New Near-Wellbore Insights from Fiber Optics and Downhole Pressure Gauge Data
Bibliographic record
Abstract
Abstract It has been widely demonstrated that frac stimulation efficiency and more importantly production, varies significantly between perforation clusters as well as between sleeve entries. Recent trends indicate that many operators are simultaneously increasing the number of perforation clusters or entries while decreasing frac-to-frac spacing. This is done with the expectation that it will lead to more productive wells overall. The purpose of this paper is to investigate some of the aspects that may limit this approach. There are an increasing number of frac diagnostic tools which allow us to get a better understanding of frac placement and production. Unfortunately, there are only few diagnostic tools available today to characterize the near wellbore region (NWR). Fiber Optics (FO) and other downhole measurements can play an important role in providing information about the NWR. In this paper, we share data and examples from wells where the combination of data from Distributed Acoustics Sensing (DAS), Distributed Temperature Sensing (DTS) and downhole gauges is helping us gain insights about this poorly understood region of our unconventional reservoirs. This paper combines DAS, DTS and downhole pressure gauge data to demonstrate the existence of significant near wellbore complexity, both during stimulation and production. We frequently observe changes in DAS signal and pressure during the stimulation of horizontal wells completed via both "Plug and Perf" (PnP) and Cemented Single Point Entry (CSPE) systems. These changes support the existence of significant near-wellbore tortuosity. Furthermore, we show that pressure data from downhole gauges can differ significantly from surface pressure data extrapolated downhole. This can impact the interpretation of Step-Down-Tests, other analytical techniques relying on the surface pressure alone and affecting the calibration of frac models aimed at understanding the NWR. In wells instrumented with a FO cable behind casing, it is possible to use the DTS data during warmback, following stimulation injection to gain insights about frac geometry in the NWR. Such data provides information about the hydraulic frac dimensions created by the stimulation process in both vertical and horizontal wells. During warmback it is easy to distinguish intervals containing hydraulic fractures near the wellbore where the temperature recovery is lagging compared to the unstimulated portions of the well. FO instrumented horizontal wells allow for estimation of the dimensions of the "Frac-Zone" along the wellbore in the NWR where a combination of hydraulically induced longiditunal and vertical transverse fracs exist. Thermal modeling is also presented for selected stages that further support the qualitative interpretation of the DTS. The diagnostics presented help quantify the dimensions of longitudinal and transverse components in horizontal wellbores in the NWR. This paper also highlights the risk of putting perforation clusters or sleeve entries too close to one another. It is clear that the NWR is poorly understood and more information is needed. Understanding the processes that govern the NWR are essential, after all, this is the region where the well and the reservoir interact.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".