Analyzing Variable-Rate/-Pressure Data in Unconventional Gas Reservoirs
Bibliographic record
Abstract
This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 149472, ’Analyzing Variable-Rate/-Pressure Data in Transient- Linear Flow in Unconventional Gas Reservoirs,’ by P. Liang, SPE, L. Mattar, SPE, and S. Moghadam, SPE, Fekete Associates, prepared for the 2011 Canadian Unconventional Resources Conference, Calgary, 15-17 November. The paper has not been peer reviewed. Often, wells in unconventional gas reservoirs exhibit linear flow during their transient period, and this transient behavior can last for several years. Currently, industry uses the type-curve-matching technique to analyze this linear flow. The common type curves assume that wells produce at constant rate. However, the production rate usually is variable and, in fact, is closer to a constant-pressure operation. The constant-pressure type curve is useful, but not suitable when both rate and pressure vary. It is necessary to have an easy-to-use method for analyzing variable-rate/-pressure data in linear flow. Introduction First, the formulation, type curve, specialized graphs, and superposition time used to analyze transient-linear flow for a deeper understanding of the theory are reviewed. Second, a practical and effective method for analyzing variable gas-production data is illustrated. In this development, the effect of skin on the type curve and on the specialized graph was studied. The constant-pressure solution was converted to its constant-rate equivalent by use of material-balance time, and it was found to be acceptable for practical purposes. Real time was converted to corrected pseudotime to account for variable gas properties, and it was determined that the effect would be small in the analysis of actual production data. The effect of outliers on superposition time also was investigated. The dominant flow regime for wells in most unconventional gas reservoirs is linear flow. The reservoir model in Fig. 1a demonstrates linear flow. A vertical well is drilled in the center of a rectangular reservoir with a biwing hydraulic fracture. The length of the fracture is the same as the width of the reservoir, and the fracture is assumed to have infinite conductivity. These assumptions form the basic reservoir model of linear flow, from which the linear-flow solution was developed. Although this model is simple, it is suitable for analyzing more-complicated reservoir geometries. For example, Fig. 1b represents a cased-hole horizontal well with a number (nf) of equally spaced fractures. All the fractures are assumed to have the same fracture half-length xf. No-flow boundaries (dashed lines in Fig. 1b) form between the fractures. The performance of this system is equal to nf times that of the biwing fracture shown in Fig. 1a.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".