ExxonMobil (United States)

Abstract Cathodic protection (CP) systems are vital to mitigating external corrosion on buried pipelines. However, a critical gap remains in the frequency and coverage of pipe-to-soil (P/S) potential measurements, which are central to compliance and CP performance assessment. While remote monitoring units (RMUs) have successfully automated rectifier and bond monitoring, the industry has yet to fully embrace year-round, remote P/S potential data acquisition due to concerns over reference electrode longevity and accuracy. This project focuses on a North American pipeline operator's experience in evaluating the long-term performance of permanent reference electrodes installed alongside RMUs, which is a subset of a group of operators that have deployed similar systems. Each site includes seven or eight reference electrodes with various chemistries and manufacturers to evaluate variability and drift. The RMU’s capabilities enable high-resolution monitoring, and ON/OFF cycling to approximate interrupted readings. The study also integrates traditional manual surveys, soil sampling and analysis, and electrode placement benchmarking to calibrate remote readings. Data is aggregated through the Mobiltex CorView platform to allow anonymized analysis and comparison across environments, installation practices, and product types. The intent is that this methodology and findings contribute to broader regulatory acceptance of permanent reference electrodes for compliance-grade monitoring. Preliminary insights suggest measurable trends in stability and accuracy among electrode types and provide early validation for their use in continuous monitoring frameworks. This paper will share lessons learned through the installation process and data trends observed over the first two years of operation at one location, establishing a platform for future follow-on analyses, and opening the discussion for recommendations across the industry.

Selective oxidation of 1,3-diisopropylbenzene with molecular oxygen was performed under a heterogeneous catalyst system of manganese molecular sieves (OMS-2) and N-hydroxyphthalimide (NHPI). The catalyst system showed high conversion (>99%) and selectivity (86%) toward the target diol product under mild conditions (40 °C, 560 psi). The recyclability test demonstrated that the catalyst system retained 80% selectivity after three consecutive reactions, highlighting its potential as a practical heterogeneous approach for the reaction. Discussion on the reaction mechanism suggested that a lower reaction temperature and abundant active sites are beneficial to suppress the β-scission reaction, thus improving the selectivity and yield of the target product.

Random ethylene-rich ethylene–propylene copolymers generally crystallize into structures, which can incorporate some methyl-branches, resulting in a lattice that is expanded compared to that of polyethylene. However, such copolymers have finite statistical probability of containing long unbranched polyethylene sequences. Using light scattering, it was found that when cooling from solution, this trace fraction of unbranched sequences crystallizes into a polyethylene habit at higher temperature, distinct from the main crystallization event of the branch-incorporated structure, which occurs at lower temperature. The propylene-content dependence of onset temperature is consistent with this mechanism. The minority orthorhombic crystal phase is directly observed using X-ray scattering.

SummaryDeep decarbonization of the US power system would require rapid deployment of variable renewable energy (VRE) resources, which are projected to provide a substantial share of electricity generation at the time of net-zero emissions. However, the exact share of generation met by VRE and the roles of other technologies in supplying key electricity services—energy and firm capacity—remain uncertain. This study employs a detailed model of the US power sector to decompose the provision and value of electricity services, including negative emissions, by technology across a range of deep decarbonization scenarios. Results indicate that while technology deployment and the share of services provided by each technology vary significantly depending on future technological and market conditions, the value composition and future roles of individual technologies remain consistent. These findings offer guidance for research and development priorities and provide insights to inform electricity policy and planning.

International audience

Abstract Production from multi-bench unconventional development is time-consuming to model in physics-based simulators, requiring multiple runs. Each simulation takes minutes to hours, even with enhanced cloud computing power. This limits history matching to a single deterministic solution, as multi-scenario algorithms need significantly more simulations. An AI-based surrogate model expedites forecasting, enabling efficient multi-scenario algorithms. The AI surrogate relies on a large dataset of high-fidelity physics-based simulations created using modern high-performance computing (HPC) infrastructure. Simulations were based on Latin-hypercube sampling of over 100 parameters, covering geology, reservoir properties, well placements, and completion designs. Results were processed on HPC to extract features and train a surrogate model using advanced AI techniques. The model was iteratively trained to meet user-defined error metrics. The final surrogate was integrated with an automated history matching (AHM) package using a Gauss-Newton trust region optimizer to obtain multiple scenarios that align with field production. Developing a surrogate model with the desired fidelity was a challenging task. Reservoir models include several highly non-linear input-output relationships from various physical processes. Training a surrogate with an acceptable level of fidelity requires both a massive dataset and specialized AI techniques to enforce physical relationships in deep learning algorithms. The latter was critical to obtain correct physical trends from the surrogate model predictions. The final surrogate model's production forecasts accurately match the trends and magnitudes of the computationally expensive, high-fidelity numerical simulations across several real examples of multi-well and multi-bench developments. The surrogate model integration within the AHM package was straightforward and enabled the efficient determination of multiple scenarios in about one hour for a typical unconventional case. This expedites the determination of plausible scenarios, empowering end-users to better optimize the development plan. A fast reservoir surrogate model significantly reduces the cycle time for using physics-based models and workflows, capturing subsurface uncertainty more holistically. The surrogate model workflow benefits from standard features of machine learning systems, including interpretability and confidence scores, providing the user with richer information before making decisions. In particular, the work demonstrates that determining multiple reservoir scenarios matching production history is practically achievable by leveraging cutting-edge AI technologies.