Well Spacing and Landing Zone Optimization to Improve Development Strategy - A Case Study from the Stack
Bibliographic record
Abstract
Abstract Sooner Trend Anadarko Canadian Kingfisher, also known as STACK, is a booming unconventional oil play in North America. As one of the main features that makes the asset profitable, multiple targeting benches raise a challenge of optimization. Well-developed natural fracture system brings in another level of complexity to estimate well spacing. This study introduces an integrated workflow to better understand the fluid flow mechanism in the reservoir and optimize development strategy. From borehole image log, natural fracture orientation and density was interpreted and statistically populated into geologic model along with petrophysical properties. To account for productivity enhancement due to natural fractures, enhanced permeability was embedded into the simulation model according to the distribution of discrete fracture network. After being history matched, the reservoir model was used to test the sensitivity on well spacing, landing zone and hydraulic fracturing pump schedule. Both infill drilling program and green field development scenarios were tested and compared to optimize our field development study. Production history match indicates that natural fractures serve as fluid flow conduit and contribute significantly to the production in Osage. Pressure transient observation shows a similar reservoir behavior in the Osage as opposed to the Woodford. Multiple wells experience productivity reduction over longer production history, indicating near-field damage (such as scaling) and/or far-field damage (such as fracture closure). Introduction of skin factor and pressure dependent permeability captured the trend on productivity behavior in the history match. In addition, the simulation study shed light on the hydraulic fracture geometry that provides direct insight on well spacing and landing zone analyses. Results from the infill drilling program show that staggered design with 3 Osage and 4 Woodford wells per section yields the higher oil recovery. However, using the greenfield sensitivities, and depending on the pumping schedule, hydraulic fractures from Woodford wells show upward growth, draining both formations effectively even without Osage wells. This study provides valuable information about the development strategy in STACK unconventional resources, particularly for scenarios with natural fracture system and multiple targeting zones. The simulation workflow considers well interference in both horizontal and vertical directions simultaneously to optimize oil recovery and reduce operational cost.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".