Probabilistic Forecasting of Unconventional Resources Using Rate Transient Analysis: Case Studies
Bibliographic record
Abstract
Abstract Reliable, early determination of long term production and ultimate recovery in oil and gas reservoirs is of utmost importance to E&P companies, reserves auditors and investors. In conventional reservoirs, the EUR can be reliably estimated once the drainage volume (hydrocarbon pore volume) has been established. This can be done using Rate Transient Analysis (RTA) if the presence of boundary dominated flow can be observed in the data. Unfortunately this approach is not easily applied to tight, fractured reservoirs because of the complexity of these reservoirs (which leads to non-unique reservoir characteristics) and the presence of persistent transient flow (which leads to non-unique estimations of ultimate recovery). In some instances, boundary dominated flow may not be observed until several years have elapsed during the producing life of the well. In recent years, there have been numerous contributions to the science of well performance-based methods for estimation of ultimate recovery of unconventional resources. Wattenbarger et al. (1998) and Brown et al. (2009) propose analytical techniques while Ilk et al. (2008), Valko and Lee (2010) and Duong (2011) have each proposed new empirical decline curve equations. While each of these methods has value, they do not specifically address the problem that underpins all unconventional well analysis, uncertainty. In 2011, the authors proposed the use of probabilistic rate transient analysis to help quantify this uncertainty. This approach acknowledges the non-uniqueness inherent in the RTA model inputs and allows for the systematic investigation of an allowable parameter space based on acceptable ranges of inputs such as the conductivity, spacing, complexity, length and height of the fractures and the matrix permeability. The result is the full set of possible production forecasts (in as much as the model can be said to capture the physics of the problem) from which the "most likely" production profile can be extracted and that help define the uncertainty in long-term recovery for the play. In this paper, we will further explore probabilistic rate transient analysis by presenting a case study from the Montney play in Canada. The primary objective of this work is to illustrate that a probabilistic approach can be practical, reliable and systematic, offering a viable alternative (or complement) to the standard deterministic techniques.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".