Estimation of Contacted and Original Gas-in-Place for Low Permeability Reservoirs Exhibiting Linear Flow
Bibliographic record
Abstract
Abstract Recent advances in drilling and completion technology have allowed commercial exploitation of ultra-low permeability gas reservoirs. Horizontal wells that have been stimulated through multiple hydraulic-fracturing stages are the most popular method for exploitation. Due to the extremely low permeability of these reservoirs, boundary-dominated flow may not be reached for a significant period of time making estimation of original gas-in-place (OGIP) difficult. In these reservoirs, it is important to estimate contacted gas-in-place (CGIP), which is some fraction of OGIP, at any point in time. One of the most popular methods for estimating OGIP in conventional reservoirs is flowing material balance (FMB), which is based on the assumption of boundary-dominated flow. However, this method has been used in the literature to estimate CGIP when dealing with transient data. In this paper, we test the applicability of the traditional FMB plot for calculating CGIP in low-permeability systems. It is found that even when dealing with a gas reservoir that is depleted significantly, FMB underestimates the total OGIP. It is also found that using FMB alone can result in misleading interpretations. For example, the FMB plotting method makes the data look like boundary-dominated flow, whereas other diagnostic tools do not indicate boundary-dominated flow. This means that one can misinterpret the gas-in-place from FMB as total OGIP, which consequently affects the reservoir characterization and reserves evaluation. Finally, we investigated whether using a material balance pseudo-time calculated using the average pressure in the region of investigation, as oppose to average reservoir pressure, will correct underestimating OGIP for the case that the reservoir is significantly depleted. This study is of practical interest for determining how much of the reservoir is contacted at each stage of flow of a multi-fractured horizontal well.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".