Field Testing of the Flowback Technology for Multistage-Fractured Horizontal Wells: Test Results and Primary Interpretation of the Results
Bibliographic record
Abstract
Abstract The paper presents the results of applying the methodology of well flowback and startup after hydraulic fracturing (HF), previously proposed in (Osiptsov et al., 2019), where the preferred conditions for well flowback after hydraulic fracturing are formulated in the form of a field experiment program. The program was implemented in 2019-2020 at four out of ten wells of the Priobskoye field in Western Siberia. The comparison of the two well clean-up designs, "smooth" and "aggressive", aimed to confirm the hypothesis that the choice of a "smooth" mode can reduce undesirable geomechanical effects to preserve the fracture conductivity and increase the recovery. Adapting our own hydrodynamic and geomechanical models to actual data made it possible to control the well clean-up process in the wells of a field experiment. Well site supervision allowed authors to fully implement the research plan, and also provided the opportunity to vary the parameters of the experiment (adjusting flowrate over time, adjusting the sampling and measurement schedules) using history matched models with actual parameters of the wells. Based on the results, the obtained data were analyzed and interpreted: flow rate, water cut, bottomhole and wellhead pressure, bottomhole temperature, suspended particulate matter (SPM) concentration, drain level, expedition pump frequency and wellhead samples. At the planning stage of the experiment, a formation zone of interest (ZOI) was selected with a set of first six pilot wells, where the geomechanical effects during the flowback period have the greatest impact on production. The field experiment program, which contains the wellhead choke steps sequence of diameters and duration of the well clean-up periods for two scenarios - "aggressive" and "smooth" for particular well. In addition to the choke schedule during eruptive period, there is a need to continue the recommended well startup after the ESP run in hole (RIH). Representativeness and repeatability conditions of field tests were formulated, comparison metrics were developed in order to standardize, normalize and estimate the well performance of the well startup a. We carried out the design of a field experiment proposed in 2019 (Osiptsov et al., 2019) and showed in practice that the dynamics of the well flowback and startup affects the well productivity index for a selected ZOI. In addition, we history-matched in-house geomechanical and hydrodynamic in order to quantify the production increase with regards to different flowback scenarios. Based on the available data, the boundaries of the pressure fluctuations opposite the hydraulic fracturing ports in the horizontal well were calculated in the absence of actual measurements to clarify the conditions for maintaining the conductivity of the fracture.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".