Progress and insights from worldwide deep coalbed methane exploration and development
Bibliographic record
Abstract
Since 2021, China has achieved significant breakthroughs in deep coalbed methane (CBM) exploration and development, making it a strategic resource for increasing natural gas reserves and production. To further support the high-quality development of deep CBM in China, it is urgent to study the CBM resource endowments and exploration and development status in worldwide major coal-bearing basins. The exploration and development of CBM in the United States, Australia, and Canada started early, currently mainly focusing on the development of medium- to low-rank shallow CBM, characterized by shallow coal seams and high permeability, with production exceeding 10 000 m3/d using vertical wells. However, due to adjustments in oil and gas strategies, the United States and Canada no longer prioritize CBM exploration. Australia, on the other hand, experiments with combined production of coal measures, propelling it to the top of global CBM production. In China, deep CBM exploration mainly focuses on medium- to high-rank deep CBM. The coal seams are characterized by significant depth variation and low permeability. The Ordos Basin has become the largest deep CBM production base. Multiple horizontal wells in the Daning-Jixian and Daniudi gas fields produce more than 100 000 m3 of gas per day. Deep CBM exploration in the Sichuan Basin has made positive progress, and the Junggar Basin shows potential for deep CBM exploration. Experiences from CBM exploration and development shows that breakthroughs in understanding enrichment patterns, advancements in engineering technologies, integrated management models, and industry- supportive policies are important factors for the rapid development of the CBM industry. Increasing exploration efforts for different types of deep CBM, strengthening theoretical and technological research, accelerating the construction of standard systems, and enhancing industry support policies will help foster high-quality exploration and efficient development of deep CBM in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".