A Method for Estimating Hydrocarbon Cumulative Production Distribution of Individual Wells in Naturally Fractured Carbonates, Sandstones, Shale Gas, Coalbed Methane and Tight Gas Formations
Bibliographic record
Abstract
Summary A method, based on factual observations of naturally fractured reservoirs in several countries, is presented for estimating distribution of hydrocarbon cumulative production in wells drilled in fractured reservoirs of Types A, B or C. These observations indicate that in reservoirs of Type C, most of the cumulative production is provided by just a few wells, while the majority of the wells contribute a small part of the reservoir cumulative production. In reservoirs of Type B, the number of wells contributing significantly to cumulative production becomes larger relative to the case of Type C reservoirs. Finally, in reservoirs of Type A, a large number of wells contribute to field production, as compared with Type B reservoirs. The method is shown to be useful for tackling problems of practical importance in naturally fractured reservoirs, including performing or not performing infill drilling, estimating the variation in cumulative hydrocarbon production per well in a given reservoir and estimating the number of wells that might be required for a given field hydrocarbon recovery. The method is illustrated using data from various fractured reservoirs, including the Barnett shale and sandstone reservoirs in the United States, carbonate reservoirs in Mexico and Venezuela and coalbed methane reservoirs and tight gas sands in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".