Predicting and Managing Inorganic Scale Associated With Produced Water from EOR Projects
Bibliographic record
Abstract
Abstract Inorganic scale associated with conventional hydrocarbon extraction has been well studied over the past 50 years and the mechanisms of formation, inhibition and removal are now well understood within the industry. For enhanced oil recovery (EOR) significant changes occur within the reservoir as a result of injected chemical or changes in fluid type that are used to increase the oil recovery. Two such EOR processes, CO2 and alkali surfactant polymer (ASP) flooding, are the subject of this paper. CO2 is miscible with oil when injected at high enough pressure, but is also very soluble in water. The resulting low pH carbonic acid typically increases the geochemical reactivity of the system, and thus it is no longer sufficient to consider the scale risk associated with the formation and injection brines, but the impact of mineral dissolution deep within the reservoir may also be a factor when analysing the scale risk as the brines are produced. The requirement for such calculations, which are not typically conducted during conventional hydrocarbon recovery processes, is discussed. Consideration is given to the value of developing models that will predict the concentration of scaling ions, and hence the scaling risk, at the production wells based on in situ reactivity. In the case of ASP flooding, the current industry understanding of scale prediction models for such systems is discussed, along with the current inhibitor screening tests to qualify scale inhibitors for squeeze application. The design of the different squeeze treatments applications for treatment of formation water and injection water production are presented. Along with the scale challenges within the production well, the scale issues associated with processing the produced ASP fluids will be reviewed in terms of additional scale risk not associated with conventional hydrocarbon production. The objective of this paper is to highlight the challenges, current understanding and gaps in the industry knowledge and processes when it comes to scale management for EOR projects.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".