The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Management
- Data Science & Engineering Analytics
Filters
Journal
- Loading...
Conference
- Loading...
Publisher
- Loading...
Theme
- Loading...
Author
- Loading...
Concept Tag
- Loading...
Source
- Loading...
File Type
- Loading...
Site News
Goto
No map tile layer
| Layer | Fill | Outline |
|---|
Map layers
| Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
|---|
| Fill | Stroke |
|---|---|
Collaborating Authors
Physics Informed Surrogate Model Development in Predicting Dynamic Temporal and Spatial Variations During CO2 Injection into Deep Saline Aquifers
Tariq, Zeeshan (King Abdullah University of Science &Technology) | Yan, Bicheng (King Abdullah University of Science &Technology) | Sun, Shuyu (King Abdullah University of Science &Technology)
Abstract Geological Carbon Sequestration (GCS) in deep geological formations, like saline aquifers and depleted oil and gas reservoirs, brings enormous potential for large-scale storage of carbon dioxide (CO2). The successful implementation of GCS requires a comprehensive risk assessment of the confinement of plumes at each potential storage site. The accurate prediction of the flow, geochemical, and geomechanical responses of the formation is essential for the management of GCS in long-term operations because excessive pressure buildup due to injection can potentially induce fracturing of the cap-rock, or activate pre-existing faults, through which fluid can leak. In this study, we build a Deep Learning (DL) workflow to effectively infer the storage potential of CO2 in deep saline aquifers. Specifically, a reservoir model is built to simulate the process of CO2 injection into deep saline aquifers, which considers the coupled phenomenon of flow and hydromechanics. Further, the reservoir model was sampled to account for a wide range of petro-physical, geological, and operational parameters. These samples generated a massive physics-informed simulation database (about 1500 simulated data points) that provides training data for the DL workflow. The ranges of varied parameters were obtained from an extensive literature survey. The DL workflow consists of Fourier Neural Operator (FNO) to take the input of the parameterized variables used in the simulation database and jointly predict the temporal-spatial responses of pressure and CO2 saturation plumes at different periods. Average Absolute Percentage Error (AAPE) and coefficient of determination (R), Structural similarity index (SSIM), and Peak Signal to Noise Ratio (PSNR) are used as error metrics to evaluate the performance of the DL workflow. Through our blind testing experiments, the DL workflow offers predictions as accurate as our physics-based reservoir simulations, yet 300 times more efficient than the latter. The developed workflow shows superior performance with an AAPE of less than 5% and R score of more than 0.99 between actual and predicted values. The workflow can predict other required outputs that numerical simulators can typically calculate, such as solubility trapping, mineral trapping, and injected fluid densities in supercritical and aqueous phases. The proposed DL workflow is not only physics informed but also driven by inputs and outputs (data-driven) and thus offers a robust prediction of the carbon storage potential in deep saline aquifers with considering the coupled physics and potential fluid leakage risk.
abdulraheem, Artificial Intelligence, asia government, chemical flooding methods, climate change, co 2, deep learning, denmark government, development, enhanced recovery, europe government, Exhibition, machine learning, Mahmoud, Modeling & Simulation, neural network, norway government, permeability, Petroleum Engineer, prediction, Reservoir Characterization, reservoir simulation, Saline Aquifer, saturation, saudi arabia annual technical symposium, seismic surveying, society, structural geology, subsurface storage, Tariq, united states government, Upstream Oil & Gas, workflow
Country:
- North America > United States (0.68)
- Asia > Middle East (0.47)
- Europe > Denmark > North Sea (0.28)
- Europe > Norway > Norwegian Sea (0.24)
Genre:
- Research Report > Experimental Study (0.68)
- Research Report > Strength High (0.54)
- Research Report > New Finding (0.49)
Oilfield Places:
- Europe > Denmark > North Sea > Danish Sector > Central Graben > Block 5604/29 > South Arne Field (0.99)
- Asia > India > Andhra Pradesh > Bay of Bengal > Krishna-Godavari Basin > Ravva Block > Ravva Field (0.99)
SPE Disciplines:
Technology:
Deep Learning Models for the Prediction of Mineral Dissolution and Precipitation During Geological Carbon Sequestration
Tariq, Zeeshan (King Abdullah University of Science & Technology) | Yildirim, Ertugrul Umut (King Abdullah University of Science & Technology) | Yan, Bicheng (King Abdullah University of Science & Technology) | Sun, Shuyu (King Abdullah University of Science & Technology)
Abstract In Geological Carbon Sequestration (GCS), mineralization is a secure carbon dioxide (CO2) trapping mechanism to prevent possible leakage at later stage of the GCS project. Modeling of the mineralization during GCS relies on numerical reservoir simulation, but the computational cost is prohibitively high due to the complex physical processes. Therefore, deep learning (DL) models can be used as a computationally cheaper and at the same time, reliable alternative to the conventional numerical simulators. In this work, we have developed a DL approach to effectively predict the dissolution and precipitation of various important minerals, including Anorthite, Kaolinite, and Calcite during CO2 injection into deep saline aquifers. We established a reservoir model to simulate the process of geological CO2 storage. About 750 simulations were performed in order to generate a comprehensive dataset for training DL models. Fourier Neural Operator (FNO) models were trained on the simulated dataset, which take the reservoir and well properties along with time information as input and predict the precipitation and dissolution of minerals in space and time scales. During the training process, root-mean-squared-error (RMSE) was chosen as the loss function to avoid overfitting. To gauge prediction performance, we applied the trained model to predict the concentrations of different mineral on the test dataset, which is 10% of the entire dataset, and two metrics, including the average absolute percentage error (AAPE) and the coefficient of determination (R) were adopted. The R value was found to be around 0.95 for calcite model, 0.94 for Kaolinite model, and 0.93 for Anorthite model. The R was calculated for all trainable points from the predictions and ground truth. On the other hand, the average AAPE for all the mappings was calculated around 1%, which demonstrates that the trained model can effectively predict the temporal and spatial evolution of the mineral concentrations. The prediction CPU time (0.2 seconds/case) by the model is much lower than that of the physics-based reservoir simulator (3600 seconds/case). Therefore, the proposed method offers predictions as accurate as our physics-based reservoir simulations, while provides a huge saving of computation time. To the authors' best knowledge, prediction of the precipitation and dissolution of minerals in a supervised learning approach using the simulation data has not been studied before in the literature. The DL models developed in this study can serve as a computationally faster alternative to conventional numerical simulators to assess mineralization trapping in GCS projects especially for the mineral trapping mechanism.
abdulraheem, Anorthite, Artificial Intelligence, asia government, chemical flooding methods, climate change, co 2, concentration, deep learning, enhanced recovery, Exhibition, fno, kaolinite, machine learning, mineral, mole fraction, neural network, permeability, Petroleum Engineer, prediction, Reservoir Characterization, reservoir simulation, simulator, society, structural geology, subsurface storage, Tariq, united states government, Upstream Oil & Gas
Country:
- Asia > Middle East (0.29)
- North America > United States (0.28)
Oilfield Places: Asia > India > Andhra Pradesh > Bay of Bengal > Krishna-Godavari Basin > Ravva Block > Ravva Field (0.99)
SPE Disciplines:
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
Deep-Learning-Based Surrogate Model to Predict CO2 Saturation Front in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach
Tariq, Zeeshan (King Abdullah University of Science and Technology) | Xu, Zhen (King Abdullah University of Science and Technology) | Gudala, Manojkumar (King Abdullah University of Science and Technology) | Yan, Bicheng (King Abdullah University of Science and Technology) | Sun, Shuyu (King Abdullah University of Science and Technology)
Abstract Naturally fractured reservoirs (NFRs), such as fractured carbonate reservoirs, are ubiquitous across the worldwide and are potentially very good source to store carbondioxide (CO2) for a longer period of time. The simulation models are great tool to assess the potential and understanding the physics behind CO2-brine interaction in subsurface reservoirs. Simulating the behavior of fluid flow in NFR reservoirs during CO2 are computationally expensive because of the multiple reasons such as highly-fractured and heterogeneous nature of the rock, fast propagation of CO2 plume in the fracture network, and high capillary contrast between matrix and fractures. This paper presents a data-driven deep learning surrogate modeling approach that can accurately and efficiently capture the temporal-spatial dynamics of CO2 saturation plumes during injection and post-injection monitoring periods of Geological Carbon Sequestration (GCS) operations in NFRs. We have built a physics-based numerical simulation model to simulate the process of CO2 injection in a naturally fractured deep saline aquifers. A standalone package was developed to couple the discrete fracture network in a fully compositional numerical simulation model. Then reservoir model was sampled using the Latin-Hypercube approach to account for a wide range of petrophysical, geological, reservoir, and operational parameters. The simulation model parameters were obtained from extensive geological surveys published in literature. These samples generated a massive physics-informed database (about 900 simulations) that provides sufficient training dataset for the Deep Learning surrogate models. Average Absolute Percentage Error (AAPE) and coefficient of determination (R) were used as error metrics to evaluate the performance of the surrogate models. The developed workflow showed superior performance by giving AAPE less than 5% and R more than 0.95 between ground truth and predictions of the state variables. The proposed Deep Learning framework provides an innovative approach to track CO2 plume in a fractured carbonate reservoir and can be used as a quick assessment tool to evaluate the long term feasibility of CO2 movement in fractured carbonate medium.
Artificial Intelligence, asia government, carbonate reservoir, co 2, complex reservoir, dataset, deep learning, denmark government, equation, europe government, fractured reservoir, machine learning, neural network, permeability, Petroleum Engineer, porosity, prediction, proceedings, reservoir, Saline Aquifer, saturation, society, ssim, storage, Upstream Oil & Gas
Country:
- Asia > Middle East (0.46)
- Europe > Denmark > North Sea (0.28)
Oilfield Places: Europe > Denmark > North Sea > Danish Sector > Central Graben > Block 5604/29 > South Arne Field (0.99)
SPE Disciplines: Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
Application of Image Processing Techniques in Deep-Learning Workflow to Predict CO2 Storage in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach
Tariq, Zeeshan (King Abdullah University of Science and Technology) | Yan, Bicheng (King Abdullah University of Science and Technology) | Sun, Shuyu (King Abdullah University of Science and Technology)
Abstract Naturally fractured reservoirs (NFRs), such as fractured carbonate reservoirs, are commonly located worldwide and have the potential to be good sources of long-term storage of carbon dioxide (CO2). The numerical reservoir simulation models are an excellent source for evaluating the likelihood and comprehending the physics underlying behind the interaction of CO2 and brine in subsurface formations. For various reasons, including the rock's highly fractured and heterogeneous nature, the rapid spread of the CO2 plume in the fractured network, and the high capillary contrast between matrix and fractures, simulating fluid flow behavior in NFR reservoirs during CO2 injection is computationally expensive and cumbersome. This paper presents a deep-learning approach to capture the spatial and temporal dynamics of CO2 saturation plumes during the injection and monitoring periods of Geological Carbon Sequestration (GCS) sequestration in NFRs. To achieve our purpose, we have first built a base case physics-based numerical simulation model to simulate the process of CO2 injection in naturally fractured deep saline aquifers. A standalone package was coded to couple the discrete fracture network in a fully compositional numerical simulation model. Then the base case reservoir model was sampled using the Latin-Hypercube approach to account for a wide range of petrophysical, geological, reservoir, and decision parameters. These samples generated a massive physics-informed database of around 900 cases that provides a sufficient training dataset for the DL model. The performance of the DL model was improved by applying multiple filters, including the Median, Sato, Hessian, Sobel, and Meijering filters. The average absolute percentage error (AAPE), root mean square error (RMSE), Structural similarity index metric (SSIM), peak signal-to-noise ratio (PSNR), and coefficient of determination (R) were used as error metrics to examine the performance of the surrogate DL models. The developed workflow showed superior performance by giving AAPE less than 5% and R more than 0.94 between ground truth and predicted values. The proposed DL-based surrogate model can be used as a quick assessment tool to evaluate the long-term feasibility of CO2 movement in a fracture carbonate medium.
Artificial Intelligence, asia government, co 2, complex reservoir, Conference, dataset, deep learning, denmark government, Denote, europe government, fractured reservoir, machine learning, neural network, permeability, Petroleum Engineer, porosity, proceedings, reservoir, reservoir simulation, rqi dataset, Saline Aquifer, saturation, Sequestration, society, subsurface storage, Upstream Oil & Gas
Country:
- Asia > Middle East (0.68)
- Europe > Denmark > North Sea (0.28)
Oilfield Places: Europe > Denmark > North Sea > Danish Sector > Central Graben > Block 5604/29 > South Arne Field (0.99)
SPE Disciplines:
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
Data-Driven Machine Learning Approach to Predict the Trapping Indices in Coupled Chemo-Hydro-Mechanical Model of CO2 Sequestration into Deep Saline Aquifer
Tariq, Zeeshan (King Abdullah University of Science and Technology) | Yan, Bicheng (King Abdullah University of Science and Technology) | Sun, Shuyu (King Abdullah University of Science and Technology)
Abstract Storing carbon dioxide (CO2) in deep geological formations, such as saline aquifers and depleted oil and gas reservoirs, through Geological Carbon Sequestration (GCS) offers tremendous potential for large-scale CO2 storage. However, ensuring the successful implementation of GCS requires a thorough evaluation of the risks associated with confinement of plumes and storage capacity at each storage location. To gain a better understanding of how CO2 is trapped in saline aquifers, it is important to create robust and speedy tools for assessing CO2 trapping efficiency. Therefore, this study focuses on using machine learning techniques to predict the efficiency of CO2 trapping in deep saline formations as part of Geological Carbon Sequestration (GCS). The methodology involves simulating the CO2 trapping mechanisms using a physics-based numerical reservoir simulator and creating training, testing, and validation datasets based on uncertainty variables. The study used a numerical reservoir simulator to simulate CO2 trapping mechanisms over 170 years, with uncertainty variables like petrophysical properties, reservoir physical parameters, and operational decision parameters being utilized to create a large dataset for training, testing, and validation. The study identified key control variables through feature importance index calculation and utilized the Latin-Hypercube approach to account for a wide range of parameters. 722 reservoir simulations were performed and the results of residual trapping, mineral trapping, solubility trapping, and cumulative CO2 injection were analyzed. The outliers and extreme data points were removed using statistical and exploratory data analysis techniques. Deep neural network was applied to predict the CO2 trapping efficiency. The results showed that the deep neural network model can predict the trapping indices with a coefficient of determination above 0.95 and average absolute percentage error below 5%. These findings suggest that machine learning models can serve as a more efficient alternative to traditional numerical simulation for estimating the performance of CO2 trapping in GCS projects.
AAPE, Artificial Intelligence, carbon dioxide, climate change, co 2, data-driven machine learning approach, dataset, deep learning, Effectiveness, geological carbon sequestration, machine learning, mineral, neural network, permeability, Reservoir Characterization, RMSE, Saline Aquifer, saturation, Sequestration, Simulation, solubility, structural geology, subsurface storage, Tariq, united states government, Upstream Oil & Gas
SPE Disciplines:
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Health, Safety, Environment & Sustainability > Environment > Climate change (1.00)
- (2 more...)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
Save or update focal point
Save a search / focal point to access it again.
Thank you!