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This study introduces an innovative physics-informed machine learning approach to predict the cumulative oil production, CO2 stored, and CO2 adsorbed during CO2 injection. The developed model can be utilized to maximize both oil recovery and CO2 storage in naturally fractured reservoirs (NDR). While existing research has largely focused on maximizing oil recovery and net present value (NPV) through CO2 injection, our methodology integrates comprehensive physics data to fine-tune gas injection processes. We consider a wide span of reservoir characteristics in five-spot injection pattern. We developed dual-permeability, naturally fractured numerical compositional models incorporating sorption to simulate flow mechanisms during injection accurately. Using Langmuir's adsorption dynamics in our simulation integrates well designs and injection strategies. Data from these simulations were trained using Artificial Neural Networks (ANN) to create a robust production prediction proxy model for different gas injection designs. The ANN model demonstrates high accuracy, reflecting its potential for optimizing oil production and carbon storage. The developed model facilitated the screening and optimization process for the considered system as it provides the optimized design in a few seconds compared to days. This study not only highlights the cost-efficiency of using machine learning algorithms in optimization processes but also deepens our understanding of their potential applications in enhanced oil recovery (EOR) and carbon capture and storage (CCUS). The developed models can be used to provide new insights into the interactions between fluid compositions and the complex physical phenomena of sorption, underscoring the transformative potential of integrating advanced machine learning with traditional petroleum engineering techniques.
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Hybrid Huff-n-Puff Process for Enhanced Oil Recovery: Integration of Surfactant Flooding with CO2 Oil Swelling
With increasing energy demands and depleting oil accessibility in reservoirs, the investigation of more effective enhanced oil recovery (EOR) methods for deep and tight reservoirs is imminent. This study investigates a novel hybrid EOR method, a synergistic approach of nonionic surfactant flooding with intermediate CO2-based oil swelling. This study is focused on the efficiency of surfactant flooding and low-pressure oil swelling in oil recovery. We conducted a fluorescence-based microscopic analysis in a microchannel to explore the effect of sodium dodecyl sulfate (SDS) surfactant on CO2 diffusion in Texas crude oil. Based on the change in emission intensity of oil, the results revealed that SDS enhanced CO2 diffusion at low pressure in oil, primarily due to SDS aggregation and reduced interfacial tension at the CO2 gas–oil interface. To validate the feasibility of our proposed EOR method, we adopted a ‘reservoir-on-a-chip’ approach, incorporating flooding tests in a polymethylmethacrylate (PMMA)-based micromodel. We estimated the cumulative oil recovery by comparing the results of two-stage surfactant flooding with intermediate CO2 swelling at different pressures. This novel hybrid approach test consisted of a three-stage sequence: an initial flooding stage, followed by intermediate CO2 swelling, and a second flooding stage. The results revealed an increase in cumulative oil recovery by nearly 10% upon a 2% (w/v) solution of SDS and water flooding compared to just water flooding. The results showed the visual phenomenon of oil imbibition during the surfactant flooding process. This innovative approach holds immense potential for future EOR processes, characterized by its unique combination of surfactant flooding and CO2 swelling, yielding higher oil recovery.
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- Award ID(s):
- 2207642
- PAR ID:
- 10654570
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 14
- Issue:
- 24
- ISSN:
- 2076-3417
- Page Range / eLocation ID:
- 12078
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/app142412078
