Investigating the Impact of Reservoir Properties and Injection Parameters on Carbon Dioxide Dissolution in Saline Aquifers

Abbaszadeh, Mohsen; Shariatipour, Seyed M.

doi:10.3390/fluids3040076

Cited by 17 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To achieve this, the study gathered a large dataset through the definition of uncertainty variables and the application of Latin hypercube sampling, guided by previous sensitivity analyses that highlighted key factors in the EOR-CO 2 process [1,[42][43][44][45]. Consequently, nine parameters were selected for detailed analysis: porosity (Por), permeability (Perm), reservoir thickness (Thickness), fracture half-length (FHL), bottom hole flowing pressure (BHP), injection rate of CO 2 (CO 2 -INJ), cumulative injected CO 2 mass (CO 2 -CMASS), soaking time (SOAK-T), and number of fractures (Numfrac).…”

Section: Obtaining Numerical Simulation Datamentioning

confidence: 99%

Prediction of ORF for Optimized CO2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

Yue,

Dai,

Liao

et al. 2024

Energies

View full text Add to dashboard Cite

Tight reservoirs characterized by complex physical properties pose significant challenges for extraction. CO2 flooding, as an EOR technique, offers both economic and environmental advantages. Accurate prediction of recovery rate plays a crucial role in the development of tight oil and gas reservoirs. But the recovery rate is influenced by a complex array of factors. Traditional methods are time-consuming and costly and cannot predict the recovery rate quickly and accurately, necessitating advanced multi-factor analysis-based prediction models. This study uses machine learning models to rapidly predict the recovery of CO2 flooding for tight oil reservoir development, establishes a numerical model for CO2 flooding for low-permeability tight reservoir development based on actual blocks, studies the effects of reservoir parameters, horizontal well parameters, and injection-production parameters on CO2 flooding recovery rate, and constructs a prediction model based on machine learning for the recovery. Using simulated datasets, three models, random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), were trained and tested for accuracy evaluation. Different levels of noise were added to the dataset and denoised, and the effects of data noise and denoising techniques on oil recovery factor prediction were studied. The results showed that the LightGBM model was superior to other models, with R2 values of 0.995, 0.961, 0.921, and 0.877 for predicting EOR for the original dataset, 5% noise dataset, 10% noise dataset, and 15% noise dataset, respectively. Finally, based on the optimized model, the key control factors for CO2 flooding for tight oil reservoirs to enhance oil recovery were analyzed. The novelty of this study is the development of a machine-learning-based method that can provide accurate and cost-effective ORF predictions for CO2 flooding for tight oil reservoir development, optimize the development process in a timely manner, significantly reduce the required costs, and make it a more feasible carbon utilization and EOR strategy.

show abstract

Section: Obtaining Numerical Simulation Datamentioning

confidence: 99%

Prediction of ORF for Optimized CO2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

Yue,

Dai,

Liao

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…First, we identify the uncertainty variables that impact the CO 2 trapping capacities and determine their corresponding ranges [54,55]. The Latin Hypercube Sampling (LHS) technique is conducted to create various designs of input samples.…”

Section: Step 1: Dataset Generationmentioning

confidence: 99%

Uncertainty Analysis of CO2 Storage in Deep Saline Aquifers Using Machine Learning and Bayesian Optimization

Abdulwahab

Yan

et al. 2023

Energies

View full text Add to dashboard Cite

Geological CO2 sequestration (GCS) has been proposed as an effective approach to mitigate carbon emissions in the atmosphere. Uncertainty and sensitivity analysis of the fate of CO2 dynamics and storage are essential aspects of large-scale reservoir simulations. This work presents a rigorous machine learning-assisted (ML) workflow for the uncertainty and global sensitivity analysis of CO2 storage prediction in deep saline aquifers. The proposed workflow comprises three main steps: The first step concerns dataset generation, in which we identify the uncertainty parameters impacting CO2 flow and transport and then determine their corresponding ranges and distributions. The training data samples are generated by combining the Latin Hypercube Sampling (LHS) technique with high-resolution simulations. The second step involves ML model development based on a data-driven ML model, which is generated to map the nonlinear relationship between the input parameters and corresponding output interests from the previous step. We show that using Bayesian optimization significantly accelerates the tuning process of hyper-parameters, which is vastly superior to a traditional trial–error analysis. In the third step, uncertainty and global sensitivity analysis are performed using Monte Carlo simulations applied to the optimized surrogate. This step is performed to explore the time-dependent uncertainty propagation of model outputs. The key uncertainty parameters are then identified by calculating the Sobol indices based on the global sensitivity analysis. The proposed workflow is accurate and efficient and could be readily implemented in field-scale CO2 sequestration in deep saline aquifers.

show abstract

“…At a constant depth, the fluid pressure also remains constant in these simulations, because it is defined as a hydrostatic pressure due to the column of the fluid at that depth. The CO2 concentration in brine is a function of the fluid pressure which generally increases with increasing the fluid pressure (Abbaszadeh and Shariatipour, 2018;Seyed M. Shariatipour et al, 2016). Thermodynamic rules (Duan and Sun, 2003;Spycher et al, 2003) determine the CO2 concentration in brine which is expected at a specific fluid pressure.…”

Section: Radial Crackingmentioning

confidence: 99%

Prediction of the lifespan of cement at a specific depth based on the coupling of geomechanical and geochemical processes for CO2 storage

Bagheri

Shariatipour

Ganjian

2019

International Journal of Greenhouse Gas Control

View full text Add to dashboard Cite

Investigating the Impact of Reservoir Properties and Injection Parameters on Carbon Dioxide Dissolution in Saline Aquifers

Cited by 17 publications

References 34 publications

Prediction of ORF for Optimized CO2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

Prediction of ORF for Optimized CO2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

Uncertainty Analysis of CO2 Storage in Deep Saline Aquifers Using Machine Learning and Bayesian Optimization

Prediction of the lifespan of cement at a specific depth based on the coupling of geomechanical and geochemical processes for CO2 storage

Contact Info

Product

Resources

About