Reconciling Empirical Methods for Reliable EUR and Production Profile Forecasts of Horizontal Wells in Tight/Shale Reservoirs
Bibliographic record
Abstract
Abstract Confidently establishing single well and/or aggregated production profiles, particularly estimated ultimate recovery (EUR), is both an important and challenging process in unconventional reservoirs. Numerous papers have proposed forecasting techniques, but four fundamental approaches dominate: Empirical decline curve analysis (DCA), such as multisegment Arps, the modified stretched exponential production decline (YM-SEPD) model, Duong's method, and power-law methods. Rate-transient analysis (RTA), which can include corrections for special dynamic mechanisms (e.g., stress-sensitivity, multiphase flow, adsorption/desorption). Numerical simulation for history-matching and forward modeling. Volumetrically determining in-place resources based on geological data and then applying a recovery factor deemed suitable for the reservoir system and depletion scheme. Each of these approaches can be implemented using either probabilistic or single-point estimates. Seidle et al. (2016) recently outlined recommended methodologies to accurately forecast time-series volumes and ultimate recovery in unconventional systems. A key recommendation in Seidle et al. (2016) is that the multiple approaches listed previously should be reconciled to establish confidence in forecasts and ultimate recovery estimates. However, Seidle et al. (2016) does not detail the means to achieve this goal. This paper establishes a methodology/workflow to reconcile the different types of empirical DCA methods, which should serve as a starting point for the ultimate goal of reconciling the four fundamental approaches listed. This paper compares and contrasts the DCA methods applied to various field cases in prominent Canadian [Western Canadian Sedimentary Basin (WCSB)] and US unconventional (oil and gas) plays (Bakken, Barnett, Cadomin, Eagle Ford, and Niobrara). Data sets are selectively chosen based on data quality and history length to increase confidence in the appraisal of the DCA approach. Hindcasting is applied to validate the results and conclusions. From analysis of a number of wells in different types of reservoirs, a new workflow (methodology) is proposed and validated with hindcasting that allows practically and accurately reconciling EURs based on various empirical methods. This manuscript is the first paper to discuss systematic reconciliation of EURs from varying approaches for horizontal wells in tight/shale reservoirs.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".