Modeling 2-Phase Flowback of Multi-Fractured Horizontal Wells Completed in Shale
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Bibliographic record
Abstract
Abstract Early fluid production and flowing pressure data gathered immediately after fracture-stimulation of multi-fractured horizontal wells may provide an early opportunity to generate long-term forecasts in shale gas reservoirs. These early data, which often consists of hourly (if not more frequent) monitoring of fracture/formation fluid rates, volumes and flowing pressures, are gathered on nearly every well that is completed. Additionally, fluid compositions may be monitored to determine the extent of load fluid recovery, and chemical tracers added during stage treatments to evaluate inflow from each of the stages. There is currently debate within the industry of the usefulness of these data for determining the long-term production performance of the wells. "Rules of thumb" based upon percentage of load fluid recovery are often used by the industry to provide a directional indication of well-performance. More quantitative analysis of the data is rarely performed; it is likely that the multi-phase flow nature of flowback, and the possibility of early data being dominated by wellbore storage effects has deterred many analysts. In this work, the use of short-term flowback data for quantitative analysis of induced hydraulic fracture properties is critically evaluated. Examples from the Marcellus shale are analyzed. The short (< 48 hours) flowback periods were followed by long-term pressure build-ups (~1 month). Gas/water production data was analyzed using analytical simulation and rate-transient analysis methods designed for analyzing multi-phase coalbed methane (CBM) data. One interpretation is that the early flowback data corresponds to wellbore + fracture volume depletion (storage). It is assumed that fracture storage volume is much greater than wellbore storage. This flow-regime appears consistent with what is interpreted from the long-term pressure buildup data, and from rate-transient analysis of flowback data. Assuming further that the complex fracture network created during stimulation is confined to a cylindrical region around perforation clusters in each stage, fluid production data can be analyzed using a 2-phase tank model simulator to determine fracture permeability and drainage radius, the latter being interpreted to be equivalent to effective (producing) fracture half-length. Total fracture half-length, derived from rate-transient analysis of on-line (post-cleanup) data, verifies the flowback estimates. An analytical forecasting tool that accounts for multiple sequences of post-storage linear flow, followed by late-stage boundary flow, was developed to forecast production using only flowback-derived parameters, volumetric inputs, matrix permeability, completion data and operating constraints. The preliminary forecasts are in very good agreement with on-line production data, after several months of production. The use of flowback data to generate early production forecasts is therefore encouraging, but needs to be tested for a greater data set for this shale play and for other plays.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it