CO2/brine relative permeability estimation using effective rock/fluid properties: A machine learning-based approach
Bibliographic record
Abstract
Relative permeability is a crucial parameter in CO2 storage as it describes the fluids' transport in porous media and residual saturations. CO2 injection into saline aquifers leads to CO2 dissolution in brine and subsequent formation of carbonic acid, resulting in complicated reactions with the rock minerals. These reactions include mineral dissolution, mineral precipitation or silicate weathering, depending on the composition and texture of the reservoir rock. As a consequence, these reactions bring about alterations in key rock characteristics including mineralogy, porosity, absolute permeability, and wettability. The main objective of this study is to develop a CO2/brine relative permeability model using machine learning (ML) algorithms based on a more comprehensive, yet representative, set of input parameters including rock/fluid properties and their compositions, instead of time-consuming experiments. In this study, experimental relative permeability data were data mined from the literature, along with some additional fluid and rock properties including absolute permeability, porosity, fluid density, fluid viscosity, CO2/brine interfacial tension, CO2 solubility, rock minerals’ concentration, brine salinity and composition, and fluid saturations. The dataset was pre-processed, and missing data were identified and populated using applicable correlations. Various supervised ML approaches were tried to predict the relative permeability values, including Decision Tree Regression (DTR), Gaussian Process Regression (GPR), Linear Regression (LR), and Bayesian Ridge Regression (BRR). In a comparative analysis, the same ML algorithms were trained and tested, incorporating only three typical input parameters that have been repeatedly used recently in the literature for relative permeability modelling (i.e., porosity, absolute permeability, and fluids saturation). Results indicate that there was a good agreement between the predicted and experimental relative permeability data for models developed using DTR and GPR algorithms when all input parameters were incorporated. However, the models were not nearly as accurate when only three conventionally agreed input parameters were used. A sensitivity analysis was also performed to determine the most accurate input parameters and their impact on relative permeability predictions. According to the sensitivity analysis results, the most important input parameters are brine salinity, CO2 and water viscosity, porosity, absolute permeability, and iron and halite content of the rock samples. Overall, the results indicate the critical role of rock-fluid interactions and composition, as well as fluid properties, in predicting relative permeability using ML-based approaches, underscoring their significance for future studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".