Use of Dynamic Data and a New Material-Balance Equation for Estimating Average Reservoir Pressure, Original Gas in Place, and Optimal Well Spacing in Shale Gas Reservoirs
Bibliographic record
Abstract
Summary The average reservoir pressure is a key parameter in material-balance calculations, but its determination is challenging when dealing with shales because of their low and ultralow permeabilities. This paper presents an easy-to-reproduce methodology for calculating the average reservoir pressure from flowing data, and its use in a new material-balance equation (MBE) that considers the simultaneous contribution of free, adsorbed, and dissolved gas. The procedure developed in this paper uses a modified gas-compressibility factor (Z′) introduced in the new MBE. Because Z′ accounts for the combined effect of free, adsorbed, and dissolved gas, then total original gas in place (OGIP) can be determined from extrapolation of the MBE straight line to an average reservoir pressure equal to zero. Drainage area can be estimated on the basis of calculated OGIP and volumetric equations. As such, the methodology offers the potential to help improve well spacing in shale gas reservoirs in such a way that no stranded gas is left in the reservoir, or that excess wells are not drilled in the field. This can help to improve recoveries from shales by assisting in the determination of the optimal number of wells needed to drain a given play efficiently. In conventional reservoirs, a well is shut in, and the average reservoir pressure is determined from the corresponding pressure-buildup test. But, for the case of unconventional shale gas reservoirs, shutting the wells in is unacceptable because of the long time it would require for estimating average reservoir pressure. The methodology developed in this paper for shale gas reservoirs circumvents this problem by using dynamic data. Production data from multistage hydraulically fractured horizontal wells completed in a Canadian shale gas reservoir are used for testing the effectiveness of the new methodology. Comparison of typical well-spacing values vs. the drainage area calculated with the new methodology leads to the conclusion that, probably, only 40% of the gas is being drained efficiently. The novelty of this work relies on the development of a methodology for calculating average reservoir pressure, OGIP, drainage area, and optimal well spacing in shale reservoirs through the combination of dynamic data and a new MBE that considers simultaneously the effects of free, adsorbed, and dissolved gas.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".