Solution Mining Research Articles

Multi-well combined solution mining for salt cavern energy storages and its displacement optimization

Improving the water solution mining rate of the salt rock layer while reducing energy consumption is the core idea for promoting the construction of salt cavern energy storage. In this work, a novel multi-well combined solution mining method for salt cavern energy storages was presented, and a displacement optimization model incorporating sub-models for outlet brine concentration, solution mining rate, and energy consumption of water injection was proposed to maximize the solution mining efficiency. The accuracy of the proposed model is verified by comparing the simulated results with the field data. The results show that under basically the same solution mining rate, the energy consumption of brine injection can decrease by 18.82 %. If 0.49 % of the solution mining rate of the salt cavern can be sacrificed, energy consumption can be reduced by 7.40 %. A special focus was given to the well selection schemes, which indicated that optimization of well selection schemes could reduce energy consumption by 3.61 %. This work could add further insights into the inter-coupling relationship between outlet brine concentration, solution mining rate, and energy consumption of water injection, and help to reduce the energy consumption of water injection and improve solution mining rate.

Feasibility analysis on the utilization of TWH-caverns with sediment space for gas storage: A case study of Sanshui salt mine

Underground salt cavern (USC) has emerged as an optimal location for large-scale energy storage, encompassing oil, gas, hydrogen, carbon dioxide, and compressed air energy storage (CAES), owing to its inherent advantages of substantial capacity, stability, and hermeticity. However, most salt mines in China are characterized by a relatively thin salt layer thickness, a large number of interlayers and low salt layers grade. Consequently, a substantial proportion of the cavernous space resulting from water solution mining becomes progressively covered by insoluble sediment. Herein, the feasibility of gas storage in two butted-well horizontal (TWH) caverns with sediments, located within the Sanshui salt mine in Guangdong Province, China, is comprehensively analyzed through engineering investigation, physical model experimentation, numerical simulation, and theoretical analysis. Firstly, a theoretical model was developed to calculate the volume of caverns based on salt grade (soluble content). Secondly, a joint physical model experiment and numerical simulation were conducted to obtain the most probable shape of the process for forming water-soluble caverns. Moreover, a novel technology was proposed to utilize sediment space for gas storage in TWH-caverns. Ultimately, the support pressure of surrounding rock was determined by using the passive earth pressure theory, and subsequently, an assessment of cavern stability was conducted. Here, the numerical simulation results demonstrated that in the absence of sediment space utilization, there was a limited capacity for gas storage, resulting in significant deformation of the surrounding rock and an increased level of roof instability. On the contrary, sediment can provide structural support to the surrounding rock, thus increasing its stability (the plastic zone expansion was reduced from 1600 % (0.3 σz), 1345 % (0.35 σz) and 1297 % (0.4 σz) to 109 %, 87 % and 78 %, respectively) and enhancing the capacity for gas storage. Accordingly, this study provides valuable guidance for the assessment of gas storage in low-grade salt sedimentary environments, as well as for the design of gas injection and brine discharge, and stability evaluations.

Modern Approaches to Uncertain Database Exploration from Categorizing Data to Advanced Mining Solutions

In today's digitized era, the ubiquity of data from diverse sources has introduced unique challenges in database management, notably the issue of data uncertainty. Uncertainty in databases can arise from various factors – sensor inaccuracies, human input errors, or inherent vagueness in data interpretation. Addressing these challenges, this research delves into modern approaches to uncertain database exploration. The paper begins by exploring methods for categorizing data based on certainty levels, emphasizing the importance and mechanisms to distinguish between certain and uncertain data. The discussion then transitions to highlight pioneering mining solutions that enhance the utility of uncertain databases. By integrating state-of-the-art techniques with traditional database management principles, this study aims to bolster the reliability, efficiency, and versatility of data mining in uncertain contexts. The implications of these methods, both theoretically and in real-world applications, hold the potential to redefine how uncertain data is perceived and utilized in diverse sectors, from healthcare to finance.

OCPMDM 2.0: An intelligent solution for materials data mining

Machine learning methods have played a significant role in materials design. With the rapid development of the Materials Genome Initiative (MGI) and data science, material researchers are confronted with the requirements to conduct sophisticated data analytics in modeling the property of materials. To make it more convenient for material researchers to design new materials using machine learning methods, an intelligent web platform called the second version of online computation platform for material data mining (OCPMDM 2.0) has been updated from the previous computation platform in our lab. Besides the various data mining algorithms developed in OCPMDM 1.0, the new platform tries to provide an intelligent solution for materials datamining, including descriptors filling, virtual screening of candidate materials, connection with materials databases and model sharing. It is convenient for materials researchers to obtain the machine learning model and the result of applying model by only submitting materials data on OCPMDM 2.0. To demonstrate the applications of the platform, two data sets of different kinds of materials were automatically processed to obtain the best models in the platform. The models are applied to screen out candidates with better properties than those in the training dataset. Material data mining process can be implemented via the platform, which provides convenient ways for material researchers in materials design and optimization. The URL of the platform is http://materials-data-mining.com/ocpmdm/.

Synergistic effects of hydrogen peroxide and phosphate on uranium(VI) immobilization: implications for the remediation of groundwater at decommissioned in situ leaching uranium mine.

The processes of acid in situ leaching (ISL) uranium (U) mines cause the pollution of groundwater. Phosphate (PO43-) has the potential to immobilize U in groundwater through forming highly insoluble phosphate minerals, but the performance is highly restricted by low pH and high sulfate concentration. In this study, hydrogen peroxide (H2O2) and PO43- were synergistically used for immobilizing U based on the specific properties of groundwater from a decommissioned acid ISL U mine. The removal mechanisms of U and the stability of U on the formed minerals were elucidated by employing X-ray diffraction, scanning electron microscopy, X-ray photoelectron spectroscopy and kinetic experiments. Our results indicated that the removal of U by simultaneously adding H2O2 and PO43- was significantly higher than the removal of U by individually adding H2O2 or PO43-. The removal of U increased with increasing PO43- concentration from 20 to 200 mg L-1 while decreased with increasing H2O2 concentration from 0.003 to 0.3%. Specifically, the removal efficiency of U from groundwater reached 98% after the application of 0.003% H2O2 and 200 mg L-1 PO43-. Amorphous iron phosphate that preferentially formed at low H2O2 and high PO43- concentrations played a dominant role in U removal, while the formations of schwertmannite and crystalline iron phosphates may be also contributed to the removal of U. This was significantly different from the immobilization mechanism of U through the formation of uranyl phosphate minerals after adding phosphate. The kinetic experimental results suggested that the immobilized U had a good stability. Our research may provide a promising method for in situ remediating U-contaminated groundwater at the decommissioned acid ISL U mines.

Application of Machine Learning Methods to Assess Filtration Properties of Host Rocks of Uranium Deposits in Kazakhstan

The uranium required for power plants is mainly extracted by two methods in roughly equal amounts: quarries (underground and open pit) and in situ leaching (ISL). Uranium mining by in situ leaching is extremely attractive because it is economical and has a minimal impact on the region’s ecology. The effective use of ISL requires, among other things, the accurate assessment of the host rocks’ filtration characteristics. An accurate assessment of the filtration properties of the host rocks allows optimizing the mining process and improving the quality of the ore reserve prediction. At the same time, in Kazakhstan, this calculation is still based on methods that were developed more than 50 years ago and, in some cases, produce inaccurate results. According to our estimates, this method provides a prediction of filtration properties with a determination coefficient R2 = 0.32. This paper describes a method of calculating the filtration coefficient of ore-bearing rocks using machine learning methods. The proposed approach was based on nonlinear regression models providing a 20–75% increase in the accuracy of the filtration coefficient assessment compared with the current methodology. The work used different types of machine learning algorithms based on the gradient boosting technique, bagging technique, feed-forward neural networks, support vector machines, etc. The results of logging, core sampling, and hydrogeological studies obtained during the exploration stage of the Inkai deposit were used as the initial data. All used machine learning models demonstrated significantly better results than the old method. This resulted in improved results compared with previous studies. The LightGBM regressor demonstrated the best result (R2 = 0.710).

Energy potential of mining transport at mines of Kyrgyzstan located at high altitude

Review and Justify use of new alternative way of gold ore transportation on high altitude mining operations in Kyrgyzstan in prime purpose of the study. Glaciers of Kyrgyzstan are main source of fresh water supply in Central Asia. Local ecosystem preservation is important part of metal mining in Kyrgyzstan and specific needs of nomads should be taken in the account. Novelty of study is preliminary assessment of mine in Kyrgyzstan for potential gravity energy by loading and dumping point’s GPS coordinates are taken on site and review of appropriate technologies for ore transportation in order to reduce carbon emissions. Aerial ropeway specific energy consumption formula and recuperating ropeway system from previous studies are reviewed and used in study. Improvement of existing mine operations by improving transportation practices should be further studied. The design and maintenance of haul roads may allow to reduce diesel fuel consumption by 10% and more. Preliminary potential energy for recuperation by aerial ropeway application in Jerooy and Jamgyr gold mines of Kyrgyzstan are identified. Energy recuperating aerial ropeway system with fuel cells can be strategically long-term preferred solution for mines in Kyrgyzstan located in high altitude.

An Efficient and Accurate GPU-based Deep Learning Model for Multimedia Recommendation

This article proposes the use of deep learning in human-computer interaction and presents a new explainable hybrid framework for recommending relevant hashtags on a set of orpheline tweets, which are tweets with hashtags. The approach starts by determining the set of batches used in the convolution neural network based on frequent pattern mining solutions. The convolutional neural network is then applied to the set of batches of tweets to learn the hashtags of the tweets. An optimization strategy has been proposed to accurately perform the learning process by reducing the number of frequent patterns. Moreover, eXplainable AI is introduced for hashtag recommendations by analyzing the user preferences and understanding the different weights of the deep learning model used in the learning process. This is performed by learning the hyper-parameters of the deep architecture using the genetic algorithm. GPU computing is also investigated to achieve high speed and enable the execution of the overall framework in real time. Extensive experimental analysis has been performed to show that our methodology is useful on different collections of tweets. The experimental results clearly show the efficiency of our proposed approach compared to baseline approaches in terms of both runtime and accuracy. Thus, the proposed solution achieves an accuracy of 90% when analyzing complex Wikipedia data while the other algorithms did not achieve 85% when processing the same amount of data.

The long-term performance of a bituminous geomembrane (BGM) under single-sided exposure conditions

A custom-designed apparatus (referred to as the Ageing Column) is used to age a 4.8-mm thick elastomeric bituminous geomembrane (BGM) under single-sided exposure to a synthetic municipal solid waste (MSW) leachate and a synthetic high pH mining solution (pH 11.5) at 55 °C, 70 °C, and 85 °C. This apparatus involves a closer simulation of the BGM's chemical exposure conditions in the field than the double-sided immersion tests in which the BGM is exposed to the solution from both surfaces. The mechanical, rheological, and chemical properties of the BGM are examined to assess the degradation in the BGM components relative to double-sided immersion experiments. While the single-sided ageing of the BGM reduces the degradation rates in the BGM mechanical properties, it does not affect the degradation rate of the bitumen coat relative to the double-sided exposure. Additionally, the exposure of the BGM to the MSW leachate resulted in faster degradation in its mechanical properties but slower degradation in the bitumen coat than that obtained in the exposure to the mining solution. However, predictions of the time to nominal failure of the BGM established using Arrhenius modelling at different temperatures show that the temperature effect is more significant on the BGM durability than the chemistry of the considered solutions.

Systematic comparison of different recycled fine aggregates from construction and demolition wastes in OPC concrete and PPC concrete

River sand is widely used as fine aggregates in concrete. Several countries have prohibited the use of river sand in construction owing to the devastating effects of excessive river sand mining. Hence, the need for alternative fine aggregates is substantially enhanced in the construction sector. The disposal of construction and demolition wastes into landfills is a major concern due to the stringent environmental regulations. Using recycled fine aggregates from construction and demolition wastes as alternative fine aggregates is an environmentally viable solution for excessive river sand mining. Although numerous previous studies on recycled fine aggregates are available, a comprehensive review of the comparative assessment of different recycled aggregates derived from construction and demolition wastes is highly limited. Therefore, the potential reuse of various types of recycled fine aggregates from construction and demolition wastes such as concrete, brick, ceramic and marble wastes as fine aggregates in ordinary concrete and PPC concrete is presented in the review. The influence of recycled fine aggregates on fresh properties such as workability, air content and density is presented. Besides, compressive strength, tensile strength, flexural strength, water absorption, carbonation and shrinkage of recycled fine aggregates used in ordinary concrete and PPC concrete are methodically compared. High water absorption of the recycled fine aggregates lowers the workability of concrete. Reduction in the density of concrete is observed due to the increased porosity while replacing river sand with recycled fine aggregates. Increase in the carbonation depth, air content and shrinkage are witnessed for all types of recycled fine aggregates incorporated concretes.

Looking at the fringes of MedTech innovation: a mapping review of horizon scanning and foresight methods

ObjectivesHorizon scanning (HS) is a method used to examine signs of change and may be used in foresight practice. HS methods used for the identification of innovative medicinal products cannot...

Experiences of Underground Mine Backfilling Using Mine Tailings Developed in the Andean Region of Peru: A Green Mining Solution to Reduce Socio-Environmental Impacts

In Peru, socio-environmental conflicts related to the development of mining-metallurgical processes and the responsible disposal of mine tailings have become central issues for accepting mining projects, especially regarding building relationships of trust with the communities. This condition has prompted the Peruvian mining industry to advance in managing alternatives to the conventional surface disposal of mine tailings. A promising and increasingly popular management strategy for mine tailings in Peru is their disposal inside underground mines. This article presents: site-specific conditions, advantages/disadvantages, and lessons learned from practical experiences of mine tailings disposal in underground mines in Peru. In addition, some techniques are highlighted, such as (i) hydraulic fill, (ii) cemented hydraulic fill, and (iii) cemented paste backfill. Finally, this article concludes that the responsible disposal of mine tailings in underground mines is a green mining solution that reduces negative socio-environmental impacts, limiting the generation of acid rock drainage (ARD) and the leaching of metals due to the decrease in contact with oxygen and rainfall, thus mitigating the contamination of surface and underground waters, reducing the footprint of affectation in the territory, and eliminating the emission of particulate matter in the environment.

Valorization of landfill mined plastic waste and soil-like fractions in polymer composites – A comprehensive solution for sustainable landfill mining

Utilization of Landfill-Mined Plastic Waste (LMPW) and LandFill-Mined-Soil like-Fractions (LFMSF), which together contribute to ≈85–90% of the landfill residues, read as Socio-Economic Refuses (SERs), is the need of the hour. Though earlier researchers have tried to utilize LMPW through energy recovery options, the emission of chlorine-based organic compounds and H2S became big concerns. Similarly, LFMSF has been investigated for its suitability as a manmade resource to produce compost, refuse-derived fuel, pH buffering material and for structural filling applications. However, its potential for leaching salts, heavy metals and microplastics remains a primary environmental concern. To overcome these issues, developing innovative solutions by using LMPW and LFSMF, in tandem, which would result in sustainable landfill mining, is necessary. One of the ways to achieve this could be the creation of polymer Composites, designated as CompoSERs, in which LMPW and LFSMF work as binder and filler, respectively. Such a strategy would help in restricting the negative impact of both these constituents on the geoenvironment in terms of leaching and/or fugitive dust in the surrounding. Keeping this in view, the influence of LFMSF (read filler content) on the overall characteristics of CompoSERs, including macro- and micro-mechanical, functional group, morphological, thermal, and leaching, has been investigated. Since the LMPW consists of inherent fillers in the form of additives/fillers used in the original plastics and adhered contaminants, the experimental (read actual) filler content (designated as FCexp) would be higher than LFMSF added in the process of manufacturing. Hence, easy-to-use empirical relationships between FCexp and mechanical properties of the CompoSERs that can be employed for field-scale applications have been proposed. Based on these relationships, the critical FCexp, which defines a change in the trends of the FCexp and mechanical properties of the CompoSERs, has been observed as 30% by weight. Moreover, unlike other existing utilization schemes of LFMSF, this strategy turns out to be an efficient way to avoid the contamination of the geoenvironment owing to the leaching of heavy metals and dissolved organic matter present in it.

Dysthyroidism during immune checkpoint inhibitors is associated with improved overall survival in adult cancers: data mining of 1385 electronic patient records

BackgroundDysthyroidism (DT) is a common toxicity of immune checkpoint inhibitors (ICIs) and prior work suggests that dysthyroidism (DT) might be associated with ICI efficacy.Patients and methodsConSoRe, a new generation data...

Potential of Salt Caverns for Hydrogen Storage in Southern Ontario, Canada

Salt caverns produced by solution mining in Southern Ontario provide ideal spaces for gas storage due to their low permeability. Underground hydrogen storage (UHS) is an important part of the future renewable energy market in Ontario in order to achieve global carbon neutrality and to fill the gap left by retiring nuclear power plants. However, large-scale hydrogen storage is still restricted by limited storage space on the ground’s surface. In this study, hydrogen’s physical and chemical properties are first introduced and characterized by low molecular weight, high diffusivity, low solubility, and low density. Then, the geological conditions of the underground reservoirs are analyzed, especially salt caverns. Salt caverns, with their inert cavity environments and stable physical properties, offer the most promising options for future hydrogen storage. The scales, heights, and thicknesses of the roof and floor salt layers and the internal temperatures and pressures conditions of salt caverns can affect stabilities and storage capacities. Finally, several potential problems that may affect the safe storage of hydrogen in salt caverns are discussed. Through the comprehensive analysis of the influencing factors of hydrogen storage in salt caverns, this study puts forward the most appropriate development strategy for salt caverns, which provides theoretical guidance for UHS in the future and helps to reduce the risk of large-scale storage design.

In-Situ Leaching Mining Technique for Deep Bauxite Extraction and the Countermeasures for Water Pollution Prevention: An Example in the Ordos Basin, China

As the second most significant metal following steel, aluminum plays a vital role in the advancement of both strategic emerging industries and national economic development. The existing oil and gas drilling data indicate that the deep bauxite deposits is abundant around the Ordos Basin in China, at the depths ranging from several hundred meters to several kilometers. Based on the geological and hydrogeological characteristics analysis, it is found that deep bauxite deposits in the basin have distinct electrical characteristics, characterized by four highs and two lows. While there is scarcity of prior research on the exploration topic for the technique limitation. In this paper, a logging interpretation model has been developed, which allows the evaluation of bauxite deposits. An efficient technology was proposed for deep bauxite exploration, utilizing an in-situ leaching mining technique. This technology is well-suited to the geological conditions of the Ordos Basin, ensuring that the solution flows within the bauxite ore bed without any seepage loss. To prevent the leaching solution from seeping into and polluting the main aquifer around the basin, several measures have been proposed. These include filling with polymer resin, well pattern seepage control plugging, and establishing monitoring systems. The results of this study provide a theoretical basis for the adoption of environmentally sustainable mining techniques and the mitigation of water pollution in deep bauxite exploration.

Late Permian evaporite facies variation in the Forth Approaches Basin, North Sea: implications for hydrogen storage

Hydrogen is expected to play a key role in decarbonizing industry and storing energy from intermittent sources such as wind energy. Underground salt caverns are an attractive target for storage due to their large volumes and effective sealing capacity. Despite ambitious goals to become a world leader in hydrogen, there are no onshore salt basins in Scotland. Therefore, the offshore Forth Approaches Basin (FAB), currently undergoing development of the Seagreen Offshore Wind Farm, could provide a critical storage site. Re-evaluation of petrophysical data from five legacy hydrocarbon wells allowed an updated assessment of the composition and variability of the Late Permian Zechstein evaporite sequence. Well analysis is combined with seismic interpretation to understand the salt bodies and their suitability for solution mining. Three halite formations are identified: (1) the Stassfurt Halite Formation, which has insufficient thickness for solution mining; (2) the Leine Halite Formation, which comprises three subunits with a KCl-dominated unit separating two halite-dominated units; and (3) the Aller Halite Formation, which is only identified in the centre of the FAB. Where halokinesis has occurred, the Leine Halite Formation reaches sufficient thicknesses (>300 m) and purity for salt cavern placement; however, heterogeneities are challenging to predict. Layered evaporites only reach sufficient thickness where the Aller Halite Formation is present and could be developed with the underlying Leine Halite Formation. Heterogeneities can be correlated across wells within the layered sequences, aiding prediction. A strong understanding of evaporite facies distribution is required to ensure that halite bodies are suitable for safe and economic solution mining in the FAB and other salt basins globally. Thematic collection: This article is part of the Hydrogen as a future energy source collection available at: https://www.lyellcollection.org/topic/collections/hydrogen

Towards Automating the Identification of Sustainable Projects Seeking Financial Support: An AI-Powered Approach

The criticality of sustainable development to control the unprecedented consequences of climate change is clear. A vital element in launching sustainability projects is financing, especially for projects by small and medium enterprises. The first and crucial step to offering financing services for sustainable development is to identify and evaluate promising projects. The current practice to accomplish this step heavily depends on subject-matter expertise and professional networks. The current practice also involves extensive manual document reviews and subjective decisions. Therefore, existing methods are time-consuming, inefficient, and not scalable. This study proposes an automated system to identify potential sustainability projects for financing services using Artificial Intelligence (AI). The proposed method uses web crawlers and text mining solutions, including Natural Language Processing (NLP), to search the Internet, analyze text data, evaluate the information quantitatively, and identify potential sustainability projects for financing services. The proposed method was implemented and empirically assessed. The results indicate that the AI-enhanced system is able to identify and prioritize potential sustainability projects with 87% accuracy. The outcomes of this study will help financial experts and decision-makers take advantage of the information available on the Internet efficiently to improve the existing methods for identifying potential projects for financing services.

Simultaneous combined XRF-XRD analysis of geological sample: New methodological approach for on-site analysis on New-Caledonian Ni-rich harzburgite

There is a growing interest in on-site, real-time analytical solutions for mining and environmental projects to characterize large areas and/or volumes of raw materials that are sometimes highly heterogeneous in terms of elemental distribution and mineralogy. Several fast and cost-effective methods are used for rapid on-site screening and real-time chemical and mineralogical characterization, such as portable X-ray fluorescence (pXRF) and X-ray diffraction (pXRD). However, these methods are not always applicable due to limitations in the detection and quantification of light elements (Mg, Al, Si) for pXRF or complex or minor minerals for pXRD, whose results need to be supported by laboratory analysis.This study presents a new methodological approach for in situ rapid chemical and mineralogical characterization of samples, based on the use of a transportable instrument (called ID2B) that allows, in a single acquisition step, a combined XRD-XRF analysis to identify and quantify the chemical elements and their associated minerals. The HI0 harzburgite sample from New Caledonia used to evaluate the data was analyzed in the laboratory (SEM-EDS, EPMA, XRF and XRD) and with the ID2B instrument to highlight the potential of our new methodology. In order to demonstrate the interest of using the ID2B combined XRF-XRD analysis approach directly in the field, where sample preparation is not always easy to implement, this comparison was made on the same sample (HI0), prepared in two different ways, either as a powderized (optimal preparation) or as-sawn (unprepared) sample. After automated processing of the combined XRF-XRD datasets acquired with the ID2B instrument, the chemical elements and mineralogical phases identified on both the powder and as-sawn samples are identical to the laboratory analyses.The chemical proportions calculated from the combined XRF-XRD data sets are also close to the laboratory XRF analysis with relative errors <5 % for Al, Mg and Si and even closer for Ca, Cr, Mn, Ni and Fe. The variability in the calculated chemical proportions is attributed to the sample heterogeneity highlighted by the mineral proportions that vary slightly between the laboratory XRD and XRD ID2B analyses of the powder, and more pronounced for the as-sawn XRD ID2B analysis. These observations show that the combined XRF-XRD approach performed on powder and as-sawn samples provides accurate chemical and mineralogical results to those obtained in the laboratory. The deployment of this new methodological approach directly on the field can provide valuable chemical and mineralogical analyses.

Assessment of the Greenhouse Gas Footprint and Environmental Impact of CO2 and O2 in situ Uranium Leaching

AbstractUnder the new development philosophy of carbon peaking and carbon neutrality, CO2 and O2 in situ leaching (ISL) has been identified as a promising technique for uranium mining in China, not only because it solves carbon dioxide utilization and sequestration, but it also alleviates the environmental burden. However, significant challenges exist in assessment of CO2 footprint and water‐rock interactions, due to complex geochemical processes. Herein this study conducts a three‐dimensional, multicomponent reactive transport model (RTM) of a field‐scale CO2 and O2 ISL process at a typical sandstone‐hosted uranium deposit in Songliao Basin, China. Numerical simulations are performed to provide new insight into quantitative interpretation of the greenhouse gas (CO2) footprint and environmental impact (SO42 –) of the CO2 and O2 ISL, considering the potential chemical reaction network for uranium recovery at the field scale. RTM results demonstrate that the fate of the CO2 could be summarized as injected CO2 dissolution, dissolved CO2 mineralization and storage of CO2 as a gas phase during the CO2 and O2 ISL process. Furthermore, compared to acid ISL, CO2 and O2 ISL has a potentially smaller environmental footprint, with 20% of SO42– concentration in the aquifer. The findings improve our fundamental understanding of carbon utilization in a long‐term CO2 and O2 ISL system and provide important environmental implications when considering complex geochemical processes.