Life-Cycle Decline Curve Estimation for Tight/Shale Reservoirs
Bibliographic record
Abstract
Abstract Low-permeability (tight) and shale (gas and oil) reservoirs have emerged as a significant source of energy in North America. Recent advances in technology, such as long horizontal lateral/multi-lateral drilling combined with hydraulic fracturing, and new surveillance techniques, have enabled commercial production from ultra-low permeability reservoirs, previously considered source or cap-rock, not reservoirs. Forecasting well production for reserves estimation, hydraulic fracture stimulation optimization, and development planning remains a challenge because of complex reservoir behavior and flow geometries associated with current wellbore architectures/stimulation treatments used to exploit tight formations. Depending on the completion design, transient flow periods can last for weeks to years, and hence traditional methods requiring boundary-dominated flow are strictly inapplicable for most of the commercial life of many wells completed in tight formations. Recently, several analytical (type-curve, flow-regime analysis and simulation) and empirical approaches have been introduced to match and forecast tight reservoir production. The challenge is to develop routine techniques that can be used to forecast tight formation production, while adequately addressing the complex physics of the problem. In this work, we build on recent attempts to combine analytical and empirical methods ("hybrid" methods) for forecasting tight/shale gas reservoirs completed with multi-fractured horizontal wells. We forecast the homogenous completion (equal hydraulic fracture length) case using established analytical procedures for transient linear flow (pre fracture interference), combined with the Arps decline curve for late-time (boundary-dominated) flow. We also examine the heterogeneous completion (unequal hydraulic fracture length) case to establish the impact of heterogeneities on decline characteristics post fracture-interference. Finally, we present an innovative method for designing hydraulic fracture and well spacing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".