Evaluation of Completion Practices in the STACK Using Completion Diagnostics and Production Analysis
Bibliographic record
Abstract
Abstract Evaluation of completion techniques continues to help in cost reduction and optimization practices. The objective of this project is to evaluate completions within Oklahoma in the STACK (Sooner Trend, Anadarko Basin, and Canadian and Kingfisher counties), comparing several types of technology and optimization methods implemented within this play. Changes to completion practices are driven by cost reduction and production needs. When evaluating a newly implemented technique, a "science well" is often selected to run a multitude of diagnostics to verify the effectiveness of the change. The dataset in this study goes beyond the concept of a single well, or several well analysis, and focuses on approximately 50 wells in this play. Wells are categorized based on completion practices and data was gathered based on completion diagnostics. A macroscopic production analysis was completed to complement the diagnostic results. As with any study of this magnitude, there are several variables to capture and take into consideration while evaluating the diagnostic and production data. Wells in this study are categorized to maximize the number of unchanged variables. Observations were made on the overall stimulation coverage of each treatment interval evaluated, isolation between stages completed, and trends in production. Several examples of diagnostic data are presented as case histories in addition to the categorized data set. The STACK is one of the four most prolific plays currently being drilled and completed in the United States. This is the largest case history published on wells completed in the STACK that utilizes an integrated daily production and diagnostic approach. The dataset and case histories in this paper provide valuable insight for operators and service companies who are completing wells in this play.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".