Subsurface CO2 Storage Research Articles

Accelerated Regional Stratigraphic Framework Building for Subsurface CO2 Storage Assessment

Accelerated Regional Stratigraphic Framework Building for Subsurface CO2 Storage Assessment

Offset VSP acquired with a single-use fiber-optic probe: Gorgon CCS case study

There are many challenges associated with monitoring industrial subsurface CO2 storage. Utilizing a reliable strategy for monitoring and assurance is paramount. Numerous aspects and trade-offs need to be considered when designing a cost-effective monitoring solution that must satisfy regulatory and social licensing requirements and minimize operational complexity. Among various geophysical techniques that are included in the suite of carbon capture and storage (CCS) monitoring programs, seismic surveys provide reliable observations of the deep subsurface. However, active surface seismic techniques are expensive, resulting in significant time separation between surveys (several months or years). Downhole seismic approaches combined with fiber-optic sensing provide flexible options in designing on-demand monitoring strategies. Distributed acoustic sensing offers the opportunity to utilize injection wells for downhole time-lapse seismic imaging and microseismic detection, minimizing the impact on production operations. This paper presents the results of a multioffset vertical seismic profiling survey acquired in CO2 injection wells using bare fibers. The fibers were deployed inside the production tubing through FiberLine Intervention technology developed by Well-SENSE. The test utilized two injection wells for active seismic acquisition. The study took place on Barrow Island (Western Australia) as part of the Gorgon CCS project. The study demonstrates the technological advantages of fast deployment of the sensing fiber and the high quality of acquired seismic data. The paper highlights characteristic noise patterns in the recorded data and suggests approaches to attenuate their effects on seismic image quality. Finally, the impact of survey geometry and directional sensitivity of fiber on the recorded wavefield and the quality of seismic imaging is discussed.

SeisRAFT: A recurrent deep learning network for 4D seismic registration and CO2 storage monitoring

The alignment or registration of seismic images is an important and extensively researched aspect in seismic data processing. This process plays a pivotal role in evaluating the evolution of CO2 plumes and assessing the potential for CO2 leakage from its reservoir storage when using 4D or time-lapse seismic methodology. The computer vision community has demonstrated considerable advantages in employing deep learning for such tasks, particularly when confronted with substantial shifts, mismatches, and noises in complex images. In this study, we leverage a cutting-edge recurrent deep learning network known as recurrent all-pairs field transforms (RAFT), incorporating tailored modifications to address seismic matching challenges. We evaluate the network's performance on both synthetic and field 4D CO2 seismic data sets, showcasing its superior accuracy and effectiveness compared to a widely used traditional method. Particularly, in some intricate and ambiguous instances, where even human vision may struggle to identify corresponding events between images, our deep learning method exhibits strong and reliable performance. Validated results confirm that this lightweight multiscale network, trained through self-supervised learning, offers a rapid, parameter-free, and end-to-end solution for 4D image matching in subsurface CO2 storage monitoring with potential applicability to many other seismic registration tasks.

Evolving fill‐and‐spill patterns across linked early post‐rift depocentres control lobe characteristics: Los Molles Formation, Argentina

ABSTRACTInherited rift topography controls the sediment routing, timing of sand supply, and sedimentary linkage of early post‐rift depocentres. Exhumed examples of early post‐rift turbidite systems are rare and previous studies have examined the evolution of individual depocentres; in contrast, the detailed evolution of early post‐rift turbidite systems across multiple depocentres has never been documented. Current fill‐and‐spill models do not detail the stratigraphic architecture and evolution of sedimentological characteristics of multiple intraslope fans developed across topography, including bed type distributions. Here, the evolution of three intraslope fans that developed across two early post‐rift depocentres is documented along an 18 km long transect in the southern Neuquén Basin, Argentina. The relative chronology of sand supply in depocentres is constrained with new U–Pb ages, and sediment source areas with provenance analysis. The early post‐rift intraslope fans record progradation of the system and progressive sedimentary linkage of post‐rift depocentres, transverse to local syn‐rift structures, with sediment routing subparallel to the cratonic basin margin. The large‐scale stratigraphic architecture of intraslope fans indicates an evolution as a fill‐and‐spill system, with initial confinement through flow stripping and overspill to spillover with erosion and bypass across a transverse topographic high separating the depocentres. Changes in early post‐rift intraslope fan characteristics, including thickness, sandstone content, lobe complex stacking patterns, stratal termination patterns and bed type distribution, record changing confinement through time within a depocentre, and spatially across depocentres. The strong spatial and vertical stratigraphic variability of transitional flow deposits and hybrid event beds reflects enhanced erosion, sediment bypass and flow transformation across transverse relief between the two depocentres during the spillover phase. These findings advance current understanding of early post‐rift turbidite systems and refine fill‐and‐spill models, which will help the prediction of spatial and vertical changes in rock quality and connectivity in subsurface hydrocarbon reservoirs and CO2 storage sites.

A Review of Coupled Geochemical–Geomechanical Impacts in Subsurface CO2, H2, and Air Storage Systems

Increased demand for decarbonization and renewable energy has led to increasing interest in engineered subsurface storage systems for large-scale carbon reduction and energy storage. In these applications, a working fluid (CO2, H2, air, etc.) is injected into a deep formation for permanent sequestration or seasonal energy storage. The heterogeneous nature of the porous formation and the fluid–rock interactions introduce complexity and uncertainty in the fate of the injected component and host formations in these applications. Interactions between the working gas, native brine, and formation mineralogy must be adequately assessed to evaluate the efficiency, risk, and viability of a particular storage site and operational regime. This study reviews the current state of knowledge about coupled geochemical–geomechanical impacts in geologic carbon sequestration (GCS), underground hydrogen storage (UHS), and compressed air energy storage (CAES) systems involving the injection of CO2, H2, and air. Specific review topics include (1) existing injection induced geochemical reactions in these systems; (2) the impact of these reactions on the porosity and permeability of host formation; (3) the impact of these reactions on the mechanical properties of host formation; and (4) the investigation of geochemical-geomechanical process in pilot scale GCS. This study helps to facilitate an understanding of the potential geochemical–geomechanical risks involved in different subsurface energy storage systems and highlights future research needs.

Long-term risk assessment of subsurface carbon storage: analogues, workflows and quantification

A key aspect of transferrable oil and gas expertise to subsurface CO 2 storage (SCS) relates to risk assessment. While initial subsurface risk and volume assessments for SCS projects are similar to oil and gas prospect evaluations, full life-cycle risk assessment requires evaluation of the current knowledge of the storage complex and future potential events that may have impacts over long time frames. It is important to learn from past water, gas, and CO 2 injection and storage projects, examples of which are reviewed here. The concepts of aleatory and epistemic risk and uncertainty are discussed, and the use of standard risk matrices for evaluation of long-term, low-frequency but potentially high-impact events is challenged. Drawing on the large volume of previous work, this paper highlights key elements for development of a robust risking framework, including thorough project framing and implementation of a staged approach to project execution. The paper assesses methods for ensuring all potential hazards are captured and addresses the challenges of defining a quantitative risking scale suitable for long-term SCS projects. Example quantitative risk profiles can be used to calculate the timing and duration of ‘Peak Risk’, augment monitoring and mitigation planning for management of risk and capital exposure, and help to ensure successful project outcomes. Thematic collection: This article is part of the Geoscience workflows for CO 2 storage collection available at: https://www.lyellcollection.org/topic/collections/geoscience-workflows-for-CO2-storage

4D microvelocimetry reveals multiphase flow field perturbations in porous media

Many environmental and industrial processes depend on how fluids displace each other in porous materials. However, the flow dynamics that govern this process are still poorly understood, hampered by the lack of methods to measure flows in optically opaque, microscopic geometries. We introduce a 4D microvelocimetry method based on high-resolution X-ray computed tomography with fast imaging rates (up to 4 Hz). We use this to measure flow fields during unsteady-state drainage, injecting a viscous fluid into rock and filter samples. This provides experimental insight into the nonequilibrium energy dynamics of this process. We show that fluid displacements convert surface energy into kinetic energy. The latter corresponds to velocity perturbations in the pore-scale flow field behind the invading fluid front, reaching local velocities more than 40 times faster than the constant pump rate. The characteristic length scale of these perturbations exceeds the characteristic pore size by more than an order of magnitude. These flow field observations suggest that nonlocal dynamic effects may be long-ranged even at low capillary numbers, impacting the local viscous-capillary force balance and the representative elementary volume. Furthermore, the velocity perturbations can enhance unsaturated dispersive mixing and colloid transport and yet, are not accounted for in current models. Overall, this work shows that 4D X-ray velocimetry opens the way to solve long-standing fundamental questions regarding flow and transport in porous materials, underlying models of, e.g., groundwater pollution remediation and subsurface storage of CO2 and hydrogen.

A conceptual CO2 fill-and-spill mega-fairway in the UK Southern North Sea: A new approach to identify and optimise large-scale underground carbon storage (CCS)

This work explores the subsurface CO2 storage potential in UKCS Quadrants 43–44, by integrating 3D seismic interpretation, regional wellbore data and CO2 migration modelling. We show 11 Bunter Sandstone domes linked together in a fill-and-spill “Bunter Mega-Fairway”. These traps host a theoretical storage capacity of 8.4 Gt CO2 (≈3–4 times the filled-to-spill theoretical capacity of the proposed Endurance storage site), while lessening CO2 leakage risks.Within the mega-fairway, the Triassic Bunter Sandstone reservoir shows a P10-P90 thickness of 78–219 m, along with moderate-high porosities (11–28 %) and permeabilities (9–669 mD). A regionally-connected reservoir is suggested from spatially consistent, near-hydrostatic aquifer pressure gradients (≈0.51 psi/ft). The top-seal is laterally effective and structural closures are lower than the CO2 columns necessary to breach the seal.At the mega-fairway storage depths, CO2 would inhabit the reservoir as a supercritical fluid, enabling it to naturally migrate buoyantly from one trap to another, away from strategically-placed injector sites. Through the integration of migration spill-point modelling, multi-physics seabed monitoring nodes and fill-and-spill injection management, the maximum storage capacity could be achieved, while alleviating CO2 leakage hazards and potentially decreasing the number of injector wells.Exploiting fill-and-spill fairways for CCS is a new concept with vast worldwide applicability.

Effective microseismic monitoring of the Quest CCS site, Alberta, Canada

Microseismic monitoring represents a key surveillance technology to verify the integrity of subsurface CO2 storage sites. The precise location of microseismic events is first and foremost a direct and immediate indication of caprock and seal behavior but could also provide insight into CO2 plume migration. Tiny precursor movements provide diagnostic information about injection-related reservoir and caprock dynamics long before potential seal failure occurs. We present a case study from the Quest CCS facility in Canada, where a variety of different monitoring technologies are employed. We present the different microseismic sensor technologies and array configurations currently installed at the site and compare them against each other with respect to their reliability and effectiveness in providing the required verification information.

Integrated containment risks assessment for subsurface CO2 storage: Overburden analysis and top seal integrity study, offshore Norway

This study summarizes the OASIS (Overburden Analysis and Seal Integrity Study for CO2 Sequestration in the North Sea) project that focuses on assessing the containment risks associated with the geological carbon sequestration (CCS) of the Northern Lights project. CCS is viewed as one of the most effective solutions for reducing carbon emissions, as it captures carbon dioxide from point sources and permanently stores it in suitable geological formations. However, injecting CO2 into the subsurface may have mechanical consequences, including fault reactivation, top seal fracturing, surface heave, etc. This study proposes an interdisciplinary workflow to characterize the caprock, faults, and overburden associated with CCS projects in the Horda Platform area, to improve injection-induced containment risk assessment. Our findings show that the proposed workflows and tools effectively characterize stress-related mechanical hazards. However, due to the complex nature of rocks, it is challenging to evaluate the top seal integrity using a single method. Therefore, the proposed interdisciplinary approach is more effective for any fluid injection site's characterization, given the complex nature of the subsurface and its behavior under injection-induced stress changes. This research paper adds knowledge about the top seal integrity assessment and the reliability of injected CO2, making CCS projects more reliable and safer. Although this study focuses on the northern North Sea, the proposed methods are equally applicable globally to characterize subsurface CO2 storage sites. Apart from CCS projects, these research results can benefit other subsurface injection projects, such as water injection for reservoir management, wastewater injection for disposal, hydrogen storage, and hydraulic fracturing for unconventional hydrocarbon resources.

Exploring CO2 storage potential in Lithuanian deep saline aquifers using digital rock volumes: a machine learning guided approach

The increasing significance of carbon capture, utilization and storage (CCUS) as a climate mitigation strategy has underscored the importance of accurately evaluating subsurface reservoirs for CO2 sequestration. In this context, digital rock volumes, obtained through advanced imaging techniques such as micro-Xray computed tomography (MXCT), offer intricate insights into the porous and permeable structures of geological formations. This study presents a comprehensive methodology for assessing CO2 storage viability within Lithuanian deep saline aquifers, namely Syderiai and Vaskai, by utilizing petrophysical properties estimated from digital rock volumes of samples from analogous formations. It also demonstrates the potential of integrating advanced imaging techniques, machine learning, and numerical modeling for accurate assessment and effective management of subsurface CO2 storage.

Bi-Objective Optimization of Subsurface CO2 Storage with Nonlinear Constraints Using Sequential Quadratic Programming with Stochastic Gradients

Summary This study focuses on carbon capture, utilization, and sequestration (CCUS) via the means of nonlinearly constrained production optimization workflow for a CO2-enhanced oil recovery (EOR) process, in which both the net present value (NPV) and the net present carbon tax credits (NPCTC) are bi-objectively maximized, with the emphasis on the consideration of injection bottomhole pressure (IBHP) constraints on the injectors, in addition to field liquid production rate (FLPR) and field water production rate (FWPR), to ensure the integrity of the formation and to prevent any potential damage during the life cycle injection/production process. The main optimization framework used in this work is a lexicographic method based on the line-search sequential quadratic programming (LS-SQP) coupled with stochastic simplex approximate gradients (StoSAG). We demonstrate the performance of the optimization algorithm and results in a field-scale realistic problem, simulated using a commercial compositional reservoir simulator. Results show that the workflow can solve the single-objective and bi-objective optimization problems computationally efficiently and effectively, especially in handling and honoring nonlinear state constraints imposed onto the problem. Various numerical settings have been experimented with to estimate the Pareto front for the bi-objective optimization problem, showing the trade-off between the two objectives of NPV and NPCTC. We also perform a single-objective optimization on the total life cycle cash flow, which is the aggregated quantity of NPV and NPCTC, and quantify the results to further emphasize the necessity of performing bi-objective production optimization, especially when used in conjunction with commercial flow simulators that lack the capability of computing adjoint-based gradients.

Exploring CO2 storage potential in Lithuanian deep saline aquifers using digital rock volumes: a machine learning guided approach

The increasing significance of carbon capture, utilization and storage (CCUS) as a climate mitigation strategy has underscored the importance of accurately evaluating subsurface reservoirs for CO2 sequestration [1]. In this context, digital rock volumes, obtained through advanced imaging techniques such as micro-Xray computed tomography (MXCT), offer intricate insights into the porous and permeable structures of geological formations [2]. This study presents a comprehensive methodology for assessing CO2 storage viability within Lithuanian deep saline aquifers, namely Syderiai and Vaskai, by utilizing petrophysical properties estimated from digital rock volumes [3, 4]. These petrophysical properties were derived from core samples collected from these formations. Utilizing machine learning algorithms, porosity was estimated while the Lattice Boltzmann method (LBM) was applied to determine permeability [5]. The methodology employed for estimating these petrophysical parameters was initially validated using samples from formations analogous to Lithuanian formations. Subsequently, it was applied to rock samples specifically obtained from Lithuanian formations. The estimated petrophysical properties were compared with peer-reviewed data from published literature. When fluids such as CO2 or H2 are injected into sub-surface reservoirs, they can alter pore and grain characteristics. Therefore, it is crucial to extract representative element volumes (REVs) from segmented volumes to study the impact of fluids on porosity and their distribution [6]. These mini models, representing small portions of the larger formation, assist in predicting fluid flow within the formation, which is vital for assessing the efficiency and safety of carbon capture and storage (CCS) operations. Subsequently, numerical modelling was conducted using the petrophysical parameters as inputs to assess the storage capacity of the Lithuanian formations using tNavigator software [7]. This research contributes to an enhanced understanding of pore space distribution and its role in various aspects of long-term CO2 storage. It also demonstrates the potential of integrating advanced imaging techniques, machine learning, and numerical modeling for accurate assessment and effective management of subsurface CO2 storage. This study shall aid in enhanced understanding of pore space distribution and their contribution towards various aspects of long-term storage. The results can be extended to study the geochemical reactions and geo-mechanical behaviour of the rocks. Such studies shall further facilitate identification of reservoir(s) wherein sequestration potential can be reliably explored.

Early marine carbonate cementation in a relict shelf-edge coral reef North of Miami, Florida: Pseudo-coupled depositional and diagenetic modeling

Carbonate depositional and early diagenetic processes overlap in time because of the high chemical reactivity of calcite. Previous studies have modeled early diagenesis in a static depositional framework. They followed a rule-based modeling approach instead of applying rigorous thermodynamics and reaction kinetics.This study has developed a pseudo-coupled approach based on a dynamic depositional framework and reactive transport modeling for diagenesis. It has been tested for the “Pompano” reef north of Miami, Florida. The model includes five reef parasequences deposited over 9.0 kyr with five facies zones. After deposition of the first parasequence, diagenetic processes are modeled. The resulting framework forms the starting condition for depositing the next parasequence. Subsequently, its diagenetic overprint and the continued overprint in the underlying parasequence(s) are modeled. These workflow steps are repeated until all five parasequences are covered.The results show the spatial and temporal development of calcite cementation and porosity distribution. During the reef catch-up phase, cement mainly developed at the depositional surface (up to 10% of the total sediment volume). During the keep-up phase, cement abundance increases to a maximum value of ∼17%. After the reef's demise, cement forms a crust at the sediment-water interface with up to 38% of the total sediment volume. In general, controlling factors of cement distribution include facies-specific flow velocities, reef facies, relative sea-level changes, depositional gaps, and seawater salinity.The modeling results of this study are well aligned with the actual cement and porosity distribution in the Pompano reef and with other sub-recent and ancient examples described in the literature. Pseudo-coupled depositional-diagenetic modeling represents a suitable approach for quantitative porosity prediction in comparable depositional and diagenetic settings. It reduces uncertainties in prediction at inter-well to prospect scale for oil and gas exploration, geothermal exploration, and subsurface storage of CO2.

Analytical solutions for brine invasion across caprock and leakage through an abandoned well in CO2 storage sites

Success in subsurface CO2 storage requires a trapping mechanism that inhibits the flow of storage layer fluid. Due to the wide prevalence of abandoned wells across geological formations, unwanted leakage events of brine and CO2 may occur if old wells near storage sites are not evaluated accurately. The abandoned wells, with time, may become conducive to flow if a sufficient pressure gradient is realized. Classical analytical models theorized abandoned well problems based on Theis’s solution and proposed strategies to find radial coordinates of the abandoned wells. However, in those formulations, brine invasion from storage to the surrounding formation is either disregarded or assumed as a vertical-only flow. In this study, we report a novel mathematical model that accounts for both brine invasion and abandoned well flow. The leakage rate across the abandoned well segment is evaluated from three contributing pressure terms: injection- and the associated brine invasion-induced pressure build-up, leakage-induced pressure drawdown in the storage layer, and leakage-induced pressure build-up in the overlying aquifer. We evaluate the average radial pressure from a two-dimensional coupled flow problem by applying Laplace and Hankel transforms. The obtained solutions in the Hankel-Laplace space were inverted back to the radial-time domain by employing the integration-summation-extrapolation and Stehfest method, respectively. We verified the developed solution against the classical solution analytically and graphically. We observe that Theis’s solution always overestimates the radial pressure as the pressure is diffusing in the lateral direction only. We identified that the abandoned well leakage rate decreases with the increase of permeability ratio, and the degree of reduction is higher in late times. We measured the degree of overestimation in leakage rates compared to classical solutions. A maximum leakage rate is observed for open flow conditions. The abandoned well leakage rate reaches its maximum early when the permeability ratios are higher. The proposed work can be employed as a base solution in old-well leakage modeling to determine the radial coordinate of the abandoned wells with a higher degree of certainty.

Basalt mineral surface charge and the effect of mineralization on its colloidal stability: Implications of subsurface CO2 storage

Global concern over climate change caused by greenhouse gas emissions has received and is still receiving a lot of attention. Companies and nations have committed to reducing their carbon footprint to halt the consequences, and work is being done to create long-term strategies for CO2 usage. One of these goals is to use the CO2 that has been captured to make chemicals or to use it to make waste that can be buried or safely disposed of. One such endeavours is CO2 mineralization, which has been suggested as a method of CO2 utilization. It entails the creation of carbonates by the interaction of a fluid that contains CO2 with cations that are found in brines or rocks. However, the mineralization process is a protracted one, and until recently it was not believed that it could be completed in less than two years. Furthermore, little research has been done on how mineralization affects the electrokinetic and surface chemistry of cation-bearing rocks like basaltic rocks. Therefore, this study assesses how mineralization (carbonate precipitation) affects the colloidal and electrokinetic characteristics of basaltic rock.According to the study's findings, the electrical double layer (EDL) effect and protonation of the silanol group function as the two main controlling mechanisms of charge formation in the basaltic rock. More so, the rock sample is negatively charged over a range of pH and colloidally stable in freshwater. However, its colloidal stability is decreased in brines, and the EDL effect and potential determining ions (H+ and OH–) dominate its charge development. Additionally, different precipitates have distinct effects on the electrokinetic characteristics of basaltic rock, with the MgCO3 and FeCO3 precipitation having a more notable impact than the CaCO3 precipitation. Furthermore, at pH values of 3 – 4 (CO2 storage pH) in both freshwater and sweater environments, basaltic rocks exhibit near zero or low surface charge values indicating weak adhesion force. Thus, the question of if the near pH values of 2, can be considered as higher values of surface charge is observed in some cases. These findings raise questions about the structural integrity of basaltic rock, which has been touted as a prime candidate for the mineralization process due to its reactive nature, particularly in the case of colloidal stability after the mineralization process. The results of this study further provide new insight into the optimization of the mineralization process, which is dependent on the structural and petrophysical characteristics of the rocks and reservoirs that provide the cations for the process.

Imaging and Modeling the Impact of Multi‐Scale Pore Connectivity on Two‐Phase Flow in Mixed‐Wet Rock

AbstractThe wetting properties of pore walls have a strong effect on multiphase flow through porous media. However, the fluid flow behavior in porous materials with both complex pore structures and non‐uniform wettability are still unclear. Here, we performed unsteady‐state quasi‐static oil‐ and water‐flooding experiments to study multiphase flow in two sister heterogeneous sandstones with variable wettability conditions (i.e., one natively water‐wet and one chemically treated to be mixed‐wet). The pore‐scale fluid distributions during this process were imaged by laboratory‐based X‐ray micro‐computed tomography (micro‐CT). In the mixed‐wet case, we observed pore filling events where the fluid interface appeared to be at quasi‐equilibrium at every position along the pore body (13% by volume), in contrast to capillary instabilities typically associated with slow drainage or imbibition. These events corresponded to slow displacements previously observed in unsteady‐state experiments, explaining the wide range of displacement time scales in mixed‐wet samples. Our new data allowed us to quantify the fluid saturations below the image resolution, indicating that slow events were caused by the presence of microporosity and the wetting heterogeneity. Finally, we investigated the sensitivity of the multi‐phase flow properties to the slow filling events using a state‐of‐the‐art multi‐scale pore network model. This indicated that pores where such events took place contributed up to 19% of the sample's total absolute permeability, but that the impact on the relative permeability may be smaller. Our study sheds new light on poorly understood multiphase fluid dynamics in complex rocks, of interest to for example, groundwater remediation and subsurface CO2 storage.

Seeing through the CO2plume: Joint inversion-segmentation of the Sleipner 4D seismic data set

Time-lapse (4D) seismic inversion is the leading method to quantitatively monitor fluid-flow dynamics in the subsurface, with applications ranging from enhanced oil recovery to subsurface CO2storage. The process of inverting 4D seismic data for reservoir properties is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data and inaccuracies in the repeatability of 4D acquisition surveys. Consequently, ad-hoc regularization strategies are essential for the 4D seismic inverse problem to obtain geologically meaningful subsurface models and associated 4D changes. Motivated by recent advances in the field of convex optimization, we propose a joint inversion-segmentation algorithm for 4D seismic inversion that integrates total variation and segmentation priors as a way to counteract missing frequencies and present noise in 4D seismic data. The proposed inversion framework is designed for poststack seismic data and applied to a pair of seismic volumes from the open Sleipner 4D seismic data set. Our method has three main advantages over state-of-the-art least-squares inversion methods. First, it produces high-resolution baseline and monitor acoustic models. Second, it mitigates nonrepeatable noise and better highlights real 4D changes by leveraging similarities between multiple data. Finally, it provides a volumetric classification of the acoustic impedance 4D difference model (4D changes) based on user-defined classes (i.e., percentages of speedup or slowdown in the subsurface). Such advantages may enable more robust stratigraphic/structural and quantitative 4D seismic interpretation and provide more accurate inputs for dynamic reservoir simulations. Alongside presenting our novel inversion method, we introduce a streamlined data preprocessing sequence for the 4D Sleipner poststack seismic data set that includes time-shift estimation and well-to-seismic tie. Finally, we provide insights into the open-source framework for large-scale optimization that we used to implement the proposed algorithm in an efficient and scalable manner.

Variability in acoustic backscatter and fish school abundance at a shallow water CCS site

Carbon capture and storage (CCS) is one approach used to reduce carbon dioxide (CO2) emissions to the atmosphere. Subsea CO2 storage sites must have low risk of leakage and have minimal impact on the marine environment. Single beam echosounders (SBES) are a good technology for detecting CO2 leakage in the water column for application in a marine Measurement, Monitoring and Verification (MM&V) framework. Characterisation of active acoustic data is needed to determine whether levels of water column backscatter enable CO2 gas leaks to be detected, and to understand acoustic false positives caused by naturally occurring acoustic signals (e.g., fish schools). We used acoustic data collected by SBES fitted to two mobile and two fixed sensor platforms over four years to characterise the site of the proposed CarbonNet CCS Project subsurface CO2 storage reservoir. We observed diel and seasonal variability in water column backscatter and fish school abundance. Water column backscatter was highest at night and in Austral summer and autumn periods but was at a level that would allow small CO2 gas leaks to be detected. Fish abundance was highest during the day and in Austral summer and autumn. To reduce the likelihood of acoustic false positives, surveys designed to detect CO2 gas leaks using active acoustic technologies could prioritise data collection at night and during the months where fish school abundance is lowest. Understanding these sources of natural variability can guide survey strategies and response actions.

Informed reduction of the geological data on rock material interfaces in subsurface CO2 storage reservoirs

Recent developments in subsurface data capturing technologies have presented the opportunity of representing high-resolution information in field scale reservoir models of CO2 geo-sequestration sites. While the wealth of geological data is critical for the accurate prediction of CO2 flow and trapping in the subsurface, it often presents computational challenges. This is especially true for the representation of rock, or lithological, interfaces which are small-scale features in reservoirs and significantly control CO2 migration and trapping. It is important to reduce this information to ensure that numerical simulations are completed within a reasonable time. However, care must be taken to ensure that none of the key lithological interface associations are lost during data reduction. This study presents a machine learning based approach for expressing the amount of rock interfaces characteristic of sedimentary CO2 storage reservoirs in terms of a reduced number of principal components which capture the necessary geological information. The workflow is applied to a high-resolution reservoir model of the Paaratte Formation, Otway Basin, Australia, which is coastal to shallow marine siliciclastic reservoir. The information in the reservoir model was captured using 922 k rock interfaces. However, the algorithm predicts that the same information could be represented using only 7 principal lithological interface associations. The outcomes are validated using multiphase flow simulations which show the uniqueness of these principal components in terms of their impact on CO2 flow and trapping.