Linking Flowback Recovery to Completion Efficiency: Niobrara-DJ Basin Case Study
Bibliographic record
Abstract
Abstract Near wellbore complexity is a current topic of discussion among geoscience and engineering disciplines across North America. Asset teams are constantly investing money and resources into the variety of near- and far-field wellbore diagnostic techniques to ascertain completion efficiency. These range from high-cost microseismic for far-field fracture placement to higher risk technologies such as fiber optics, cameras, and production logging tools. These techniques are generally used for parameter constraints for rate-transient-analysis (RTA) that requires months (and sometimes years) of production after post-frac flowback. Therefore, in this study we utilize flowback water-oil-ratio (WOR) as a diagnostic tool to provide early-time feedback for completion-efficiency evaluation. We analyze flowback, post-flowback and completion-design data of 19 multi-fractured horizontal wells (MFHWs) completed in Niobrara and Codell formations that are classified into parent and child groups. Child wells are then sub-clustered into Zipper-1 and -2 completed with more and less intense completion strategy, respectively. First, we analyze the flowback rate and pressure profiles of the 19 wells to estimate initial pressure in the stimulated area around wellbore and validate it against the outcomes of diagnostic fracture injection test (DFIT). Second, we apply rate-normalized-pressure (RNP) diagnostic analysis to a) investigate flow regimes during flowback and post-flowback periods; and b) assess interference between parent and child wells. Third, we use WOR diagnostic plots to estimate ultimate load recovery (ULR) and calculate initial effective fracture volume as two indicators for completion efficiency. We also cross-check the estimated effective fracture volume with microseismic dimensions. Finally, we apply rate-decline analysis on oil production data to predict ultimate oil recovery (UQo), assuming a critical oil rate of 1 stbd, and use it as a third performance indicator to evaluate the completion-design efficiency of each group. Child wells show 32% more load recovery compared with the parent wells. However, the parent wells show 38% and 50% more 9-months cumulative oil production (Qo) and UQo, respectively. For both the parent and child wells, more than 50% of the predicted ULR is produced back within the first three months of production. Although the intense completion-design strategy for Zipper-1 wells led to 35% larger effective fracture volume compared to Zipper-2 wells, both groups show similar oil recovery performance. Generally, Niobrara wells show less load recovery and effective fracture volume compared to Codell wells in each completion group.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".