Impact of Completion Design and Interwell Communication on Well Performance in Full Section Development: A STACK Case Study Using DNA Based Diagnostics
Bibliographic record
Abstract
Abstract The STACK (Sooner Trend Anadarko Canadian and Kingfisher counties) is a prolific multi-target stacked play in Oklahoma. Development challenges in the STACK are underpinned by fieldwide geological heterogeneities, including variable reservoir quality throughout the Sycamore-Meramec and Woodford formations and the presence of natural fractures and dense laminations. This case study examines the operator's first fully co-developed section in Canadian County, which comprised of 11 wells across 4 targets. This project was undertaken after de-risking much of the geological uncertainty in several offset pads. The data acquisition program was designed to assess the impact of total completion design including: interwell spacing, targeting, and wine-rack configuration on well-to-well connectivity and well performance in full section development. Within the section, half of the wells were drilled with the same spacing as offset pads and the other half were downspaced. On both sides of the section, similar targets received the same hydraulic fracturing design. Given it was the operator's first full section development in the county, the operator utilized an advanced data acquisition program that included downhole pressure gauges, chemical tracers, and DNA based diagnostics. DNA diagnostics proved especially useful in measuring the relative contribution from the multiple strata between landing zones, which would not have otherwise been possible. Although the previous offset pilot pads were developed with similar spacing and completion parameters, there were significant differences between average production profiles, with higher initial production (IP-180) observed in the full section. This paper evaluates these production differences by examining the impact of well spacing/targeting, completion design, and interwell communication on well performance in full section development. Well performance was assessed by integrating production, pressure, and tracer data, along with DNA based diagnostics. DNA diagnostics played a key role in assessing and monitoring the duration of interwell communication between offset wells across the section. Results from this integrated approach demonstrated that full section well performance was impacted by completion design and interwell communication in three notable ways: 1) interbench co-development significantly increased communication across perceived deterrents to fracture growth, 2) well-to-well communication was influenced by completion order, and 3) aggregate interwell communication was higher in full section development than in pilot pads, which may have contributed to the full section initially outperforming pre-drill expectations. The differences in well performance and well-to-well connectivity carry important implications for operators who plan to use partial spacing tests to develop multi-target full sections. Specifically, these observations underscore the potential for similar completion designs to yield materially different well performances between full section and 1 to 3 well pad development. These results also demonstrate the ability of DNA based diagnostics to accelerate learnings in full section development, which may have otherwise required additional CAPEX to test via heuristic techniques.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".