WO2025040953A2 - Method and system to digitally model, monitor, measure, report and verify in-situ co2 mineralization - Google Patents

Abstract

A system and method for digitally managing a CO₂ injection and mineralization process within a subterranean geologic reservoir and a subterranean igneous rock formation. The system includes a processor with circuitry with program instructions configured to measure parameters of an injection fluid and a reservoir fluid, calculate injection data including the amount of CO₂, a tracer, and supplementary substances effective to maintain a predetermined CO₂ to tracer mass ratio in the injection fluid or the reservoir fluid, and inject the injection fluid into the subterranean geologic reservoir and/or the subterranean igneous rock formation to manage the CO₂ injection and mineralization process, a measuring device connected to the processor, an injection device connected to the processor.

Description

METHOD AND SYSTEM TO DIGITALLY MODEL, MONITOR, MEASURE, REPORT AND VERIFY IN-SITU CO₂ MINERALIZATION

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. Provisional Application No. 63/520,788, filed on August 21, 2023, and U.S. Provisional Application No. 63/520,782, filed on August 21, 2023, each of which is herein incorporated in its entirety by reference for all purposes as set forth herein.

BACKGROUND

TECHNICAL FIELD

The present disclosure relates to the field of carbon capture and storage technology. Specifically, the invention is related to an automated system and method employing algorithms and sensors to autonomously monitor and verify the injection of carbon dioxide (CO₂) into reservoirs for the purpose of mineralization.

The present disclosure is related to a digital and automated apparatus, system, or method to monitor and assess in-situ mineralization, and subsequently integrate this data into digital twin models. In particular, the present disclosure focuses on the digitization of the monitoring, reporting, and verification process associated with CO₂ removal through in-situ mineralization in igneous rock formations, such as mafic and ultra-mafic rocks. This digitized approach aims to facilitate the verification and generation of various forms of carbon credits, serving as a tangible representation of CO₂ removal.

The present disclosure is also related to CO₂ management and mitigation, particularly within the context of addressing the substantial global emissions resulting from fossil fuel consumption. BACKGROUND OF THE INVENTION

The pressing global challenge of mitigating CO₂ emissions, predominantly attributed to fossil fuel consumption, has spurred innovative avenues for sustainable solutions. Two distinct but synergistic fields of invention address this challenge. The first field revolves around the creation and utilization of digital models to monitor and evaluate in-situ mineralization processes, complemented by their integration into digital twin models for predictive analysis. The second field is centered on the digitization of the monitoring, reporting, and verification procedures associated with CO₂ removal through in-situ mineralization, culminating in the generation of diverse forms of carbon credits.

Automating and digitally monitoring carbon dioxide mineralization in igneous rocks represents a promising avenue in the quest for effective carbon caphire and storage solutions. Mafic and ultramafic rocks, (e.g., basalts, peridotites, etc.}, rich in alkaline minerals, offer the potential to permanently sequester CO₂ through the process of mineral carbonation, converting CO₂ into stable carbonate minerals. However, automating this intricate process poses several challenges that require innovative solutions to harness the full potential of in- situ mineralization based carbon capture and storage (CCS). In addressing the complexities of this process, the present disclosure introduces innovative approaches that culminate in a transformative system for automated CO₂ in-situ mineralization.

Fossil fuel-derived carbon dioxide emissions surpassed 37,000 million metric tons (MMT) in the year 2018, underscoring the urgent need for innovative solutions that can effectively curtail these emissions and mitigate their adverse environmental impact. Geologic carbon storage (GCS) has emerged as a potential avenue for managing CO₂ emissions on a large scale. In 2017, GCS endeavors were responsible for sequestering approximately 230 MMT of CO₂, with approximately 150 MMT employed for enhanced oil and gas recovery (EOR) operations, while the remaining volume was earmarked for dedicated geological storage. A remarkable aspect of such storage initiatives lies in their ability to leverage deep saline aquifer systems for the long-term geological confinement of CO₂. These practices, commonly referred to as carbon capture and sequestration (CCS) projects, involve sequestering CO₂ into various types of rock formations. Most of the current projects capitalize on deep saline aquifer systems as repositories for CO₂, positioning them as promising solutions for sustainable CO₂ management. While established CCS projects have predominantly focused on utilizing deep saline aquifers, emerging interest centers around carbon storage within igneous rock formations, such as mafic and ultramafic rocks. Despite being a relatively nascent field, investigations into carbon capture and sequestration (CCS) within these igneous formations hold promise due to the prevalence of reactive minerals and glassy phases within these formations. Such characteristics present an opportunity for the permanent and rapid conversion of CO₂ into stable carbonate minerals, contributing to effective and lasting CO₂ entrapment. An example of this type of project relates to mineralizing CO₂ with alkaline-rich rock formations such as peridotite and basalt. In these types of projects, typically CO₂ is reacted with alkaline minerals in these rocks producing calcium-magnesium-iron carbontes (e.g. calcite, magnesite, siderite) and thereby storing CO₂ permanently.

One major aspect of mineralizing CO₂ and making mineralization profitable is the generation and estimation of carbon credits. A carbon credit, as presented in present disclosure is a market-tradable certificate and/or financial contract symbolizing the reduction or avoidance of one metric ton of carbon dioxide (CO₂) or equivalent greenhouse gas emissions. This innovation serves as a fundamental element within carbon removal and offset programs and emissions trading systems, and provides a means to incentivize emission reduction endeavors by enabling entities to earn carbon credits through emission-reducing projects. These projects span activities such as renewable energy generation, reforestation, energy efficiency enhancements, and carbon capture and storage initiatives. The issuance and exchange of carbon credits fosters a market-based approach that efficiently encourages emission reductions and removals and supports global efforts to mitigate climate change, aligning with international agreements like the Paris Agreement. This, however, relies on accurate estimates of the amount of CO₂ sequestered. Estimating carbon credits for the mineralization of carbon dioxide (CO₂) within igneous rock formations presents a multifaceted challenge in the realm of carbon offset and removal mechanisms. The complex and dynamic nature of geochemical processes involved in CO₂ mineralization introduces uncertainties that hinder precise quantification. One significant challenge lies in accurately measuring the extent of CO₂ mineralization over time, as this process is influenced by various factors such as mineral composition, temperature, pressure, and the availability of reactive sites. Furthermore, distinguishing between naturally occurring mineral alterations and those induced by CO₂ injection and subsequent mineralization poses a challenge in attributing the carbon sequestration specifically to the injection efforts. The inherent heterogeneity of rock formations complicates efforts to extrapolate results from small-scale tests to larger reservoirs, leading to difficulties in establishing consistent methodologies for calculating carbon credits. Moreover, the long-term stability of mineralized CO₂ within rock formations and the potential for re-release into the atmosphere necessitate rigorous monitoring and verification, which can be logistically challenging and resource intensive. As the field of CO₂ mineralization within igneous rock formations continues to evolve, developing robust and universally accepted methodologies for quantifying carbon credits becomes important to ensuring the integrity of carbon offset programs, facilitating transparent accounting, and promoting reliable CO₂ mitigation strategies. C0₂ emissions contribute significantly to global climate change and traditional mitigation strategies are challenged by the scale and speed of emissions. There is an increasing demand for innovative solutions that can sequester CO₂ effectively. Various igneous rocks have shown promise in mineralizing CO₂ through a natural process, such as basalts and peridotites. Igneous rocks, with their intrinsic ability to naturally mineralize CO₂, have emerged as a promising avenue for carbon capture and storage (CCS). One such process is the use of peridotites, which involves the reaction of CO₂ with minerals present hi peridotite formations, converting it into stable and benign carbonate minerals over time. This natural carbon sequestration mechanism has caught the attention of researchers and environmentalists due to its potential to significantly offset anthropogenic emissions.

While the concept of igneous rock-based CO₂ mineralization holds promise, practical implementation on a meaningful scale has been challenging. Enhancing the process of mixing of CO₂ with carrier fluids and injecting these into the subsurface as well as monitoring the progress of mineralization and assessing its impact in real time presents intricate geological, technical, and computational obstacles. Moreover, the lack of robust verification and reporting mechanisms has hindered the adoption of this technology within regulatory frameworks and stakeholder communities.

To bridge these gaps, the present disclosure provides a comprehensive method, system and process that digitally models, monitors, measures, reports, and verifies CO₂ mineralization in igneous rocks in real time and uses the resulting data to provide a foundation for a robust and reliable method and system for issuing, maintaining, marketing CCS credits. This breakthrough integrates cutting-edge technology with the intrinsic geological properties of igneous rock formations, offering a holistic solution to the intricate challenges associated with carbon capture and storage. DESCRIPTION OF THE RELATED ART

In the global pursuit of addressing climate change and reducing greenhouse gas emissions, carbon capture and storage (CCS) technologies have emerged as vital tools to mitigate the adverse impacts of excessive carbon dioxide (CO₂) in the atmosphere. One approach to achieve this goal involves the injection of CO₂-rich fluids into reservoirs for mineralization — a process that permanently converts CO₂ into stable mineral forms. The efficacy of this mineralization process depends on precise control, real-time monitoring, and accurate verification of the injected fluid's composition, behavior within the reservoir, and subsequent mineralization rates.

Traditional methods for CO₂ injection and mineralization involve manual control and periodic testing, which can lead to inefficiencies, inaccuracies, and uncertainties in assessing the progress of the mineralization process. This creates a demand for a more advanced and automated system capable of ensuring enhanced CO₂ dissolution, precise fluid composition, and accurate quantification of mineralization rates within reservoirs.

The Wallula Basalt Pilot Project has provided qualitative evidence of mineralization processes through the presence of ankerite nodules correlated with injected CO₂. However, the need for accurate quantitative estimation of in-situ mineralization remains unmet. Existing methods, including those pioneered by the CarbFix project in Iceland, involve radiotracer and isotope fractionation techniques to estimate post-injection mineralization. However, their application at a reservoir scale introduces complexities due to factors such as the interplay of different geochemical processes, the differentiation between natural alteration minerals and reaction-induced alterations, and the dependence on flow paths within the reservoir.

A growing body of prior art and published research has been dedicated to the intricate challenge of estimating carbon credits associated with the mineralization of CO₂ within rock formations. Various studies have explored methods for assessing the extent of CO₂ mineralization, with a focus on geochemical reactions, mineralogical transformations, and the resultant carbon storage potential. Some research endeavors have concentrated on quantifying the effectiveness of specific rock types, investigating the role of mineral surfaces in facilitating CO₂ mineralization, and evaluating the influence of environmental conditions on the process. Models that integrate factors like rock permeability, mineral reactivity, and injection parameters to predict CO₂ mineralization rates have been devised. Additionally, studies have examined the feasibility of monitoring and verifying mineralized CO₂ over time, exploring techniques such as geochemical tracers, isotopic analysis, and geophysical imaging. Despite these advancements, challenges persist in standardizing methodologies, addressing uncertainties related to natural versus induced alterations, and extrapolating laboratory findings to real-world reservoirs. The culmination of prior research offers a foundation for refining and advancing strategies to accurately estimate carbon credits for CO₂ mineralization in rock formations, facilitating the development of effective carbon offset mechanisms and contributing to sustainable climate mitigation efforts.

The present disclosure addresses these challenges by employing advanced numerical analysis techniques to detect subtle changes in reservoir conditions. The present disclosure, an Automated Control Unit (ACU) designed to manage the CO₂ injection process and subsequent mineralization within reservoirs, addresses these challenges by leveraging cutting-edge technology and algorithmic intelligence.

The ACU's ability to dynamically measure, calculate, adjust, and interpret various parameters and interactions within the CO₂ injection and mineralization process offers a novel solution to these challenges. By integrating sophisticated algorithms and real-time sensor data, the ACU aims to improve the mineralization process, increase its efficiency, and provide valuable insights for assessing reservoir behavior, mineralization rates, future maintenance requirements as well as establishing a basis for marketable carbon credits.

As regulatory bodies and industries strive to establish sustainable practices and minimize carbon emissions, technologies that provide accurate, automated, and verifiable control over the CO₂ mineralization process become indispensable. The present method and system aim to bridge the gap between traditional manual approaches and the need for precise, automated solutions by providing an ACU-driven system capable of enhancing the reliability, transparency, and quantification of CO₂ mineralization outcomes. By offering a holistic approach that spans from injection fluid preparation to mineralization assessment and prediction, the ACU promises to revolutionize the field of carbon capture and storage, contributing significantly to global efforts to combat climate change.

This invention can be used for any company who is injecting CO₂ into the reactive rocks for mineralization. Buyers of carbon credits want their credits to be fully verified with proof that the CO₂ has been removed. The present system and method can digitally prove the basis of CCS credits, an advantage that is not available by current methods.

By harnessing the unique spatial distribution of the CO₂ plume and its hydrological signature in a rock formation, this approach aims to quantify CO₂ trapping via mineralization. The present disclosure provides methodologies, findings, and implications for this innovative approach, thereby contributing significantly to the field of carbon dioxide management and advancing the goal of sustainable CO₂ mitigation.

The present disclosure satisfies the need for effective carbon mitigation strategies and has propelled innovation at the intersection of geology and technology. The method and process disclosed herein include a comprehensive solution that not only maximizes the potential of igneous rocks for carbon capture and storage but also overcomes the challenges associated with real-time monitoring, reporting, and verification. The method and system of the present disclosure hold the promise to transform the landscape of climate change mitigation, exemplifying the harmonious synergy between human ingenuity and the Earth’s natural processes.

SUMMARY OF THE INVENTION

The present disclosure is related to an automated system that uses algorithms and sensors to digitally monitor and verify CO_: injection into a reservoir for mineralization.

Currently, when companies mineralize CO₂, they have to rely on extensive water testing and calculations to prove that the CO₂ has been mineralized. The method and system of the present disclosure address these challenges digitally and thereby autonomously streamline the process to remove human error. Furthermore, the system can detect changes in the subsurface and can adapt the injection fluid and/or injection conditions to improve the process. Finally, the results will be verifiable as the data is provided by sensors.

The present disclosure leverages the natural CO₂ mineralization potential of igneous rocks by augmenting it with advanced computational algorithms and sensor technology. By digitally modeling the intricate interactions between CO₂ emissions and reactive minerals, the method and system of the present disclosure provide accurate measurements and/or predictions of mineralization rates and efficiency. The real-time monitoring of important variables, such as temperature, pressure, and CO₂ concentrations both on the surface of the Earth as well as within reactive subterranean mineral-rich sites, ensures a dynamic feedback loop that drives adaptive management of the mineralization process. Moreover, the present disclosure provides automated reporting mechanisms that offer a transparent and accountable means to document the progress of CO₂ mineralization. These reports, encompassing captured CO₂ volumes, mineral transformation rates, and long-term impact projections, serve as a link between technological advancements and regulatory compliance. By enhancing transparency, traceability', and verification, this invention addresses concerns related to credibility and ensures that the potential benefits of igneous rock-based CO₂ mineralization can be confidently communicated to stakeholders, from policymakers to investors.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings show embodiments of the disclosed subject matter for the purpose illustrating the invention. However, it should be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is an illustration of a non-limiting example of a system and method according to certain embodiments;

FIG. 2 is a schematic process flow diagram of a system and method according to certain embodiments;

FIG. 3A is schematic process flow diagram of a system and method according to certain embodiments;

FIG. 3B is schematic process flow diagram of a system and method according to certain embodiments;

Fig. 4 is an illustration of a non-limiting example of details of computing hardware used in the computing system, according to certain embodiments;

Fig. 5 is an exemplary schematic diagram of a data processing system used within the computing system, according to certain embodiments;

Fig. 6 is an exemplary schematic diagram of a processor used with the computing system, according to certain embodiments; and

Fig. 7 is an illustration of a non-limiting example of distributed components which may share processing with the controller, according to certain embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present disclosure encompasses an Automated Control Unit (ACU) configured to facilitate the creation and management of a CO₂-rich injection fluid within a carbon capture and storage framework. The ACU integrates innovative algorithms and sensor technologies to usher in a new era of precision and efficiency in the mineralization of carbon dioxide.

In some embodiments, the ACU improves CO₂ mineralization by offering unprecedented control, precision, and adaptability in the creation, injection, monitoring, and verification of CO₂-rich fluids within reservoirs. The ACU's algorithmic intelligence and integration of real-time data create a holistic ecosystem poised to drive advancements in carbon capture and storage technologies.

Referring now to FIG. 1, a system 100 includes an ACU 102, a water analyzer 104, and an injection device 118. The water analyzer 104 includes measuring devices, such as a sensor system 201, configured to measure parameters, such as fluid characteristics, pressure, temperature, water quality, pH, total dissolved solids (TDS), dissolved gas concentrations including but not limited to CO₂ concentration, electrical conductivity (EC), Oxidation Reduction Potential (ORP) and other physical properties, of injection fluid or samples from a reservoir and/or monitoring wells. The measuring devices are placed strategically within monitoring wells and/or within the reservoir, e.g., at various depths in the reservoir which may correspond with formation conditions (e.g., temperature and/or pressure) or formation composition (e.g., rock composition). The parameters measured by the measuring devices of the water analyzer 104 are sent to the ACU 102, which commences its operation, including generating a CO₂-enriched injection fluid, by diligently assessing the composition of incoming water into the system. Employing an algorithm of advanced computational sophistication, the ACU 102 performs a series of calculations: (i) Determination of CO₂ Dissolution: The ACU 102 computes the amount of CO₂ that can be dissolved within the injection fluid, taking into consideration the parameters measured by the water analyzer 104.

(ii) Tracer Compensation Calculation: An aspect of the invention involves maintaining a predetermined CO₂ to tracer molar ratio. In some embodiments, the predetermined CO₂ to tracer molar ratio is determined based on the type of the tracer used, the detection method used, and the degree of dilution expected in the reservoir over the expected monitoring period. In some embodiments, a predetermined CO₂ to tracer molar ratio may be used in lieu of the predetermined CO₂ to tracer mass ratio. In some embodiments, the predetermined CO₂ to tracer mass ratio is from 0.1 ppm to 1,000 ppm. In some embodiments, the predetermined CO₂ to tracer mass ratio is from 1 ppm to 500 ppm, 10 ppm to 250 ppm or 50-200 ppm. The ACU 102 accurately computes the requisite amount of tracer compensation necessary to uphold this predetermined ratio based on, e.g., injection conditions such as injection rate and/or formation conditions, ensuring meticulous monitoring and verification capabilities. For example, a tracer 112 (e.g., sodium fluorescein) is injected at a specific rate (e.g., 5 mL/min) by the injection device 118. In some embodiments, the injection device 118 includes a dosing pump (DP) to regulate the tracer 112. The tracer is injected into the injection fluid 110 at pressure, in the pipe section between the BPs and the injection well head, using a set of DPs. The dosing rate is regulated throughout the injection process. In some embodiments, the injection device 118 includes an automatic CO₂ gas switchover manifold to stabilize and regulate the pressure of CO₂ injector 114. The pressure of CO₂ injector 114 gradually decreases as the contained gas is depleted and due to the sensitive thermodynamic behavior of gas temperature swings between day and night has a significant impact on injector supply pressure. Further the expansion of the gas through various valves and through the pipes is also associated with temperature changes and consequently pressure changes. To allow for accurate and controlled pressure delivery the manifold regulates the delivery pressure from the CO₂ injector 114 to a selected setpoint. Gas flow is difficult to regulate due to the impact of temperature upstream and downstream pressure on the flow rate. The gas flow controller adjusts the flow to account for all gas flow fluctuation to maintain a stable flow rate of a minimum of 1.3g/s. In some embodiments, Gas Dissolution Module (e.g., Gas Dissolution System of US Published Patent Application US 2023-0038447 Al, which is incorporated herein by reference in its entirety) is used.

(iii) Injection Fluid Performance Improvement: The ACU 102 identifies supplementary substances effective to improve the injection fluid's performance, factoring in a spectrum of parameters to guarantee ideal fluid properties for efficient CO₂ mineralization.

Following the calculations, the ACU 102 orchestrates the dissolution of the precisely programmed quantities of CO₂, tracer, and other supplementary agents within the injection fluid by controlling the injection device 118. This injection fluid is then readied for injection into the designated reservoir. The ACU 102 is configured to compute the water-to-CO₂ mass ratio that ensures the solubility of CO₂ under the specified downhole conditions. This computation is based on feedstock parameters, including water and CO₂ chemistry, as well as the prevailing reservoir conditions. For example, the amount of CO₂ injected is calculated based on a CO₂ solubility curve relating one or more of water pressure, water temperature, conductivity, pH and concentration of other gases. Tire ACU 102 incorporates a model that dynamically adjusts the mixing ratio in response to variations in these input parameters, thereby continuously optimizing the ratio in real-time to account for fluctuations in the feedstock.

Moreover, the ACU 102 regulates the tracer dosing rate in accordance with the adjusted water-to-CO₂ mass ratio, ensuring the maintenance of a constant CO₂-to-tracer mass ratio throughout the process. The ACU 102 also controls the addition of supplementary agents to the injection fluid, which may serve to enhance the solubility of CO₂, provide corrosion inhibition, or prevent fouling. The ACU 102 employs specific algorithms designed to optimize the dosage of these supplementary agents, thereby maximizing their effectiveness based on real-time, in-line measured values. These algorithms enable the ACU 102 to adaptively manage the injection process, ensuring optimal performance under varying operational conditions.

In some embodiments, the system 100 performs tasks such as reservoir monitoring and data acquisition. The reservoir itself becomes a hub of electronic devices measuring fluid chemistry, pressure, temperature, and acoustics. These instruments provide a comprehensive real-time dataset of reservoir behavior, facilitating insights into the dynamic interactions between the injection fluid 110 and the reservoir matrix. Data acquired within the reservoir is transmitted to the ACU 102, creating a closed-loop feedback system for continuous refinement.

In some embodiments, the ACU 102 provides adaptive control and refinement of the control of the injection device. The ACU 102 undertakes a multifaceted role in response to the acquired reservoir data. For example, the ACU 102 observes the phase behavior and dynamics linked to the dissolution of CO₂ from the injection fluid 110 into the reservoir matrix, ensuring best mineralization conditions are maintained. In addition, through analysis of the reservoir data, the ACU 102 accurately establishes the volume of CO₂ that can be dissolved per unit volume of injection fluid 110, bolstering precision in mineralization operations. The ACU 102 compares observed behavior against its calculations and autonomously adapts the CO₂ content within the injection fluid 110, ensuring an alignment between projections and empirical observations. For example, the ACU 102 is configured to measure parameters of an injection fluid and a reservoir fluid with the measuring device, calculate injection data including the amount of CO₂, tracers, and supplementary substances based on the measured parameters, wherein the amount is effective to maintain a predetermined CO₂ to tracer molar ratio in the injection fluid and/or the reservoir fluid, and inject the injection fluid into the subterranean geologic reservoir to manage the CO₂ injection and mineralization process.

In some embodiments, the ACU 102, leveraging the reservoir, well, and injection fluid data, employs an algorithm to perform an array of functions. The ACU 102 utilizes multiple methods, including tracer to CO₂ ratios, acoustic data, and fluid chemistry, are harnessed to accurately quantify the extent of CO₂ mineralization within the reservoir from the injected fluid. In some embodiments, the ACU 102 precisely calculates the rates at which CO₂ is mineralized within the reservoir, providing valuable insights into process kinetics. For example, drawing from historical data and ongoing observations, the ACU 102 predicts the reservoir's future mineralization capacity and rates, facilitating informed decision-making, anticipating maintenance needs, and/or enhancing operational efficiency and longevity. In addition, the ACU 102 harnesses the quantified data to generate carbon credits, providing tangible incentives for environmentally conscious practices.

Referring to FIG. 2, a digital control system 200 to digitally model, monitor, measure, report and verify in-situ CO₂ mineralization in real-time includes a sensor system 201, an injection and monitoring system 203, and a processor 205. The sensor system 201 is disposed strategically in a site (e.g., a subterranean igneous rock formation 246) to measure and/or monitor parameters and conditions such as Surface Conditions 202 (e.g., seismic, near surface CO₂, etc.), Down Well Conditions 204 (e.g., hydrophones, pressure, temperature, injectivity etc.), Well Design 208 (e.g., casing, perforation, depth, size, materials of construction, etc.), and Geological Conditions 206 (e.g., structures, dimensions, etc.).

In some embodiments, the injection and monitoring system 203 includes a CO₂ source

218, a water source 226, an injection fluid mixing chamber 214, an injection well 220, a monitoring well 230. Water from the water source 226 is filtered and processed through a processing device 232 before flowing into an injection fluid chamber 212, where tracers and supplementary agents are mixed in while off gas 222 and waste 228 are separated out. The fluid from the injection fluid chamber 212 flows into the injection fluid mixing chamber 214 to be mixed with CO₂ in an amount effective to mineralize CO₂ in the subterranean igneous rock formation 246 as determined by the processor 200 based on a digital model. In some embodiments, the injection and monitoring system 203 monitors and assesses in-situ mineralization processes. These processes involve the mineralization of CO₂ within the subterranean igneous geological formation 246, entrapment, e.g., according to the digital model. The digital models leverage real-time data from the sensor system 201 and the injection and monitoring system 203, which record a spectrum of parameters encompassing, for example, pressure, temperature, strain, pH, dissolved ions, dissolved gases, tracer elements, and other parameters. The collected data are aggregated into a comprehensive, realtime model, reflecting the dynamic conditions of the subsurface mineralization process. By employing sophisticated algorithms, this model quantifies the extent of CO₂ removal via mineralization, providing insights into the efficacy of the process. In some embodiments, the digital model is a digital twin 234, which is generated by the processor 205 based on well data 236 comprising the aforementioned parameters and conditions. The digital twin harnesses predictive modeling based on historical mineralization data, enabling operators to anticipate operational characteristics by providing operation parameters 238. Moreover, the model can be enhanced by integrating off-line measurement data, further enhancing its accuracy and predictive capabilities. This fusion of real-time and predictive models sets the stage for robust, data-driven decision-making.

In some embodiments, the digital control system 200 provides the digitization of the monitoring, reporting, and verification procedures linked to CO₂ sequestration through in-situ mineralization. The digitized process utilizes the real-time model generated by sensor data, applying it to the quantification of CO₂ removal. This quantification forms the basis for the generation of diverse forms of carbon credits. Carbon credits, in this context, signify tradable certificates symbolizing the reduction or avoidance of specific CO₂ emissions. The generated carbon credits, which considers various standards 240 (e.g., ISO, I-REC, country-specific rules and regulations, etc.\ take various forms, including carbon offsets and removal credits, reflecting different aspects of CO₂ mitigation. The quantified CO₂ removal data is fed into carbon credit agencies' systems. These agencies employ algorithmic methods to verify the extent of CO₂ removal and subsequently generate carbon credits in real-time. The digitized approach provides transparency, accuracy, and efficiency to the carbon credit verification process 242, including but not limited to, GCC, CCS+ Initiative, Puro, Isometric, and other proprietary verification processes.

The real-time model's output contributes to enhance operational practices. By analyzing the collected data and quantifying CO₂ removal, the model offers insights into maximizing mineralization capacity and injectivity. This integration of monitoring, reporting, and verification creates a holistic framework for effective and sustainable CO₂ mitigation. By employing real-time data, digital modeling, predictive analysis, and digitized verification processes, the invention provides a robust framework for CO₂ removal, carbon credit generation, culminating in a comprehensive solution to the global challenge of carbon mitigation.

The digital control system 200 overcomes challenges that arise while attempting to model and digitally monitor CO₂ mineralization due to the complexity of such processes. The intricate interplay of geochemical reactions between CO₂, alkaline minerals, and water in reactive minerals in the subterranean igneous rock formation 246 necessitates cutting-edge automation. The preferred embodiment of the present disclosure recognizes this complexity as an opportunity to pioneer advancements in predictive modeling, data analytics, and realtime monitoring. The automation system is designed to decipher reaction kinetics, adapting parameters instantaneously to real-time conditions, thereby enhancing mineralization dynamics.

In various embodiments, the digital control system 200 provides adaptive systems for geological heterogeneity. The subterranean igneous rock formation 246 is characterized by geological diversity, offering fertile ground for the development of adaptive automation systems. This method and system of the present disclosure seize the opportunity to design systems that can intelligently adapt to the unique characteristics of different rock formations. By tailoring reaction parameters such as CO₂ concentration and CO₂/Water ratios to specific geological attributes and the conditions 202, 204, 206, 208, the system maximizes the overall mineralization efficiency, thus capitalizing on the inherent heterogeneity of the subterranean igneous rock formation 246.

As previously discussed, the digital control system 200 provides real-time monitoring and control utilizing sensor technologies and data transmission techniques. Realizing accurate data capture in real time and its translation into actionable insights lays the foundation for the automation systems that dynamically respond to changing conditions. This ensures that mineralization rates and CO₂ sequestration are conducted efficiently.

In some embodiments, the sensor system 201 includes permeability and flow measurement sensors that address the challenge of carbonate mineral precipitation and clogging by various factors including reacted rocks and formed carbonate minerals (e.g calcite, magnesite, siderite etc.). With algorithms engineered based on fluid dynamics, surface modifications, and flow control mechanisms, the system is engineered to counteract mineral precipitation. This ensures uninterrupted CO₂ flow through the subterranean igneous rock formation 246, preserving high mineralization rates. Referring to FIG. 3 A and 3B, some embodiments of the present disclosure include a carbon credit estimation and management function for the controlled in-situ CO₂ mineralization. FIG. 3A illustrates an exemplary resource management system 300 that balances efficient CO₂ mineralization with responsible resource consumption ensuring efficient carbon credit generation. The resource management system 300 receives a combination of offline data 314 and online data 316 which are aggregated into a digital model. The digital model is then processed by the resource management system 300 to generate control parameters to enhance operations. These control parameters are delivered to site 302 via a direct process 318, 320 and/or through a controlling entity 306 which manages the operation of the site 302. In some embodiments, the online data 316 can be received via an optional single device 304. The resource management system 300 provides a convergence of real-time sensor data, historical records, and predictive algorithms, establishing a cohesive automation framework. By integrating disparate data sources, such as historic data 308, 310, 312 from all sites within the system, the resource management system 300 improves operations by making informed decisions in real-time. This convergence translates into enhanced mineralization rates and effective CO₂ removal. The automation system improves energy, water and mineral utilization, laying the foundation for sustainable practices that resonate with environmental stewardship and operational effectiveness. This invention pioneered the development of methods for verifying and validating CO₂ mineralization progress, instilling confidence in the efficacy of automated systems. Through transparent and reliable quantification techniques, the invention ensures the accuracy of mineralization, CO₂ sequestration, and carbon credit generation, fostering trust within the carbon offset landscape.

As shown in FIG. 3B, the resource management system 300 may also be applied against various standards and/or methodologies 240 and provide the data 314, 316 as well as historic data 308, 310, 312 for verification process 242. In some embodiments, the combined process 322 can involve governments, operators, and/or third-party entities. The resource management system 300 may be integrated with various carbon market products 244 including but not limited to registries, marketplaces, and rating agencies. The issued carbon credits may be provided to the operator 306 and/or carbon market purchasers via a process 324.

The hardware description of the computing environment according to exemplary embodiments is described with reference to FIG. 4. In FIG. 4, a controller 400 is described as representative of the system of some embodiments, including but not limited to ACU 102 or digital control systems 200, 300, in which the controller is a computing device which includes a CPU 401 which performs the processes described above/below. The process data and instructions may be stored in memory 402. These processes and instructions may also be stored on a storage medium disk 404 such as a hard drive (HDD) or portable storage medium or may be stored remotely.

Further, the claims are not limited by the form of the computer-readable media on which the instructions of the inventive process are stored in a hardware system or in a cloudbased storage system. For example, the instructions may be stored on CDs, DVDs, in FLASH memory, RAM, ROM, PROM, EPROM, EEPROM, hard disk or any other information processing device with which the computing device communicates, such as a network based server or cloud-based computers in which the data privacy and data security will be applied In addition there will be a master data management system will also be implemented.

Further, the claims may be provided as a utility application, background daemon, or component of an operating system, or combination thereof, executing in conjunction with CPU 401, 403 and an operating system such as Microsoft Windows 7, Microsoft Windows 10, Microsoft Windows 11, UNIX, Solaris, LINUX, Apple MAC-OS, and other systems known to those skilled in the art.

The hardware elements to achieve the computing device may be realized by various circuitry elements, known to those skilled in the art. For example, CPU 401 or CPU 403 may be a Xenon or Core processor from Intel of America or an Opteron processor from AMD of America, or may be other processor types that would be recognized by one of ordinary skill in the art. Alternatively, the CPU 401, 403 may be implemented on an FPGA, ASIC, PLD or using discrete logic circuits, as one of ordinary skill in the art would recognize. Further, CPU 401, 403 may be implemented as multiple processors cooperatively working in parallel to perform the instructions of the inventive processes described above.

The computing device in FIG. 4 also includes a network controller 406, such as an Intel Ethernet PRO network interface card from Intel Corporation of America or an Internet of Things (loT) based network care, for interfacing and transferring the data within the connected network 460. As can be appreciated, the network 460 can be a public network, such as the Internet, or a private network such as an LAN or WAN network, or any combination thereof and can also include PSTN or ISDN sub-networks. The network 460 can also be wired, such as an Ethernet network, or can be wireless such as a cellular network including EDGE, 3G, 4G and 5G wireless cellular systems. The wireless network can also be WiFi, Bluetooth, or any other wireless form of communication that is known.

The computing device further includes a display controller 408, such as a NVIDIA GeForce GTX or Quadro graphics adaptor from NVIDIA Corporation of America for interfacing with display 410, such as a Hewlett Packard HPL2445w LCD monitor. A general purpose I/O interface 412 interfaces with a keyboard and/or mouse 414 as well as a touch screen panel 416 on or separate from display 410. General purpose I/O interface also connects to a variety of peripherals 418 including printers and scanners, such as an OfficeJet or DeskJet from Hewlett Packard.

A sound controller 420 is also provided in the computing device such as Sound Blaster X-Fi Titanium from Creative, to interface with speakers/mi crophone 422 thereby providing sounds and/or music.

The general purpose storage controller 424 connects the storage medium disk 404 with communication bus 426, which may be an ISA, EISA, VESA, PCI, or similar, for interconnecting all of the components of the computing device. A description of the general features and functionality of the display 410, keyboard and/or mouse 414, as well as the display controller 408, storage controller 424, network controller 406, sound controller 420, and general purpose I/O interface 412 is omitted herein for brevity as these features are known.

The exemplary circuit elements described in the context of the present disclosure may be replaced with other elements and structured differently than the examples provided herein. Moreover, circuitry configured to perform features described herein may be implemented in multiple circuit units (e.g., chips), or the features may be combined in circuitry on a single chipset, as shown on FIG. 5.

FIG. 5 shows a schematic diagram of a data processing system, according to some embodiments, for performing the functions of the exemplary embodiments. The data processing system is an example of a computer in which code or instructions implementing the processes of the illustrative embodiments may be located.

In FIG. 5, data processing system 500 employs a hub architecture including a north bridge and memory controller hub (NB/MCH) 525 and a south bridge and input/output (I/O) controller hub (SB/ICH) 520. The central processing unit (CPU) 530 is connected to NB/MCH 525. The NB/MCH 525 also connects to the memory 545 via a memory bus and connects to the graphics processor 550 via an accelerated graphics port (AGP). The NB/MCH 525 also connects to the SB/ICH 520 via an internal bus (e.g., a unified media interface or a direct media interface). The CPU Processing unit 530 may contain one or more processors and even may be implemented using one or more heterogeneous processor systems.

For example, FIG. 6 shows one implementation of CPU 530. In one implementation, the instruction register 638 retrieves instructions from the fast memory 640. At least part of these instructions are fetched from the instruction register 638 by the control logic 636 and interpreted according to the instruction set architecture of the CPU 530. Part of the instructions can also be directed to the register 632. In one implementation the instructions are decoded according to a hardwired method, and in another implementation the instructions are decoded according to a microprogram that translates instructions into sets of CPU configuration signals that are applied sequentially over multiple clock pulses. After fetching and decoding the instructions, the instructions are executed using the arithmetic logic unit (ALU) 634 that loads values from the register 632 and performs logical and mathematical operations on the loaded values according to the instructions. The results from these operations can be feedback into the register and/or stored in the fast memory 640. According to certain implementations, the instruction set architecture of the CPU 530 can use a reduced instruction set architecture, a complex instruction set architecture, a vector processor architecture, and a very large instruction word architecture. Furthermore, the CPU 530 can be based on the Von Neuman model or the Harvard model. The CPU 530 can be a digital signal processor, an FPGA, an ASIC, a PLA, a PLD, or a CPLD. Further, the CPU 530 can be an x86 processor by Intel or by AMD; an ARM processor, a Power architecture processor by, e.g., IBM; a SPARC architecture processor by Sun Microsystems or by Oracle; or other known CPU architecture. Referring again to FIG. 5, the data processing system 500 can include that the SB/ICH

520 is coupled through a system bus to an I/O Bus, a read only memory (ROM) 556, universal serial bus (USB) port 564, a flash binary input/output system (BIOS) 568, and a graphics controller 558. PCI/PCIe devices can also be coupled to SB/ICH 588 through a PCI bus 562.

The PCI devices may include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. The Hard disk drive 560 and CD-ROM 566 can use, for example, an integrated drive electronics (IDE) or serial advanced technology attachment (SATA) interface. In one implementation the I/O bus can include a super I/O (SIO) device.

Further, the hard disk drive (HDD) 560 and optical drive 566 can also be coupled to the SB/ICH 520 through a system bus. In one implementation, a keyboard 570, a mouse 572, a parallel port 578, and a serial port 576 can be connected to the system bus through the I/O bus. Other peripherals and devices that can be connected to the SB/ICH 520 using a mass storage controller such as SATA or PAT A, an Ethernet port, an ISA bus, a LPC bridge, SMBus, a DMA controller, and an Audio Codec.

Moreover, the present disclosure is not limited to the specific circuit elements described herein, nor is the present disclosure limited to the specific sizing and classification of these elements. For example, the skilled artisan will appreciate that the circuitry described herein may be adapted based on changes on battery sizing and chemistry, or based on the requirements of the intended back-up load to be powered.

The functions and features described herein may also be executed by various distributed components of a system. For example, one or more processors may execute these system functions, wherein the processors are distributed across multiple components communicating in a network. The distributed components may include one or more client and server machines, which may share processing, as shown by FIG. 7, in addition to various human interface and communication devices (e.g., display monitors, smart phones, tablets, personal digital assistants (PDAs)). The network may be a private network, such as a LAN or WAN, or maybe a public network, such as the Internet. Input to the system may be received via direct user input and received remotely either in real-time or as a batch process.

Additionally, some implementations may be performed on modules or hardware not identical to those described. Accordingly, other implementations are within the scope that may be claimed.

Additionally, some aspects of the present disclosure may be performed on modules or hardware not identical to those described. The above-described hardware description is a nonlimiting example of corresponding structure for performing the functionality described herein.

Numerous modifications and variations of the present invention are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims, the invention may be practiced otherwise than as specifically described herein.

Claims

CLAIMS:

1. A system for managing a CO₂ injection and mineralization process within a subterranean geologic reservoir, comprising: an automated control unit (ACU) having a processor and a memory, wherein the processor includes circuitry with program instructions; a measuring device communicatively connected to the processor; and an injection device communicatively connected to the processor, wherein the processor is configured to execute the program instructions, and wherein the program instructions are configured to: measure a plurality of parameters of an injection fluid and a reservoir fluid with the measuring device; calculate a plurality of injection data including a first amount of CO₂, a second amount of a tracer, and a third amount of supplementary substances based on the plurality of parameters, wherein the plurality of injection data is effective to maintain a predetermined CO₂ to tracer mass ratio in the injection fluid and/or the reservoir fluid; and inject the injection fluid into the subterranean geologic reservoir to manage the CO₂ injection and mineralization process.

2. The system of claim 1, wherein the program instructions further comprise: measuring a phase behavior of the reservoir fluid, the phase behavior including a CO₂ mineralization condition after the injecting; determining a fourth amount of dissolved CO₂ in the injection fluid; and adjusting the first amount of CO₂ in the injection fluid based on the phase behavior and the fourth amount of dissolved CO₂.

3. The system of claim 2, wherein the measuring device comprises a plurality of sensors, wherein the plurality of sensors is disposed in one or more injection wells in the subterranean geologic reservoir and one or more monitoring wells in the subterranean geologic reservoir.

4. The system of claim 3, wherein the plurality of parameters includes a fluid chemistry, a pressure, a temperature, data of the reservoir fluid and a monitoring well fluid, and reservoir acoustic data.

5. The system of claim 3, wherein the program instruction further comprises: quantifying a gross CO₂ mineralization and a current CO₂ mineralization rate in the in the subterranean geologic reservoir based on the plurality of injection data and the plurality of parameters; and generating a carbon credit based on the gross CO₂ mineralization and the current CO₂ mineralization rate.

6. The system of claim 5, wherein the program instructions further comprise: predicting a remaining mineralization capacity, a future CO₂ mineralization rate, and a future maintenance need based on the gross CO₂ mineralization, the current CO₂ mineralization rate, the plurality of injection data, and the plurality of parameters.

7. A method for controlling a CO₂ mineralization rate with a CO₂-rich injection fluid for mineralization in a subterranean geologic reservoir, comprising: measuring a plurality of parameters of a reservoir fluid in the subterranean geologic reservoir; calculating a plurality of injection data, including a first amount of CO₂, a second amount of a tracer, and a third number of supplementary substances based on the plurality' of parameters, wherein the plurality of injection data is effective to maintain a predetermined CO₂ to tracer mass ratio in the reservoir fluid; and injecting the CO₂-rich injection fluid into the subterranean geologic reservoir at a controlled CO₂ injection rate to control the CO₂ mineralization rate.

8. The method of claim 7, wherein the method further comprises: measuring a phase behavior of the reservoir fluid, the phase behavior including a CO₂ mineralization condition, after the injecting; determining a fourth amount of dissolved CO₂ in the injection fluid; and adjusting the first amount of CO₂ in the injection fluid based on the phase behavior of the reservoir fluid and the fourth amount of dissolved CO₂.

9. The method of claim 8, wherein the plurality of parameters includes a fluid chemistry, a pressure, a temperature, and an acoustic data of reservoir fluid at one or more injection wells in the subterranean geologic reservoir and a monitoring fluid at one or more monitoring wells in the subterranean geologic reservoir.

10. The method of claim 9, wherein the program instruction further comprises: quantifying a gross CO₂ mineralization and a current CO₂ mineralization rate in the subterranean geologic reservoir based on the plurality of injection data and the plurality of parameters; and generating a carbon credit based on the gross CO₂ mineralization and the current CO₂ mineralization rate.

11. The method of claim 10, wherein the program instructions further comprise: predicting a remaining mineralization capacity and a future CO₂ mineralization rate, and a future maintenance need based on the gross CO₂ mineralization, the current CO₂ mineralization rate, the plurality of injection data, and the plurality of parameters.

12. A digital control method for CO₂ mineralization in a subterranean igneous rock formation, comprising: gathering a plurality of well data from a sensor system installed in the subterranean igneous rock formation; generating a digital model of the subterranean igneous rock formation based on the plurality of well data; determining a plurality of operation parameters for CO₂ mineralization in the subterranean igneous rock formation with the digital model; forming an injection fluid based on the plurality of operation parameters; and injecting the injection fluid into the subterranean igneous rock formation at an operation parameter rate to control the CO₂ mineralization in the subterranean igneous rock formation.

13. The method of claim 12, wherein the plurality of well data includes a plurality of surface conditions, a plurality of down well conditions, a well design, and a plurality of geological conditions.

14. The method of claim 12, wherein the digital model of the subterranean igneous rock formation is a digital twin of the subterranean igneous rock formation.

15. The method of claim 14, wherein the determining further comprises: predicting a future well data based on the digital twin of the subterranean igneous rock formation; and determining the plurality of operation parameters based on the future well data.

16. The method of claim 12, wherein the sensor system is disposed in a plurality of predetermined locations in the subterranean igneous rock formation, wherein the plurality of predetermined locations includes a reactive mineral rich site within the subterranean igneous rock formation, a reactive mineral rich site within the subterranean igneous rock formation, an injection well in the subterranean igneous rock formation, and a monitor well in the subterranean igneous rock formation.

17. The method of claim 12, further comprising: gathering a plurality of updated well data from the sensor system after the injecting; calculating a carbon credit based on the plurality of updated well data; and generating a report based on the plurality of updated well data.

18. The method of 17, wherein the calculating the carbon credit comprises: quantifying a CO₂ removal rate in the subterranean igneous rock formation based on the plurality of updated well data; calculating an amount of CO₂ removal; and calculating the carbon credit including a carbon offset and a removal credit.

19. The method of claim 18, further comprising transmitting the report to a carbon credit agency.

20. The method of claim 12, wherein the subterranean igneous rock formation is a mafic rock formation and/or an ultramafic rock formation.

21. A digital control system for CO₂ mineralization in a subterranean igneous rock formation, comprising: a sensor system installed in the subterranean igneous rock formation; an injection and monitoring system, comprising: a CO₂ source; a water source; an injection fluid mixing chamber fluidly connected to the water source and configured to form an injection fluid; an injection fluid tank fluidly connected to the injection fluid mixing chamber and configured to store the injection fluid; an injection well fluidly connected to the CO₂ source and the injection fluid tank, wherein the injection well is configured to inject the injection fluid to the subterranean igneous rock formation; and a monitor well fluidly connected to the injection fluid mixing chamber and configured to collect a return fluid from the subterranean igneous rock formation; and a processor communicatively connected to the sensor system and the injection and monitoring system, wherein the processor includes circuitry with program instructions configured to monitor, model, and control the CO₂ mineralization in the subterranean igneous rock formation.

22. The system of claim 21, wherein the program instructions are further configured to: gather a plurality of well data from the sensor system installed in the subterranean igneous rock formation; generate a digital model of the subterranean igneous rock formation based on the plurality' of well data; determining a plurality of operation parameters to inject an injection fluid into the subterranean igneous rock formation and mineralize CO₂ in the subterranean igneous rock formation based on the digital model; forming the injection fluid based on the plurality of operation parameters; and injecting the injection fluid into the subterranean igneous rock formation to control CO₂ mineralization in the subterranean igneous rock formation.

23. The system of claim 22, wherein the digital model of the subterranean igneous rock formation is a digital twin of the subterranean igneous rock formation.

24. The system of claim 21, wherein the sensor system is installed in a plurality of predetermined locations in the subterranean igneous rock formation, wherein the plurality of predetermined locations includes a reactive mineral rich site within the subterranean igneous rock formation, a reactive mineral rich site within the subterranean igneous rock formation, one or more injection wells in the subterranean igneous rock formation, and one or more monitor wells in the subterranean igneous rock formation.

25. The system of claim 12, wherein the program instructions are further configured to: predict a future well data based on the digital twin of the subterranean igneous rock formation; determine the plurality of operation parameters based on the future well data gathering a plurality of updated well data from the sensor system after the injecting; calculate a carbon credit based on the plurality of updated well data; and generate a report based on the plurality of updated well data.

26. The system of claim 25, wherein the program instructions are further configured to: quantify a CO₂ removal rate in the subterranean igneous rock formation based on the plurality of updated well data; calculate an amount of CO₂ removal; and calculate the carbon credit including a carbon offset and a removal credit.

27. The system of claim 26, wherein the subterranean igneous rock formation is a mafic rock formation and/or an ultramafic rock formation.