Evaluation and Management of Stimulation Placement Control in Cemented Sleeve Completions
Bibliographic record
Abstract
Abstract An improved and systematic method to evaluate and quantify inter-stage isolation and stimulation distribution effectiveness in cemented sleeve completions has been developed. Data is presented for over 1400 stages from 24 wells in five resource play field development areas in the USA and Canada. The method includes quantification of stimulation effectiveness and communication leakage risk. An example of effective communication mitigation is included, with optic fiber diagnostic validation. The method of evaluation includes integration of data from stimulation treatment parameters (treatment pressures, treatment rates, and proppant slurry concentrations) with bottom hole pressure and temperature sensors from both the toe-side and heel-side of the stimulation treatment bottom hole assembly (BHA). The process includes assessment of pressure differential observed across the heel-side and toe-side pressure sensors to quantify the level of toe-side communication leakage, and integration of the temperature response of the toe-side temperature sensor enables evaluation of the location of the leakage within the sleeve to sleeve dimension. One well includes validation with optic fiber distributed sensing methods. Categorical and systematic methods to evaluate inter-stage isolation effectiveness are described, applied and compared for sleeve spacing ranging from 55 feet to 175 feet. The method and model to systematically rank the isolation effectiveness was developed to better quantify communication leakage, and the workflow is presented.Stimulation Distribution effectiveness ranged from 47% to 100%. The poorest isolation was observed in wells with 55 foot sleeve spacing (although we had one well with 55 foot sleeve spacing with 94% stimulation distribution effectiveness). We observed a general trend of improved isolation with larger sleeve spacing, however, two wells with ~ 175-foot sleeve spacing also observed significant communication leakage (76% and 81% stimulation distribution effectiveness). Amethod of identification and effective communication mitigation is described and includes observations with surface treatment pressure response. Effective mitigation is confirmed with both BHA pressures and optic fiber diagnostics. This data provides necessary input for economic justification to attempt to improve isolation methods in cemented horizontal completions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".