Automatic Isotherm Derivation From Field Data for Oilfield Scale-Inhibitor Squeeze Treatments
Bibliographic record
Abstract
Summary Oilfield scale formation represents a significant flow-assurance challenge to the oil and gas industry, because of increasing water production worldwide and higher oil prices. Scale-inhibitor (SI) squeeze treatment is the most widespread method to combat downhole scaling. To predict SI squeeze treatments accurately for further optimization, it is necessary to simulate the SI retention in the formation, which may be described by a pseudoadsorption isotherm. Although these are often derived from coreflood experiments, sometimes they are not appropriate for modeling well treatments because the core tests on which they are based cannot fully represent field-scale processes. In practice, the parameters of an analytic form of the isotherm equation are modified by trial and error by an experienced practitioner until a match is obtained between the prediction and the return profile of the first treatment in the field. The main purpose of this paper is to present a stochastic hill-climbing algorithm for automatic isotherm derivation. The performance of the algorithm was evaluated by use of data from three field cases. Two success criteria were defined: the ability to match a single historical treatment and the ability to predict subsequent successive treatments. To test for the second criterion, a candidate isotherm was derived from the first treatment in a well that was treated with the same chemical package on consecutive occasions, and then the predictions by use of the suggested solution were compared with the observed SI concentration-return profiles from the subsequent treatments. In all the calculations, the performances of both the isotherms suggested by the hill-climbing algorithm and the isotherms derived by trial and error were compared. The results demonstrate that the hill-climbing algorithm is an effective technique for deriving an isotherm for a single treatment, although predictions for successive treatments worsened slightly with each treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".