EOR Screening Methods Assisted by Digital Rock Analysis: A Step Forward
Bibliographic record
Abstract
Abstract The screening of chemical EOR technologies for a Colombian field was performed using two different screening tools (weighting averages and artificial intelligence). The Alkali-Surfactant-Polymer (ASP) pilot results were compared with the initial screening studies identifying some weaknesses that are addressed in this paper. Additionally, the use of Lattice Boltzmann pore-scale flow simulation approach to support EOR screening studies is also presented. The screening study was developed using the same input data (e.g. pressure, temperature, porosity, permeability, oil gravity, and viscosity). Screening results and potential reservoir analogs identified using both systems were compared, including the evaluation of the geological parameters that are normally missing in most of screening studies. The results are compared with the ASP pilot performance to validate the effectiveness of conventional screening studies overlooking geologic information. In addition, the results were also confirmed evaluating ASP field cases reported in the literature. Finally, the use of digital rock analysis using micro CT scan images to support ongoing screening results is presented. Screening results obtained using different screening tools were similar identifying the EOR recovery process (e.g. Chemical EOR). However, the screening results excluding the evaluation of geological parameters such as rock cementation (e.g. sandstone formations with carbonate cement) did not prevent the selection of ASP flooding as an EOR recovery process for the field under study. This was confirmed with the severe scaling problems observed during the ASP pilot test implemented in Colombia as well in Canadian ASP floods. This paper describes the main steps for conducting robust EOR screening studies, including the use of Lattice Boltzmann pore-scale flow simulation to evaluate preliminary performance of oil recovery processes (e.g. waterflooding, polymer and surfactant injection) that contributes to field evaluations and experimental lab design. The proposed screening approach will contribute identifying the technical and economic EOR potential (from exploratory appraisal to mature field rejuvenation) under conditions of limited information and time constraints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".