Conventional reservoir models often overlook micro-scale heterogeneity because standard well logs and core plug data lack the resolution to capture fine-scale features. This omission reduces the precision of reservoir characterization, particularly in thin sandstone layers where reservoir quality is critical for CO₂ sequestration. To address this limitation, high-resolution micro-CT imaging and digital core analysis (DCA) were combined with upscaling algorithms to bridge the gap between pore-scale properties and field-scale reservoir simulations. This study applied the upscaled data from digital core analysis to improve reservoir characterisation and modelling for CO₂ injection projects in the Otway formation. Micro-CT imaging provided detailed three-dimensional pore structures, and pore network modelling was used to generate relative permeability and capillary pressure functions. Then, these properties were upscaled to reservoir scale and incorporated into simulation models. History matching was performed using multiple field datasets, including seismic CO₂ plume shapes, CO₂ saturations from neutron logs, and pressure buildup from a single-phase water injection test. The results demonstrated three key advantages of DCA over conventional laboratory core experiments. First, DCA significantly reduced uncertainty in the measurement of rock properties, leading to more accurate predictions of CO₂ plume behaviour. Second, DCA enabled the testing of individual rock types within facies associations, and their effects were captured in upscaled properties for large-scale simulations. Third, plume simulations based on upscaled DCA data reproduced the observed seismic plume, with a more accurate vertical profile and reduced lateral extent compared with earlier simulations using traditional SCAL data. Overall, this work highlights the value of integrating micro-CT imaging, DCA, and upscaling into reservoir modelling. The approach provides a more reliable representation of reservoir heterogeneity and improves the accuracy of CO₂ storage forecasts, offering a practical pathway for enhancing the safety and efficiency of geological sequestration projects.

 

Reliable estimation of CO₂–water relative permeability is essential for predicting injectivity and storage performance in saline aquifers. Yet, standard laboratory protocols are often complicated by mineral reactions and fines migration. In this study, we combine controlled coreflooding experiments, porous plate desaturation, and microscopic visualisation to evaluate how CO₂–water–rock interactions alter flow functions.

For Berea sandstone, the injection of CO₂-saturated water triggered mineral dissolution and fines mobilisation, confirmed by ICP–OES analysis (Ca²⁺, Fe³⁺, Mg²⁺ release) and SEM–EDS imaging of pore alteration and fines precipitation. These reactions produced a 21–48% reduction in CO₂ relative permeability and significant injectivity decline with increasing pore volumes of CO₂-saturated water injected. By contrast, experiments on sintered glass cores showed negligible changes, underscoring the mineralogical control. 

A porous plate method was applied to carefully desaturate cores at constant pressure, reducing water saturation in a controlled manner and enabling direct measurement of maximum CO₂ relative permeability under drainage conditions. This approach was coupled with conventional unsteady-state flooding. The porous plate desaturation technique also demonstrated that at water saturations below ~0.34, experimental limitations arise; however, it provides a more representative upper bound for CO₂ flow capacity than conventional extrapolations. Taken together, our results show that coupling visualisation with refined saturation control reveals how pore-scale reactions govern injectivity and relative permeability.

This integrated workflow advances laboratory protocols by mitigating artefacts, improving the reliability of relative permeability functions, and informing the design of CO₂ geostorage projects.

 

The Otway GeoCquest Field Validation experiment involved the injection of approximately 10,000 tonnes of supercritical CO₂-rich gas (80 mol% CO₂ and 20 mol% CH₄) into the lithologically heterogeneous Paaratte Formation Parasequence 2 within the onshore Otway Basin at a depth of approximately 1.5 kilometres. One of the major objectives of this field-scale test was to study the role of small-scale geological heterogeneities, such as petrophysical thin beds composed of low-permeability, low-porosity intraformational baffles. We assessed how these features control the vertical and lateral distribution of the CO₂ plume across different geological layers and influence overall plume-migration dynamics.

To monitor plume behaviour, a high-frequency pulsed-neutron logging (PNL) program was conducted in the dedicated passive monitoring well, CRC-8, over a five-month period, with multiple passes acquired daily during both injection and post-injection phases. A time-lapse differencing approach applied to baseline and monitoring datasets enabled detection of subtle changes in log responses and their spatiotemporal evolution with high fidelity. An integrated thermodynamics-based petrophysical framework was developed that combines different PNL measurements to quantify changes in saturation across the reservoir while accounting for measurement noise, depth mismatches, and borehole environmental effects.

Results indicate rapid migration and early gas breakthrough along preferential high-permeability streaks, together with clear evidence that small-scale heterogeneities strongly influence CO₂ plume distribution. Geological features such as heterolithic intervals and grain-size variability were found to control capillary entry pressures, resulting in capillary-heterogeneity-driven flow patterns and non-uniform saturation distributions at the field scale. Additionally, sedimentary structures including clay-rich thin beds, cross-bedding, and low-angle sandstone beds with carbonaceous laminae exerted further control on plume geometry during both injection and post-injection phases. Overall, these findings highlight the dominant role of small-scale heterogeneities in governing plume migration and provide critical insights for advancing carbon sequestration monitoring technologies and improving the reliability of predictive models.

Understanding how geological heterogeneity influences CO₂ plume behaviour is critical for designing secure and efficient storage projects in saline aquifers. Features such as intraformational baffles, heterolithic intervals, and subtle facies transitions can significantly impact plume migration by slowing vertical rise, enhancing lateral spread, or promoting trapping mechanisms. Yet in low dip, low relief structural settings, a key challenge remains: to what extent can these small scale heterogeneities offset buoyancy driven migration?

This study presents a pre-injection geological modelling workflow for a small saline aquifer CO₂ storage site (<6 km²) targeting a thin reservoir (~50 m). With seismic data resolution insufficient to resolve internal architecture, we relied on high resolution core descriptions, image logs, and stratigraphic interpretation to characterize key depositional elements. Particular attention was given to low permeability features such as heterolithic baffles and sharp facies transitions that may affect vertical CO₂ movement.

These heterogeneities were incorporated into a 3D static model using stochastic methods to represent the variability in facies connectivity and lateral discontinuities. Dynamic simulations assessed the associated CO₂ plume behaviours, ranging from slow, fingering migration controlled by intraformational baffles to rapid channelised flow through connected high permeability streaks. 

Results highlight that in low-relief structural settings; even subtle heterogeneity can significantly influence plume architecture and migration pathways. This was validated through the correlation with high resolution saturation logs. Incorporating realistic geological complexity enhanced our ability to anticipate plume behaviour, guiding injection strategy, monitoring design, and contingency planning for uncertain migration outcomes. This work reinforces the value of integrating detailed sedimentological analysis with geological modelling to better constrain reservoir performance, reduce geological uncertainty, and support safer and more reliable CO₂ storage outcomes.

The successful storage of CO2 in saline aquifers requires careful selection of injection scenarios that maximize storage capacity whilst minimizing leakage risks. The limited data on these fields means the challenge lies in optimizing development strategies whilst accounting for uncertainty. This study proposes an automated workflow to generate an ensemble of hydrodynamic-geomechanical models and optimize potential injection locations for efficient carbon storage.

The workflow was applied to the Smeaheia open dataset, creating multiple plausible models by varying key reservoir and geomechanical parameters. A neural network algorithm was then run across this ensemble to identify injection strategies that maximise CO₂ storage whilst maintaining geomechanical integrity. 

Results show that effective well location and perforation schemes can be identified across a wide range of possible scenarios. Compared to deterministic optimization, this uncertainty approach reduces the likelihood of selecting injection locations that appear optimal in one case but are high risk under alternative scenarios. 

This work demonstrates the value of combining automated optimization with uncertainty analysis in coupled fluid-geomechanical models. The methodology highlighted in this study offers a transferable framework for designing injection strategies that remain effective under geological and geomechanical uncertainty. 

CO₂ geological storage is a promising strategy for mitigating global carbon emissions, with reservoir porosity and permeability being key parameters for assessing storage potential. However, characterizing the spatial distribution of these properties at the field scale presents a major challenge due to data scarcity and heterogeneity. Conventional methods often struggle to effectively integrate discrete well-log and core measurements with continuous seismic data, while common machine learning approaches tend to neglect crucial geological controls like diagenetic processes. This limitation often leads to models with poor generalization beyond the training data.To overcome these challenges, we propose a novel multi-source, multi-scale data fusion approach for predicting porosity and permeability in tight sandstone reservoirs. Our method integrates high-precision core data with comprehensive well-log and seismic data, leveraging multiple machine learning algorithms to enhance the predictive power and generalization of the model. Taking the Upper Paleozoic Shiqianfeng Formation in the Ordos Basin as a case study, we demonstrate that this fusion approach significantly improves the accuracy of reservoir property prediction at the field scale.The results provide a more reliable foundation for evaluating storage potential and forecasting injection capacity in commercial-scale CO₂ geological storage projects. This study addresses the common issue of limited well and core data in seismic work areas, offering a robust and intelligent solution for reservoir characterization.