Results from a Collaborative Industry Study on Parent/Child Interactions: Bakken, Permian Basin, and Montney
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper presents results from a collaborative industry study involving ten high-quality pad-scale datasets from the Delaware Basin, Midland Basin, Bakken, and Montney. The study had three primary goals: (a) compare/contrast observations between each dataset, (b) identify general strategies that can be used to mitigate parent/child impacts, and (c) provide concrete recommendations to optimize fracture design and well placement. For each dataset, an integrated hydraulic fracturing and reservoir simulation model was constructed and history matched to the observations. The models were calibrated to production data and pressure measurements, as well as to diagnostics such as: distributed acoustic sensing (DAS), microseismic, downhole imaging, chemical tracers, geochemical production allocations, and pressure observations from offset wells. History matching was performed by varying formation properties and model inputs to ensure consistency with the observations. Once the models were calibrated, the same set of approximately 120 sensitivity analysis simulations was performed on each model. Finally, an automated algorithm was used to quantitatively optimize fracture design and well placement to maximize economic performance. At each step in the process, the results were analyzed to identify the similarities and differences between the datasets and to explain why. The results show how differences in stratigraphy, well configuration, fracture design, and formation properties drive differences in parent/child phenomena. Optimal strategies to mitigate challenges depend on these site-specific conditions. Negative impacts from parent/child interactions cannot be entirely avoided. There is no strategy that can prevent the most important cause of child well underperformance – that wells are attempting to produce hydrocarbons from rock that has already been significantly depleted by parent well production. However, strategic design choices and quantitative economic optimization can significantly improve net present value and return on investment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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