Development of a Methodology for Hydraulic Fracturing Models in Tight, Massively Stacked, Lenticular Reservoirs
Bibliographic record
Abstract
Abstract This paper describes and critically assesses a common methodology currently used to model hydraulic fractures in geologically complex, fluvial, tight gas reservoirs. A planar 3- D fracture simulator is used with a fully coupled fluid/solid transport simulator. The model incorporates a unique data set from the Piceance basin, Colorado, which produces hydrocarbons from the Cretaceous-age Mesaverde formation. Initially, vertical variations in geo-mechanical rock properties (Young’s modulus, Poisson’s ratio and Biot’s constant) were calculated from well logs. The results were then compared with previous work undertaken on the Mesaverde formation and carried out at the DOE/GRI MWX site. From this analysis, specific correlations were developed for rock properties derived from well logs on a foot-by-foot basis to be used in the hydraulic fracture model. Diagnostic mini-frac injection tests of individual sandstone reservoirs were used to confirm model inputs and develop a valid stress model. Previous attempts to model hydraulic fracture growth in the Mesaverde have been hampered by a lack of detailed input data sets and the inability to accurately determine horizontal rock property variations. This paper outlines a method which uses micro-seismic/tiltmeter data to constrain and verify the model inputs. The resulting frac model is shown to have not only matched the fracture containment but also pressure matched the actual net surface pressure data in this extremely geologically complex area. From these results it is possible to get a better understanding of how fracs grow and interact with complex fluvial reservoirs, allowing operators to better optimize field well performance and completion methods in these geologic settings. Additionally, the minimum critical data required to develop such a model has been identified and will aid operators in developing their data acquisition programs. Although developed in the Rocky Mountain region, the presented technique can be extrapolated to other similar geologically complex reservoirs world-wide.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".