A Semi-Analytical Method for Forecasting Wells Completed in Low Permeability, Undersaturated CBM Reservoirs
Bibliographic record
Abstract
Abstract Exploration and development of coalbed methane (CBM) reservoirs is ongoing in the Asia Pacific region, with low permeability, undersaturated coals being the target in some areas. Horizontal wells are typically required to produce low permeability CBM reservoirs at commercial rates, and may experience a period of single-phase (water) transient flow, followed by a transition to two-phase (water+gas) transient flow. Wells exhibiting this behavior are particularly difficult to model analytically, yet simple analytical and semi-analytical tools are desirable for making quick, but reasonably accurate forecasts. The purpose of this work is to therefore provide a new forecasting methodology for low permeability, undersaturated CBM wells. The new method for forecasting CBM wells introduced in this work is based on the concept of a dynamic drainage area (DDA), recently adapted to forecast tight black oil and gas condensate wells exhibiting two-phase flow. A critical advancement provided in the current work is modeling the transition from single-phase flow of water to two-phase flow of gas and water during the transient linear flow period, which was not attempted for the black oil/gas condensate cases. Above desorption pressure, material balance, performed on the dynamic drainage area to determine average pressure in the distance of investigation, is combined with the linear flow productivity index equation to forecast water production at each time step. For two-phase flow below desorption pressure, the two-phase version of the linear productivity equation and material balance equations for water and gas are solved iteratively for pressure, saturation and fluid production rate within the DDA. The gas material balance equation accounts for desorption effects. In order to calculate the DDA at each time step, the linear flow distance of investigation equation is used. Importantly, using the DDA method, variable operating conditions (flowing pressures) may be accounted for, which is critical for CBM wells during early stages of production. The new semi-analytical DDA model is used to history-match production data from an actual horizontal well completed in a low permeability, undersaturated, western Canadian CBM reservoir. Further, the match is compared to that obtained from rigorous numerical simulation. The history-match of the field data is reasonable with the new model and comparable to numerical simulation. The new model is however easier to setup and less data-intensive than numerical simulation, providing a practical alternative to CBM reservoir engineers who are responsible for generating a large number of forecasts.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".