New Estimation Technique Shifts Assessment of Global Unconventional Gas Resources
Bibliographic record
Abstract
This article, written by Editorial Manager Adam Wilson, contains highlights of paper SPE 148365, ’Global Unconventional Gas Resource Assessment,’ by Z. Dong, S.A. Holditch, SPE, D.A. McVay, SPE, and W.B. Ayers, SPE, Texas A&M University, prepared for the 2011 SPE Canadian Unconventional Resources Conference, Calgary, 15-17 November. The paper has not been peer reviewed. As the world reserves of liquid hydrocarbons from conventional reservoirs peak and begin to decline, natural gas will play an increasingly important energy supply role. Most estimates of the original gas in place (OGIP) reported for world unconventional gas start with Hans-Holger Rogner’s 1997 top-down study. That global estimate is most likely quite conservative because the oil and gas industry has discovered enormous volumes of shale gas around the world since the 1990s. The objective of this project was to estimate the probabilistic distributions of original volumes of gas trapped in coalbed, tight sand, and shale reservoirs worldwide. Introduction To accomplish this objective, the authors reviewed published assessments of coal and conventional and unconventional resources and established the quantitative relationship between unconventional gas [coalbed methane (CBM), tight sands gas, and shale gas] and the conventional hydro-carbon (coal, conventional gas, and oil) resource endowments for North America. Then, they used this relationship to extrapolate original unconventional gas in place worldwide. For the assessments, P10, P50, and P90 are the values for which the probability is 10, 50, and 90%, respectively, that the value will be exceeded. This work established an unconventional OGIP of 83,400 Tcf (P90)–184,200 Tcf (P10), which is 2.6–5.7 times greater than Rogner’s estimate of approximately 32,600 Tcf. Global Unconventional Gas Base. According to Rogner’s estimates, world-wide original in-place resources were approximately 9,000 Tcf of CBM, 16,000 Tcf of shale gas, and 7,400 Tcf of tight sands gas. Rogner’s 1997 global estimate is most likely quite conservative, given the recent discovery of significant shale gas around the world. Resource Triangle. J.A. Masters suggested in a 1979 paper that hydrocarbon resource types can be assigned to various resource classes in a triangular distribution and that their positions in the triangle reflect their abundance, their reservoir quality, and the technology required for recovery (Fig. 1). As one goes lower on the gas resource triangle, the reservoirs are lower grade, which usually means the reservoir permeability is decreasing. These low- permeability reservoirs are much larger than the higher-quality 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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".