Chevron (Netherlands)

Abstract Quantitative risk assessments (QRAs) have helped assure the oil & gas industry and stakeholders (neighbors and regulators) that our facilities can be operated safely and have identified areas where the risk of major accident events can be better managed. QRA tools available in the industry necessitate a premium of cost and time in the quest for required quality. This creates a challenge for projects and operating assets in making risk‐informed decisions in a timely manner. Chevron, in partnership with a commercial software vendor, optimized both onshore and offshore QRA applications, reducing cycle time and cost by over 70%. These applications support agile project development, provide the data needed to make day‐to‐day operations decisions, analyze various potential risk reduction strategies in real time, and reduce reliance on third parties. Chevron also developed in‐house Power BI tools to interrogate QRA results such that the top risk contributors can instantly be traced back all the way down to a single release event. The published QRA results in Power BI can be accessed and interrogated by any user with no background or skillset to operate a QRA tool. This enables the customers to easily understand the QRA results and its top risk contributors.

Access to clean and safe water remains a critical global challenge, particularly for underserved and remote communities lacking centralized infrastructure. Decentralized IoT-enabled filtration systems offer an innovative solution by combining advanced water treatment technologies with the real-time monitoring capabilities of the Internet of Things (IoT). These systems ensure water quality, optimize resource use, and provide sustainable water access at the point of need. This paper explores the technological advances driving these systems, their impact on water access, and the economic and social benefits they deliver. It also addresses key challenges, including economic barriers, logistical constraints, and cybersecurity concerns, while offering actionable recommendations to enhance adoption, affordability, and long-term sustainability. Decentralized IoT-enabled filtration systems represent a scalable and adaptive approach to addressing the global water crisis, with significant potential to transform water access for vulnerable populations.

Gas shales are fine-grained, organic-rich sedimentary geomaterials with ultra-low permeability requiring hydraulic stimulation for gas extraction. Characterizing their water retention behavior is critical for predicting hydromechanical behavior and fluid flow, yet it remains challenging due to their complex pore network and mixed wettability. This study investigates the water retention behavior of a gas shale through comprehensive characterization and laboratory tests, where water content and strains both perpendicular and parallel to the bedding plane were measured over two wetting-drying cycles. The results suggest that the coexistence of hydrophilic clay minerals and hydrophobic organic matter limits water access to parts of the pore and microcrack network, resulting in incomplete saturation even at a null suction. Three water retention models were modified by introducing an additional parameter to account for this wettability effect, among which the van Genuchten model provided the best overall fit. The fitted curves revealed a surprisingly low air entry value, underscoring the role of percolated hydrophobic networks in facilitating gas flow. The swelling strains indicated irreversible opening of bedding-parallel microcracks. The shrinkage strains were reversible, better representing the elastic hydromechanical anisotropy. Comparisons with other shales revealed that shrinkage anisotropy correlates more strongly with burial depth than with clay fraction, suggesting that compaction and diagenesis may play a more critical role than the amount of clay. Wettability may reduce the impact of pores and microcracks on shrinkage anisotropy. These findings emphasize the need for advanced constitutive models for gas shales that incorporate the observed wettability-controlled water saturation and hydromechanical anisotropy. • Wettability controls gas shale water retention by limiting access to organic pores. • A modified van Genuchten model is proposed for water retention curves of gas shales. • Gas shales show markedly higher hydromechanical anisotropy than claystone shales. • Compaction and diagenesis may enhance shrinkage anisotropy more than clay fraction.

<div class="section abstract"><div class="htmlview paragraph">Renewable Gasoline Blends can deliver greater than 50% reduction in vehicle Well-to-Wheel (WtW) greenhouse gas emissions when used in current vehicles. When paired with a state-of-the-art hybrid vehicle (relative to an average vehicle on U.S. roads today), a greater than 70% reduction in WtW emissions can be achieved. Importantly, Renewable Gasoline Blends can be formulated to align with existing market standards for gasoline, thereby functioning as a drop-in fuel solution compatible with all gasoline-powered vehicles. Renewable Gasoline Blends can also be formulated with higher ethanol blend ratios to meet a variety of fuel grades and market standards. These varying formulations can result in tradeoffs across engine performance, fuel economy, and potentially cost. For example, higher ethanol blends investigated lead to slight decreases in fuel economy across FTP, HWFE, and US06 cycles (typically ~1 - 2%, despite 2 – 5% lower heating values); however, significant decreases in PM emissions were observed as well (e.g. 71- 84% reduction in the cold FTP and/or US06 cycles). Renewable Gasoline blends offer the potential for performance similar to conventional gasoline in both modern and future engines, while providing immediate reductions in greenhouse gas emissions.</div></div>

Abstract Exploration portfolio management is crucial for oil companies operating deep-water assets, whose portfolios include hundreds of exploration prospects with varying risk profiles and hydrocarbon volumes and dozens of producing platforms with varying excess capacity available for new tie-back projects. In this paper, we describe a methodology for portfolio planning and optimization (PPO) that uses reservoir simulators to model production of exploration scenarios, and the Markov decision process (MDP) to optimize the portfolio development plan. In the PPO methodology, prospects, existing platforms, and segments of the export pipeline network (that connect platforms to markets onshore) are modeled as wells and nodes in the reservoir simulator. As such, field management logic typically used for surface facilities is adapted to model the export network on a regional level. The exploration plan is modeled as an MDP to capture the impact of outcomes of earlier events on later decisions. Optimization is performed on a specially parameterized policy to allow for the optimization of exploration timing, tie-back options, and project sequences. The adapted simulation model is integrated with an external engine that evaluates the cost and economics of each scenario. The optimizer orchestrates the large number of runs needed for multi-objective optimization under uncertainty. This implementation is deployed to portfolio planners and applied to portfolios with more than 200 prospects and 70 platforms. Results showed that field management logic in the reservoir simulator is adequate to model all oil and gas capacity constraints in the platforms and export path on a regional level. Three optimization approaches are investigated with a synthetic portfolio example. It is shown that the black-box bi-objective optimization (BOO) can improve portfolio values, but the improvement is modest if the number of functions evaluated is small. Proxy accelerated optimization (PAO) allows for a larger population size and more iterations, producing solutions that are superior with similar computational costs. Finally, an automatic hierarchical optimization (AHO) approach is also examined, which leverages the structure of the problem to cut options step-by-step. AHO is shown to provide superior results to BOO and PAO in the example case but requires very problem-specific logic that may be hard to generalize. As far as we know, this is the first-ever adaptation of reservoir simulation for modeling productions from an exploration portfolio. The MDP formulation of the portfolio optimization problem and the policy-based parameterization are also first in the literature. While the methodology is introduced with applications to the deep-water asset class, the modeling approach applies to generic portfolio problems in other asset classes and other parts of the world.

Background. Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in behavior, social communication, and interaction. Objectives. To describe health staff's knowledge, attitudes, and practices regarding ASD in Lomé. Methods. From January to March 2020, we conducted a multicenter, cross-sectional, descriptive, and analytical study on ASD in childhood. A survey was administered to a sample of healthcare professionals in 5 facilities in Lomé, Togo. A questionnaire assessed the knowledge, attitudes, and practices of ASD among 139 participants (pediatricians, neurologists, general practitioners, psychologists, speech therapists, and nurses). Results.The participation rate was 84.2 % (117 / 139). One hundred seven health workers (91.5 %) had heard of ASD. Inaccurate knowledge was observed: 65.4 % gave a good definition of autism, 73.2 % did not know the autistic triad, the early warning signs (64.6 %), or the age of onset (61.0 %). Only 22.0 % of staff were familiar with autism screening tools. The highest knowledge was significantly associated with being a speech therapist or pediatrician practicing in a tertiary health facility (p < 0,0001). Higher-graded staff also observed better knowledge levels (p = 0, 0128). Concerning attitudes or practices regarding autistic children, 26 of those surveyed (22,2 %) had already screened for ASD, of whom 23 (88,5 %) worked with other specialties in case management. Conclusion. This study highlights inappropriate knowledge, attitudes, and practices among health staff. These findings warrant the need for good initial training on ASD, awareness campaigns, and the setting up a specialized center in Togo.

Attributing the sources of legacy contamination, including brines, is important to determine remediation options and to allocate responsibility. To make sound remediation decisions, it is necessary to distinguish subsurface sources, such as leaking oil and gas ("O&G") wells or natural upward fluid migrations, from surface releases. While chemical signatures of surface and subsurface releases may be similar, they are expected to imprint specific dissolved noble gas signatures, caused by the accumulation of terrigenic noble gases in subsurface leaks or re-equilibration of noble gases following surface releases. We demonstrate that only a historic surface release influenced the dissolved noble gas signature of groundwater in monitoring wells contaminated with brine near an abandoned O&G well, rather than subsurface leakage from the well. Elevated brine concentrations wereassociated with lower terrigenic helium concentrations, indicating re-equilibration with atmospheric helium at the surface during the release. Geophysical surveying indicating elevated salinity in surficial soils upgradient of the wells further supported the interpretation of the noble gas data. Eliminating the possibility that subsurface leakage was the source of the plume was critical to selecting the proper remedial action at the site, which otherwise may have included an unnecessary and costly well re-abandonment. This study demonstrates the use of noble gas analysis to compare potential sources of brine contamination in groundwater and to exclude subsurface leakage as a potential source in an oilfield.

Abstract Improving cluster efficiency is critical for economic and efficient multi-cluster per stage fracturing in unconventional shale & tight horizontal well completion. This paper highlights the findings from a field trial to test different perforation design variables which contribute to cluster efficiency. The goal was to optimize perforation design parameters and improve cluster efficiency for a given stage, and thus the well in its entirety. A two well trial was conducted across the same bench formation on a single pad in Midland Basin. In all, eight perforation designs were created using two set points (high and low) across three key perforation design variables: 1) perforation phasing & orientation, 2) perforation diameter, and 3) perforation friction. Each design was repeated eight times (i.e. eight stages) to allow for a meaningful number of data points. After stimulation operations were conducted an acoustic imaging technology was utilized to assess the perforation dimensions for all perforations post-fracture for all stages as well as various sets of pre-fracture perforations. In total, the trial was conducted across 64 stages (8 perforation designs × 8 stages per perforation design) using a Design of Experiments (DoE) method to assign low or high set points for each perforation design to best ascertain the impact of each test variable on the response variable as well as test for multicollinearity across the test variables. The uniformity index metric was used as a proxy for cluster efficiency and was calculated using two methods (a) eroded perforation area increase, and (b) post frac perforation area. Based upon the results obtained from the acoustic imaging data set and the subsequent data analysis, the uniformity index improved with a perforation design that had higher average perforation friction, smaller perforation hole shot size and a 0 degree in-line perforation orientation. The field trial results of uniformity index provided high quality statistical quantification of optimum perforation design parameters and its impact on cluster efficiency.