Challenging Assumptions About Fracture Stimulation Placement Effectiveness Using Fiber Optic Distributed Sensing Diagnostics: Diversion, Stage Isolation and Overflushing
Bibliographic record
Abstract
Abstract The connection of the wellbore to the hydrocarbon resource volumes via effective fracture stimulation is a critical factor in unconventional reservoir completions. Various well construction and dynamic placement methods are used to distribute treatment volumes into targeted sections of the wellbore. This paper provides some insights into the effectiveness of hydraulic fracture stimulation process using Fiber Optics (FO): distributed acoustic sensing (DAS) and distributed temperature sensing (DTS). This paper reviews examples from multiple wells where FO has been used to gain a better understanding of three highly debated fracture stimulation distribution topics: Diversion, Stage Isolation and Overflushing. Diversion is increasingly being used as a way to improve the efficiency of hydraulic fracture stimulation distributions. The effectiveness of the diversion techniques has traditionally been judged on the basis of surface pressure response during treatment and ultimately, from production comparisons to reference wells. Unfortunately, getting clear answers from production performance takes significant time. FO allows for monitoring of the diversion process in real-time. Analysis of DAS and DTS responses is used to quantify diversion efficiency in re-directing hydraulic fracture stimulation from dominant perforation clusters to those not being stimulated. Lack of isolation between stages has frequently been observed in wells with diagnostics. There is consensus amongst the completion community that communication between stages is highly undesirable because the energy and materials of the stimulation are partially or totally misdirected from the target interval to other portions of the wellbore. The analysis of DAS and DTS not only can help determine the frequency of occurrence of communication between stages in cemented and uncemented horizontal wells but also can provide insights about the different communication paths. Fiber Optic distributed sensing in conjunction with complementary diagnostics is also being used to investigate if connections are being maintained at the end of the treatment between the newly created fracs and the wellbore. The use of integrated diagnostics allows evaluation of the frequency in which overflushing (over-displacement) occurs in both vertical and horizontal wells and its impact on well inflow performance where production profiling data is available.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".