Optimizing CO2 Sequestration Monitoring: A Bayesian Approach to Efficient Well Placement

Conceptos Básicos

An efficient algorithm for strategically placing a limited number of monitoring wells to effectively track CO2 plumes and mitigate risks in carbon sequestration projects.

Resumen

The content presents a methodology called BEACON (Bayesian Experimental design Acceleration with Conditional Normalizing flows) for optimizing the placement of monitoring wells in CO2 sequestration projects. The key highlights are:

The approach integrates fluid-flow solvers for forecasting plume trajectories with generative neural networks (normalizing flows) for plume inference uncertainty. This allows the algorithm to adaptively place wells in locations that provide the most information gain.

The well placement is formulated as a joint optimization problem, where the training of the normalizing flow and the optimization of the well location probability density are performed simultaneously.

The algorithm is designed to be integrated into a Digital Twin workflow, enabling continuous refinement of well placement strategies based on incoming field observations. This allows the approach to evolve alongside the project's life cycle.

In a synthetic case study using realistic permeability models, the optimized well placements outperformed randomly selected well locations, demonstrating reduced uncertainty in plume inferences and lower error rates.

The method is extensible to three-dimensional domains and is developed within a Bayesian framework for optimal experimental design, ensuring scalability and mathematical optimality.

Estadísticas

"CO2 sequestration is a crucial engineering solution for mitigating climate change."
"Given the high costs associated with drilling, it is crucial to strategically place a limited number of wells to ensure maximally effective monitoring within budgetary constraints."
"We use a realistic case study to verify these claims by demonstrating our method's application in a large scale domains and optimal performance as compared to baseline well placement."
"In our synthetic case study, we employed realistic samples for permeability models derived from the Compass dataset."

Citas

"Our approach for selecting well locations integrates fluid-flow solvers for forecasting plume trajectories with generative neural networks for plume inference uncertainty."
"We combine training of a normalizing flow fθ and optimization of probability density of well locations into the joint optimization."
"We denote our general algorithm BEACON: Bayesian Experimental design Acceleration with COnditional Normalizing flows."

Ideas clave extraídas de

BEACON

by Rafael Orozc... a las arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00075.pdf

Consultas más profundas

How can the BEACON algorithm be extended to incorporate other types of monitoring data, such as seismic imaging, to further improve the accuracy of CO2 plume tracking?

Incorporating seismic imaging data into the BEACON algorithm can enhance the accuracy of CO2 plume tracking by providing additional insights into subsurface structures and fluid flow dynamics. One way to extend BEACON is by integrating seismic imaging data as an input to the generative neural network, allowing it to learn the relationships between seismic responses and CO2 plume behavior. This integration can enable the algorithm to leverage seismic data to refine its predictions and reduce uncertainties in plume tracking.
By including seismic imaging data in the training process, BEACON can learn to associate seismic signatures with specific plume characteristics, such as migration patterns and saturation levels. This additional information can help improve the algorithm's ability to infer the underlying subsurface properties and predict the behavior of CO2 plumes more accurately. Furthermore, by optimizing the placement of seismic sensors in conjunction with monitoring wells, BEACON can maximize the information gained from both types of data, leading to more robust and reliable monitoring strategies for CO2 sequestration projects.

What are the potential limitations or challenges in applying the BEACON algorithm in real-world CO2 sequestration projects with complex geological and operational conditions?

While the BEACON algorithm offers significant advantages in optimizing well placement for CO2 sequestration monitoring, several limitations and challenges may arise when applying it in real-world projects with complex geological and operational conditions. One key challenge is the computational complexity associated with simulating fluid flow dynamics in heterogeneous subsurface environments. Real-world geological formations often exhibit complex structures and properties that can impact the accuracy of plume tracking predictions.
Another limitation is the availability and quality of input data required for training the algorithm. Inaccurate or incomplete data on subsurface properties, fluid behavior, or monitoring well locations can lead to suboptimal results and uncertainties in the algorithm's predictions. Additionally, the scalability of the algorithm to large-scale projects with numerous monitoring points and varying operational constraints may pose challenges in terms of computational resources and optimization efficiency.
Moreover, the integration of multiple data sources, such as seismic imaging, well data, and geological models, introduces additional complexities in data fusion and interpretation. Ensuring the consistency and compatibility of diverse data types within the algorithm framework can be a non-trivial task, especially in the presence of uncertainties and noise in the data.

How can the insights gained from the optimal well placement strategy using BEACON be leveraged to inform the overall design and management of CO2 sequestration projects, including the selection of suitable storage sites and the development of comprehensive monitoring and risk mitigation plans?

The insights obtained from the optimal well placement strategy using BEACON can play a crucial role in informing the overall design and management of CO2 sequestration projects, from site selection to risk mitigation planning. By identifying the most informative locations for monitoring wells, the algorithm can guide decision-makers in choosing suitable storage sites with favorable geological characteristics for CO2 injection and storage.
Furthermore, the optimized well placement strategy can help in developing comprehensive monitoring plans that maximize the effectiveness of CO2 plume tracking while minimizing operational costs. By strategically situating monitoring wells based on the algorithm's recommendations, project managers can enhance their ability to detect and mitigate potential risks, such as leakage or induced seismicity, in a timely manner.
Moreover, the data-driven approach of BEACON can support the development of risk mitigation plans by providing insights into the uncertainties associated with CO2 plume behavior and subsurface properties. By incorporating these insights into risk assessment frameworks, project stakeholders can better anticipate and address potential challenges, ensuring the safe and efficient operation of CO2 sequestration projects.
Overall, leveraging the insights gained from BEACON's optimal well placement strategy can enhance the overall design, monitoring, and risk management of CO2 sequestration projects, contributing to their long-term sustainability and success.