Production Analysis in the Barnett Shale – Field Example for Reservoir Characterization Using Public Data
Bibliographic record
Abstract
Abstract Development of unconventional reservoirs continues to expand in North America and has gained interest worldwide. The first unconventional play to be rapidly developed is the Barnett Shale located in North Central Texas. As of July 2012, the Barnett Shale had more than 13,000 multi-staged fractured horizontal wells (MFHW) with approximately 2,500 of these wells with over five-plus years of production history. In addition to these MFHW, there are approximately 3,000 vertical wells in the Barnett Shale. Well spacing is a key value driver for field development and needs to be addressed early in the appraisal process. Interpretation of early production analysis from MFHW can provide many insights into the reservoir and fracture characteristics of these wells. However, these interpretations are non-unique until end of linear flow (ELF) is reached. This study used public production data that lacks wellhead flowing pressures. For this study the wells that did see end of linear flow did so in a time period where flowing pressures are expected to be relatively constant and therefore having measured pressures was not deemed necessary. Reservoir permeability, fracture half-length, and original-gas-in-place of the area contacted by the created hydraulic fracture network can be determined once end of linear flow is reached. Once fracture half-length and permeability have been determined, the appropriate well spacing can be estimated using simulation and economics. A comprehensive review of approximately 2,500 MFHW using public data within two Northern counties of the Barnett Shale found more than 100 wells where end of linear flow could be clearly observed in production characteristics. With the end of linear flow determined for these wells, estimates of permeability and fracture half-length were determined. A single well simulation study feeding an economic evaluation was then used to study well spacing and yield suggested optimum development spacing. This paper will review all of this work, the results and conclusions, and provide observations of some of the trends.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".