Geotechnical properties of oil-polluted soil: a review

Long-term performance and durability of lime-stabilized oil-contaminated soils

In oil-rich regions, the increasing risk of oil spills on soil is largely attributed to intensified extraction and transportation activities. This situation necessitates a focus on the short-term and long-term strength of contaminated soils. While existing literature primarily evaluates the oil-contaminated soils over short-term periods, typically up to 28 days, it is essential to investigate their long-term performance, extending the evaluation period to 365 days. This study addresses the critical gap in understanding the long-term performance of soils contaminated with 4 %, 7 %, and 10 % oil by evaluating the effectiveness of lime stabilization over a one-year period. Laboratory tests were conducted on soils treated with varying lime contents (0 %, 3 %, 6 %, and 9 %) and cured for 1, 14, 28, and 365 days. Key performance indicators, including unconfined compressive strength (UCS), California Bearing Ratio (CBR), and durability under wet-dry cycles, were measured. The results demonstrate that a 6 % lime content significantly improves long-term UCS, with strength gains ranging from 16.6 % to 24.5 % while enhancing resilience to wet-dry cycles. Microstructural analyses confirmed the formation of calcium-aluminum-silicate-hydrate (C-A-S-H) phases, contributing to the observed strength and durability improvements. This research underscores the potential of lime stabilization as a sustainable solution for managing oil-contaminated soils, reducing reliance on raw materials, and promoting more sustainable infrastructure development.

Applying clustered artificial neural networks to enhance contaminant diffusion prediction in geotechnical engineering

This paper introduces a novel approach using Clustered Artificial Neural Networks (CLANN) to address the challenge of developing predictive models for multimodal dataset with extreme parameter values. The CLANN method strategically decomposes the dataset, derived from Finite Element Analysis (FEA), into clusters, each representing distinct diffusion behaviors, and applies specialized neural networks within these clusters. The CLANN model was rigorously evaluated and demonstrated superior accuracy and consistency compared to traditional methods such as the Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy expert systems. While these conventional models struggled to capture the full range of diffusion dynamics, particularly under extreme conditions, CLANN consistently provided predictions that closely aligned with the actual FEA data across all scenarios. The versatility of the CLANN approach extends beyond its application to soil contamination. Its ability to handle complex, multimodal datasets suggests that this methodology can be generalized to a wide range of scientific and engineering problems characterized by similar data structures. This makes CLANN not only a powerful tool for geotechnical engineers but also a promising framework for broader applications where traditional models fall short. The findings of this study pave the way for more accurate, reliable, and adaptable predictive modeling in diverse domains, enhancing our ability to manage and mitigate environmental and engineering challenges.

Progress of Crude Oil Gasification Technology Assisted by Microorganisms in Reservoirs

Crude oil gasification bacteria, including fermenting bacteria, hydrocarbon-oxidizing bacteria, reducing bacteria, and methanogenic bacteria, participate in multi-step reactions involving initial activation, intermediate metabolism, and the methanogenesis of crude oil hydrocarbons. These bacteria degrade crude oil into smaller molecules such as hydrogen, carbon dioxide, acetic acid, and formic acid. Ultimately, they convert it into methane, which can be utilized or stored as a strategic resource. However, the current challenges in crude oil gasification include long production cycles and low efficiency. This paper provides a summary of the microbial flora involved in crude oil gasification, the gasification metabolism pathways within reservoirs, and other relevant information. It specifically focuses on analyzing the factors that affect the efficiency of crude oil gasification metabolism and proposes suggestions for improving this efficiency. These studies deepen our understanding of the potential of reservoir ecosystems and provide valuable insights for future reservoir development and management.

Adsorption of Polyetheramine-230 on Expansive Clay and Structure Properties Investigation

Polyetheramine (PEA) is a swelling inhibitor used to address engineering challenges arising from the interaction between montmorillonite (Mt) and water. This study comprehensively investigates the adsorption characteristics of PEA on three representative expansive clay samples: Na-Mt, Ca-Mt, and engineered expansive soil. Additionally, the desorption of exchangeable ions is examined. The findings reveal that a two-stage adsorption kinetic model and a pseudo-second-order kinetic model can properly describe the adsorption kinetics of PEA on expansive clays. PEA exhibits a strong capacity for ion exchange with sodium ions, while the exchange capacity for calcium ions is limited. Both protonated and non-protonated PEA contribute to rapid adsorption processes. The adsorption isotherms are well-fitted by the Langmuir and Freundlich models, with the Langmuir model being reasonable. At lower equilibrium concentrations, a higher proportion of the adsorption amount is attributed to ion exchange compared to higher equilibrium concentrations. Ion exchange emerges as the primary factor contributing to the adsorption of PEA on Na-Mt, whereas the adsorption of PEA on Ca-Mt and expansive soil is primarily attributed to physical adsorption by non-protonated PEA. X-ray diffraction results reveal significant intercalation effects of PEA as they penetrate the interlayer space and hinder interlayer ion hydration. Fourier transform infrared spectrum results demonstrate that the adsorption of PEA minimally impacts the framework of Mt structural units but primarily reduces the adsorbed water content. Clay-PEA composites exhibit a decreased affinity for water. Zeta potential experiments indicate that the adsorption of PEA significantly diminishes the surface potential of clay-PEA composite particles, effectively inhibiting their hydration dispersion.