Development of Specialized Plots for Production Data Analysis of Tight Reservoirs with Secondary Fractures
Bibliographic record
Abstract
Abstract Many fractured horizontal wells are completed in tight oil/gas or shale gas reservoirs which have significant networks of interconnected secondary fractures (SF). However, the existing linear transient dual- and triple-porosity models do not properly account for SF. While the dual-porosity model assumes negligible SF, the linear sequential triple-porosity model assumes negligible fluid transfer between the rock matrix and hydraulic fractures. Hence, the application of these models for production data analysis of fractured horizontal wells could result in unreasonable reservoir/fracture parameter estimates and hydrocarbon forecast. For this reason, the quadrilinear flow model (QFM) was developed to account for matrix—hydraulic fracture communication. Although QFM properly accounts for the contribution of SF during reservoir depletion, reservoir parameter estimation from its type-curve matching procedure has a high degree of uncertainty. This paper proposes simplified QFM flow regime equations to reduce the uncertainty associated with reservoir parameter estimation. This study carefully analyzes the general QFM solution by observing the flow regions from dimensionless rate and pressure type-curves. This solution is simplified by eliminating dimensionless parameters with negligible contribution to fluid depletion within the duration of each flow region. The resulting simplified equations are analytically inverted from Laplace space to time space. The simplification process 1) yields flow-region analysis equations for the specialized rate-normalized pressure and pressure derivative plots and 2) explains the possible physics behind the flow-regions. The effect of secondary fracture networks on reservoir depletion can be investigated by applying QFM analysis equations on specialized rate-normalized pressure and pressure derivative production data plots. The choice of these plots is based on operational well constraints and observable flow regions. The analysis equations are applied to interpret production data of two multifractured horizontal wells completed in the Cardium and Bakken Formations. The results estimate effective half-length of hydraulic fractures, investigate the presence/absence of secondary fractures and propose a workflow for handling the uncertainty in reservoir parameter estimation when applying the specialized analysis equation plots.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".