Enhancement of Production and Economics Through Design Optimization in the STACK
Bibliographic record
Abstract
Abstract This paper discusses a stimulation optimization case history in the STACK (Sooner Trend, Anadarko Basin, Canadian, and Kingfisher counties of Oklahoma). This area has been active for a number of years, and operators are still trying to figure out how best to drill and complete the asset. This case history will cover a field trial conducted in the Meramec, the Sycamore and the Woodford, and will compare the completion methods of two offset pads, with one pad serving as the pre-optimization offset. These field trials consisted of wellbore spacing tests as well as the implementation of certain design and execution changes. The first major design change was the implementation of an algorithm-based, automated controlled breakdown process designed to improve the uniformity and the cluster efficiency of given stimulation treatments. When stimulations are conducted, it has been consistently demonstrated that a disproportionate number of perforation clusters are not effectively treated during any given stimulation treatment. The implementation of a controlled breakdown process not only allows for a better stimulation efficiency, but also allows for consistent treatment operations across stages and wellbores. The second design change was the application of ultra-fine particulates (UFP) during the initial pad stages of stimulation treatments. Unconventional reservoirs contain some level of complexity in the form of microfractures. Conventional proppants are typically too large to prop those microfractures, which limits how much hydrocarbon can be produced from a given reservoir. UFPs are small enough to enter those microfractures. Likewise, UFPs have been shown to reduce near wellbore friction early in stimulation treatments, which improves operational efficiencies. The third design change was the utilization of a wellhead connection system in place of a traditional zipper manifold. This system reduces equipment on location as well as the complexity of operations when stages on two or more wells are being stimulated in sequence. The operational changes on the post-optimization pad yielded increased efficiencies and reduced costs. The wells also showed a substantial production improvement over the typical curves for the area. The post-optimization wells had a higher rate of return over the pre-optimization pad. The wells where the automated controlled breakdown process was utilized demonstrated a 5 to 15% production increase over comparable offsets on the same pad. The wellhead connection system improved transition times between wellhead swaps during well zipper stimulation operations. The implementation of the breakdown process, the wellhead connection system, and UFPs improved operational efficiencies as well as making treatments more consistent. Observed treating pressures were lower and more consistent when the breakdown process was utilized. This paper demonstrates how the implementation of a controlled breakdown process, wellhead connection system, and UFPs can help improve well economics. Also, the economic potential for the STACK when utilizing an optimized completion is demonstrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".