Integrating DAS, Treatment Pressure Analysis and Video-Based Perforation Imaging to Evaluate Limited Entry Treatment Effectiveness
Bibliographic record
Abstract
Abstract The primary objectives of perforating a lengthy cased-and-cemented wellbore section for fracture stimulation are to 1.) enable extensive communication with the reservoir and 2.) control the allocation of fluid and proppant into multiple intervals as efficiently as possible during fracturing treatments. Simultaneously treating multiple intervals reduces the number of fracturing stages required, thus reducing treatment cost. One way to control the allocation is to use limited entry perforating. Limited entry is the process of either limiting the number of perforations or reducing the size of the perforation entry-hole to achieve significant perforation friction pressure during a hydraulic fracturing treatment. Perforation friction establishes a backpressure in the wellbore that helps to allocate flow among multiple, simultaneously-treated perforation intervals/clusters that have differing fracture propagation pressures. Execution and optimization of limited entry perforating requires awareness of the factors that can affect performance. This paper presents a case study of plug-and-perf horizontal well treatments in an unconventional shale play in which various diagnostic methods were used to better understand and quantify these factors. Within the case study, three types of perforation evaluation diagnostics were implemented: 1.) injection step-down tests and pressure analysis of the fracturing treatments, 2.) video-based perforation imaging and 3.) distributed acoustic sensing (DAS). Injection step-down tests indicated that all perforations were initially accepting fluid. However, history-matched solutions of step-down tests are non-unique due to multiple variables involved in the calculations and uncertainty regarding the exact initial-perforation conditions. Surface pressure analysis of the main fracturing treatments indicated that in certain cases, several perforations were not accepting fluid and proppant (slurry) by the end of the job. The number of inactive perforations was typically equivalent to the amount contained in two clusters. Video-based imaging highlighted several trends and concepts for perforating. Zero-phase perforating toward the high side of the well was advantageous for obtaining quality images and relatively consistent perforation dimensions. A large majority of perforations showed unambiguous qualitative evidence of significant proppant entry. Even though images captured were post-stimulation, it was apparent that initial perforation dimensions were significantly smaller and gun phasing had a more significant effect than originally predicted. Evaluation of the erosion patterns on the perforations showed a positional bias where for a given frac stage, perforations in clusters nearest the heel of the well were more eroded than perforations in clusters nearest the toe of the well. Distributed acoustic sensing (DAS) analysis confirmed the conclusions of the surface pressure analysis. In the example provided, the data showed all clusters accepting fluid during the step-down test. Later in the stage, the DAS data showed two clusters not accepting fluid at different times of the stage. DAS analysis was able to confirm the timing and position of the two clusters. The DAS data also showed a positional bias, allocating more slurry volume to clusters nearest the heel of the well. However, DAS analysis also showed that changing the number of perforations in a cluster had a larger effect than the positional bias. The staggered perforation design featuring two fewer perforations in the cluster closest to the heel effectively counteracted the positional bias but resulted in diverting too much slurry volume from that cluster. The results also highlight the importance of perforator quality control in terms of perforation hole size. Treating pressure and DAS analysis indicated a particular cluster stopped taking slurry relatively early in the treatment and post-frac imaging dimensioned the hole sizes and revealed they were extremely undersized from the expected hole size. Based on the results of the case study, it was recommended to use a staggered perforation design with more gradual changes. This was verified with modeling using updated parameters which showed that the resulting changes are likely to improve slurry allocation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".