Advancing Carbon Sequestration through Smart Proxy Modeling: Leveraging Domain Expertise and Machine Learning for Efficient Reservoir Simulation

Geological carbon sequestration (GCS) offers a promising solution to effectively manage extra carbon, mitigating the impact of climate change. This doctoral research introduces a cutting-edge Smart Proxy Modeling-based framework, integrating artificial neural networks (ANNs) and domain expertise, to re-engineer and empower numerical reservoir simulation for efficient modeling of CO2 sequestration and demonstrate predictive conformance and replicative capabilities of smart proxy modeling. Creating well-performing proxy models requires extensive human intervention and trial-and-error processes. Additionally, a large training database is essential to ANN model for complex tasks such as deep saline aquifer CO2 sequestration since it is used as the neural network\u27s input and output data. One major limitation in CCS programs is the lack of real field data due to a lack of field applications and issues with confidentiality. Considering these drawbacks, and due to high-dimensional nonlinearity, heterogeneity, and coupling of multiple physical processes associated with numerical reservoir simulation, novel research to handle these complexities as it allows for the creation of possible CO2 sequestration scenarios that may be used as a training set. This study addresses several types of static and dynamic realistic and practical field-base data augmentation techniques ranging from spatial complexity, spatio-temporal complexity, and heterogeneity of reservoir characteristics. By incorporating domain-expertise-based feature generation, this framework honors precise representation of reservoir overcoming computational challenges associated with numerical reservoir tools. The developed ANN accurately replicated fluid flow behavior, resulting in significant computational savings compared to traditional numerical simulation models. The results showed that all the ML models achieved very good accuracies and high efficiency. The findings revealed that the quality of the path between the focal cell and injection wells emerged as the most crucial factor in both CO2 saturation and pressure estimation models. These insights significantly contribute to our understanding of CO2 plume monitoring, paving the way for breakthroughs in investigating reservoir behavior at a minimal computational cost. The study\u27s commitment to replicating numerical reservoir simulation results underscores the model\u27s potential to contribute valuable insights into the behavior and performance of CO2 sequestration systems, as a complimentary tool to numerical reservoir simulation when there is no measured data available from the field. The transformative nature of this research has vast implications for advancing carbon storage modeling technologies. By addressing the computational limitations of traditional numerical reservoir models and harnessing the synergy between machine learning and domain expertise, this work provides a practical workflow for efficient decision-making in sequestration projects

Characterising the Pore Space of Selected Sandstone Samples using Multiple Approaches

A comprehensive knowledge of the porosity and pore size distribution (PSD) of hydrocarbon reservoirs is vital to several petroleum engineering disciplines including reserve estimation, reservoir characterisation, drilling operations and reservoir development planning. This work examines the three methods of Mercury Injection Capillary Pressure (MICP) Testing, Pore Network Modelling (PNM) and Nuclear Magnetic Resonance (NMR) which are currently used within the petroleum industry to determine representative measures of porosity and PSD. Although MICP is a common method used within the petroleum industry, several factors impact its suitability for determining porosity and PSD. These are related to the destructive nature of the test which is challenging when samples are limited in quantity as well as the limitations of MICP to provide robust results for certain kinds of reservoir material, particularly for those that are unconsolidated and unconventional. Recent advances in PNM and NMR have made these approaches attractive alternatives for pore evaluation studies which can enhance, supplement or replace the information derived from MICP testing. To examine the applicability of PNM and NMR methods to determine porosity and PSD, three sandstone core samples were used throughout this study. These were the Berea and Bentheimer core samples, which are consolidated and homogenous in nature allowing an opportunity for the benchmark testing of the PNM and NMR approaches and an Athabasca Oil Sand (AOS) sample, which is a prime example of unconsolidated material containing a very viscous in-situ fluid. During the PNM process, micro-computed tomography (micro-CT) was used to obtain 2D contiguous images of a sample which were then compiled to produce a 3D representation of the pore space. Based on the literature, a 12-step comprehensive PNM approach was developed in this work and applied to the benchmark Berea and Bentheimer core samples to derive their porosity and PSD. This had a substantial processing time of over 100 hours (> 4 days) for each sample. The key findings from this comprehensive approach formed the basis of a simplified recommended PNM practice having only 9 steps and an anticipated processing time of 7 hours and 26 hours for homogenous and heterogeneous samples respectively. This simplified recommended PNM practice was then applied to the AOS sample with the porosity and PSD results showing a good agreement to that from the MICP and NMR testing. The determination of porosity and PSD from NMR testing requires specific fluids to be contained in the pore space. This generally involves the removal and replacement of all original fluids with water (or brine) since the response of the low-viscosity water correlates well with surface measurements of the pore space. This typically precludes the testing of samples imbued with their original fluids which poses several restrictions for the NMR testing of unconsolidated and partially consolidated material. The development of techniques which allow for the robust pore space testing of these kinds of materials without the cleaning or removal of their native fluids is therefore valuable to the petroleum industry. Based on these ideas, a novel empirical transform was developed which could allow the NMR testing of samples containing viscous fluids. This transform used the NMR response of glycerol (which is 1,412 times more viscous than water at 20oC) to develop a transform based on viscosity. The use of this transform showed great success in obtaining a robust PSD for the AOS sample containing its native bitumen which is comparable to the PSDs from the MICP and PNM approaches. These results indicate that the PNM and NMR approaches can provide comparable results to conventional MICP testing, that they can be used as independent techniques for evaluating the pore space and that they can provide a robust measurement of the porosity and PSD for samples imbued with their native hydrocarbon fluids. When compared to MICP testing, these approaches might be preferred when testing a limited quantity of core samples, partially consolidated and unconsolidated samples and samples containing their original fluids. Future work to strengthen these results include using a wider range of sandstone samples to test the developed recommended PNM practice and using a wider range of samples containing a greater variety of fluids to test the empirical transform

Remote sensing, numerical modelling and ground truthing for analysis of lake water quality and temperature

Freshwater accounts for just 2.5% of the earth’s water resources, and its quality and availability are becoming an issue of global concern in the 21st century. Growing human population, over-exploitation of water sources and pressures of global warming mean that both water quantity and quality are affected. In order to effectively manage water quality there is a need for increased monitoring and predictive modelling of freshwater resources. To address these concerns in New Zealand inland waters, an approach which integrates biological and physical sciences is needed. Remote sensing has the potential to allow this integration and vastly increase the temporal and spatial resolution of current monitoring techniques, which typically involve collecting grab-samples. In a complementary way, lake modelling has the potential to enable more effective management of water resources by testing the effectiveness of a range of possible management scenarios prior to implementation. Together, the combination of remote sensing and modelling data allows for improved model initialisation, calibration and validation, which ultimately aid in understanding of complex lake ecosystem processes. This study investigated the use of remote sensing using empirical and semi-analytical algorithms for the retrieval of chlorophyll a (chl a), tripton, suspended minerals (SM), total suspended sediment (SS) and water surface temperature. It demonstrated the use of spatially resolved statistical techniques for comparing satellite estimated and 3-D simulated water quality and temperature. An automated procedure was developed for retrieval of chl a from Landsat Enhanced Thematic Mapper (ETM+) imagery, using 106 satellite images captured from 1999 to 2011. Radiative transfer-based atmospheric correction was applied to images using the Second Simulation of the Satellite in the Solar Spectrum model (6sv). For the estimation of chl a over a time series of images, the use of symbolic regression resulted in a significant improvement in the precision of chl a hindcasts compared with traditional regression equations. Results from this investigation suggest that remote sensing provides a valuable tool to assess temporal and spatial distributions of chl a. Bio-optical models were applied to quantify the physical processes responsible for the relationship between chl a concentrations and subsurface irradiance reflectance used in regression algorithms, allowing the identification of possible sources of error in chl a estimation. While the symbolic regression model was more accurate than traditional empirical models, it was still susceptible to errors in optically complex waters such as Lake Rotorua, due to the effect of variations of SS and CDOM on reflectance. Atmospheric correction of Landsat 7 ETM+ thermal data was carried out for the purpose of retrieval of lake water surface temperature in Rotorua lakes, and Lake Taupo, North Island, New Zealand. Atmospheric correction was repeated using four sources of atmospheric profile data as input to a radiative transfer model, MODerate resolution atmospheric TRANsmission (MODTRAN) v.3.7. The retrieved water temperatures from 14 images between 2007 and 2009 were validated using a high-frequency temperature sensor deployed from a mid-lake monitoring buoy at the water surface of Lake Rotorua. The most accurate temperature estimation for Lake Rotorua was with radiosonde data as an input into MODTRAN, followed by Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2, Atmospheric Infrared Sounder (AIRS) Level 3, and NASA data. Retrieved surface water temperature was used for assessing spatial heterogeneity of surface water temperature simulated with a three-dimensional (3-D) hydrodynamic model (ELCOM) of Lake Rotoehu, located approximately 20 km east of Lake Rotorua. This comparison demonstrated that simulations reproduced the dominant horizontal variations in surface water temperature in the lake. The transport and mixing of a geothermal inflow and basin-scale circulation patterns were inferred from thermal distributions from satellite and model estimations of surface water temperature and a spatially resolved statistical evaluation was used to validate simulations. This study has demonstrated the potential of accurate satellite-based thermal monitoring to validate water surface temperature simulated by 3-D hydrodynamic models. Semi-analytical and empirical algorithms were derived to determine spatial and temporal variations in SS in Lake Ellesmere, South Island, New Zealand, using MODIS band 1. The semi-analytical model and empirical model had a similar level of precision in SS estimation, however, the semi-analytical model has the advantage of being applicable to different satellite sensors, spatial locations, and SS concentration ranges. The estimations of SS concentration (and estimated SM concentration) from the semi-analytical model were used for a spatially resolved validation of simulations of SM derived from ELCOM-CAEDYM. Visual comparisons were compared with spatially-resolved statistical techniques. The spatial statistics derived from the Map Comparison Kit allowed a non-subjective and quantitative method to rank simulation performance on different dates. The visual and statistical comparison between satellite estimated and model simulated SM showed that the model did not perform well in reproducing both basin-scale and fine-scale spatial variation in SM derived from MODIS satellite imagery. Application of the semi-analytical model to estimate SS over the lifetime of the MODIS sensor will greatly extend its spatial and temporal coverage for historical monitoring purposes, and provide a tool to validate SM simulated by 1-D and 3-D models on a daily basis. A bio-optical model was developed to derive chl a, SS concentrations, and coloured dissolved organic matter /detritus absorption at 443 nm, from MODIS Aqua subsurface remote sensing reflectance of Lake Taupo, a large, deep, oligotrophic lake in North Island, New Zealand. The model was optimised using in situ inherent optical properties (IOPs) from the literature. Images were atmospherically corrected using the radiative transfer model 6sv. Application of the bio-optical model using a single chl a-specific absorption spectrum (a*ϕ(λ)) resulted in low correlation between estimated and observed values. Therefore, two different absorption curves were used, based on the seasonal dominance of phytoplankton phyla with differing absorption properties. The application of this model resulted in reasonable agreement between modelled and in situ chl a concentrations. Highest concentrations were observed during winter when Bacillariophytes (diatoms) dominated the phytoplankton assemblage. On 4 and 5 March 2004 an unusually large turbidity current was observed originating from the Tongariro River inflow in the south-east of the lake. In order to resolve fine details of the plume, empirical relationships were developed between MODIS band 1 reflectance (250 m resolution) and SS estimated from MODIS bio-optical features (1 km resolution) were used estimate SS at 250 m resolution. Complex lake circulation patterns were observed including a large clockwise gyre. With the development of this bio-optical model MODIS can potentially be used to remotely sense water quality in near real time, and the relationship developed for B1 SS allows for resolution of fine-scale features such turbidity currents

Innovation Of Petrophysical And Geomechanical Experiment Methodologies: The Application Of 3D Printing Technology

The petrophysical and geomechanical properties of rocks link the geology origin with engineering practice, which serves as the fundamental of various disciplinaries associated with subsurface porous media, including civil engineering, underground water, geological exploration, and petroleum engineering. The research methodologies can be mainly divided into three aspects: theoretical modelling, numerical simulation, and experiments, in which the last approach plays a critical role that can support, validate, calibrate, or even refute a hypothesis. Only replying on repeatable trials and consolidate analysis of precise results can the experiments be successful and convincing, though uncertainties, due to multiple factors, need to be scrutinized and controlled. The challenges also existed in the characterization and measurements of rock properties as a result of heterogeneity and anisotropy as well as the inevitable impact of experimental operation. 3D printing, a cutting-edge technology, was introduced and utilized in the study that is supposed to be capable of controlling the mineralogy, microstructure, physical properties of physical rock replicas and further benefit the petrophysical and geomechanical experimental methodologies. My PhD research project attempted to answer the questions from the standpoint of petrophysicisits and geomechanics scientist: Can 3D printed rocks replicate natural rocks in terms of microstructure, petrophysical and geomechanical properties? If not, by any means can we improve the quality of replicas to mimic the common rock types? Which 3D printing method is best suitable for our research purposes? How could it be applied in the conventional experiments and integrated with theoretical calculation or numerical simulation? Three main types of printing materials and techniques (gypsum, silica sand, resin) were characterized first individually, which demonstrated varying microstructure, anisotropy, petrophysical and geomechanical properties. Post-processing effect was examined on the 3D printed gypsum rocks that show impact differences on nanoscale and microscale pore structures. Through comparison, resin, the material used in stereolithography technology, best suits the reconstruction of intricate pore network that aims to complement digital rock physics and ultimately be applied in petrophysical research. Gypsum material, however, has been proved as the best candidate for geomechanical research spanning from reference samples to upscaling methods validation. Currently, a practical approach of utilizing 3D printing in petroleum geoscience is taking advantages of the characteristics we focus on the research while disregarding the other properties, by which a suitable 3D printing material and technique can emerge

Issues in Evaluating Health Department Web-Based Data Query Systems: Working Papers

Compiles papers on conceptual and methodological topics to consider in evaluating state health department systems that provide aggregate data online, such as taxonomy, logic models, indicators, and design. Includes surveys and examples of evaluations

In situ study of granular micromechanics in semi-solid carbon steels

The granular micromechanics of semi-solid steel at ∼80% solid are studied by synchrotron radiography. A particulate soil mechanics approach to image analysis shows that deformation occurs by the translation and rotation of quasi-rigid grains under the action of contact forces, and that the changes in directional fabric and grain–grain contacts occur by mechanisms similar to those of highly compacted soils including “locked sands”. Grain-scale phenomena are then linked to the macroscopic displacement and strain fields and it is shown that shear-induced dilation is a fundamental response at both the grain and macro scales. Based on this, recommendations are made on future rheology experiments

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that