Analysis of Production Data in Shale Gas Reservoirs: Rigorous Corrections for Fluid and Flow Properties
Bibliographic record
Abstract
Abstract Analysis of long-term linear flow periods associated with shale gas production has received much attention in recent literature as a means of obtaining information about stimulation efficiency. However, the most popular methods for analysis (ex. square-root of time plot) can lead to incorrect characterization. Nobakht and Clarkson (2011a) demonstrated that the square root-time plot may not be a straight line for constant gas rate production linear flow and the non-linear shape may lead to incorrect flow regime identification. The square root-time plot is however a straight line for constant flowing pressure (Nobakht and Clarkson, 2011b). Ibrahim and Wattenbarger (2005; 2006) and Nobakht and Clarkson (2011b) showed that using the slope of square root-time plot, for constant flowing pressure constraint, leads to an overestimation of fracture half-length. Additional important considerations for shale gas analysis are non-Darcy flow and non-static reservoir properties. Clarkson et al. (2011) demonstrated that ignoring gas slippage effects, thought to be important in ultra-low permeability reservoirs, can cause errors in reservoir characterization. They incorporated slippage into pseudo-variables for production data analysis, as has been done with non-static permeability (Thompson et al., 2010). Finally, Nobakht et al. (2011) extended the methodology proposed by Nobakht and Clarkson (2011b) to properly analyze linear flow in the presence of slippage and desorption. The purpose of the current work is to evaluate the current methods for analyzing linear flow in shale gas reservoirs, and establish which method is the most accurate for reservoir characterization. First, recent studies addressing linear flow under constant flowing pressure and constant gas rate production are briefly reviewed. Then, a comparison among the above-mentioned methods for calculating fracture half-length or contacted matrix surface area is made. It is shown that Nobakht et al. (2011) method yields the fracture half-lengths that best match the expected values for constant flowing pressure. Finally, we present a method for analyzing linear flow for real production data, where neither flowing pressure nor gas rate is constant. The method is validated using three numerically-simulated cases. It is found that this method works well for the three cases provided.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".