Improved Production with Mineralogy-Based Acid Designs
Bibliographic record
Abstract
Abstract This paper presents a case history on new sandstone acidizing technology using a nonhydrofluoric formulation to successfully treat a high carbonaceous sandstone formation. The improved understanding of the chemical complications of hydrofluoric (HF) on dirty sandstones led to the design of a nonhydrofluoric treatment on the high carbonate content (dirty) sandstone formation. Previous treatments using various formulations of HF acid failed to remove the high skin associated with several wells in this formation. A new approach was taken to identify the damage mechanism and evaluate damage removal options based on the formation mineralogy. This approach analyzed the potential chemistry risks associated with using HF type treatments in the presence of particular mineralogies and temperatures. The new approach also used logging and reservoir modeling technology to forecast the estimated production profile of the complex multilayered formation. Candidate wells were identified by comparing the forecast production profile potentials to the surveyed production profiles based on production logging (PLT) of the prescreening candidates. The final treatment candidate was then selected for the trial of the new treatment formulation. The treatment was specifically tailored based on the identified mineralogy and encompassed the damage prevention strategies. The result was a 40% increase in oil production for the well, but a 2-fold to 10-fold increase for the treated zone, depending on pretreatment production assumptions.
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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 | 0.000 | 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) | 0.001 | 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".