajay Ganesh, and Gonzalo Zambrano, SPE, University of Alberta, et al. IPTC 25000 - Integrated Well-Network-Design Mode Developed for CO2 EOR and Storage by Zangyuan Wu, PetroChina, CNPC, and China University of Petroleum; Yongliang Tang, PetroChina and CNPC; and Liming Lian, CNPC, et al. SPE 221850 - The First Application of Quantum Computing Algorithm in Streamline-Based Simulation of Waterflooding Reservoirs by Xiang Rao, Yangtze University SPE 227695 - CO2 Flooding Optimization Using Artificial Neural Networks: Enhancing Oil Recovery and Carbon Sequestration by N.A. Almakki, University of Khartoum, et al. SPE 224577 - Leveraging Machine Learning To Model Hydrocarbon/CO2 Solubility Behavior by Seyed Mehdi Alizadeh, Australian University, et al.
Dents are among the most encountered types of deformation of buried long-distance pipelines. This paper aims to determine the presence of turbulence flow within the dented spool using frequency domain analysis from acoustic emission response. This is a consideration based on the difficulties in extracting prominent information from acoustic emission signal during an existing dent inspection. The acoustic emission signal responses were obtained during the flow loop test from healthy, 5
