Recent Acid-Fracturing Practices on Strawn Formation in Terrell County, Texas
Bibliographic record
Abstract
Abstract The goal of an acid fracture treatment is to generate a highly conductive pathway of sufficient length from the reservoir to the wellbore. Depth of penetration of live acid is the critical factor in determining the success of an acid-fracturing treatment. Depth of penetration is controlled by the acid reaction rate, leakoff, and stimulation rate. Acid reaction rate is a function of several factors, the most important of which is the reservoir temperature. Yet another concern, in acid fracturing in long carbonate intervals, is attaining the necessary diversion to ensure that multiple sets of perforations are adequately stimulated. Because of their high solubility and highly fractured/vugular nature, carbonate reservoirs in the Permian Basin show excellent response to acid fracturing treatments. However, inadequate diversion can leave substantial portions of the reservoir untreated. Different acid systems have been developed to counter the problems in acid fracture stimulations. Chemical and mechanical means of diversion have been used with varying degrees of success. Likewise, there have been many attempts made in retarding the reaction rates of hydrochloric acids in high temperature environments. Recently, there has been a large number of highly successful acid fracture treatments in the Permian Basin incorporating a combination of new polymer-free self-diverting acid combined with an existing acid-oil emulsion technology. 120 This paper will discuss a combination of technologies, which has recently been applied successfully in the Strawn formation in Terrell County, Texas. It will also focus on what is being done to mitigate the affect of high temperature on hydrochloric acid's reaction rate. It will further develop improvements in reservoir characterization and pay zone determination, which has been improved by the utilization of resistivity imaging logs. Some examples presented contain information from radioactive tracers and production logs, which are fundamental to understanding how good zonal coverage was achieved using different techniques. Additionally production analysis has been conducted to determine the effective fracture half-length and etched conductivity. Finally, a relative comparison between the old and new completion methodologies is made taking into account that the new completion practices have only been applied in full combination since 2004.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".