Horizon Attributes and Fracture-Swarm Sweet Spots in Low-Permeability Gas Reservoirs
Bibliographic record
Abstract
Abstract Horizon attributes, i.e., attributes that numerically describe geometric characteristics of interpreted horizons from conventional (i.e., p-wave) 3-D seismic volumes, hold considerable potential for identifying fracture-swarm sweet spots in low permeability reservoirs. Typically, these attributes (e.g., dip, azimuth, and curvature) are used to define subtle faults that are near the limit of seismic detectability. These subtle structures can play important roles in compartmentalizing conventional reservoirs. However, in low permeability gas reservoirs where fracture permeability is critical, these same attributes can be used to define high-permeability fracture swarms. We illustrate this point with three case studies, two clastic the other carbonate, from the San Juan Basin area of northwestern New Mexico. The Paradox Formation is a Pennsylvanian age low permeability carbonate reservoir. At Ute Dome Field, production characteristics indicate that fractures are the main control on gas production from these carbonates. Comparison of horizon attributes from this level with production data shows that these attributes are defining high-permeability fracture swarms associated with faults. The Mesaverde Group consists of Cretaceous age clastics and is a tight reservoir in the Blanco Field. Again, horizon attributes (including curvature attributes) can be used to define fault-related fracture swarms that will produce at higher rates than surrounding areas. The Dakota Sandstone is another Cretaceous tight gas sandstone. A map of horizon dip in one area shows a trend that is associated with anomalous production from two wells. However, these two wells cannot be described as "sweet spot" wells because their production is not anomalously high. These observations indicate that development drilling plans for low permeability reservoirs should take into account geologic heterogeneity that can be associated with fracture swarms. Undrilled fracture swarms should be targeted to produce high-rate wells. On the other hand, offset wells should specifically avoid drilling into previously tapped fracture swarms to avoid drainage interference. Other factors that need to be considered are: a) the orientation of the fractures with respect to in-situ stress directions, and b) lithologic (i.e., stratigraphic) control on fracture density.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".