Bibliographic record
Abstract
Abstract Outline of Problem Gas field surveillance historically has not been given the same priority as oil field surveillance because of a couple of significant stumbling blocks: first, the apparent simplicity of gas reservoirs- at least upon initial examination; and, second, the historically low commodity prices of gas relative to oil. The presumption that gasfield surveillance was easily derived from the knowledge that, in most gas pools, single phase flow existed, leading to the assumption that "tank like" behaviour was occurring. However, on examination of hundreds of gas pools, my viewpoint on that matter has changed dramatically. Of course, today's commodity prices are also changing both the relative and absolute perceptions about the economic importance of gas. Initially, in the Western Canadian Sedimentary Basin (WCSB), industry encountered and dealt with very high quality gas resources, such as those found in Leduc reservoirs. These reservoirs had high permeability and were not compartmentalized; this naturally led to the "tank model" approach. These pools had behaviour as illustrated by Figure 1(a). In the last few years, we have moved from what I would term "Tier One" resources to lower quality ones, as shown in Figure 2. Indeed, a large portion of reservoirs do not exhibit that "tank like" behaviour shown in Figure 1(a); instead, there is often a fairly large pressure gradient across pools and non-tank like behaviour, such as that shown in Figure 1(b). This paper stresses the importance of comprehensive surveillance for those types of fields, using simple methods of production analysis and pressure diagnostic tools. There has been an explosion of research on using gas rate decline methods to characterize reservoirs with an effective permeability less than 0.1 mD (k < 0.1mD). However, the transient gas rate decline and practical analysis of such low permeability gas reservoirs are not the focus of this paper. This paper instead focuses on gas field surveillance for multi-well pools in which wells are in boundary dominated flow. The objectives of any gas surveillance program are relatively simple:What is the Original Gas in Place (OGIP)?;What is the current gas in place?;Where is the current gas in place located?;What is the permeability or capacity (kh) of the wells and pool?;What are the limiting considerations to gas recovery factor (low permeability, liquid lifting problems, low continuity, high back pressure, or water influx)?;What can be done to improve the rate of recovery and the recovery factor? and,Can this be done economically? In order to understand the above questions, we must relate the static model (geological/petrophysical) to the dynamic (production/ pressure data) one. Surveillance Methodology The following work flow has proven to be useful in determining the true potential of gas reservoirs:Examine the production profile of individual wells and the field. Examine the initial production rates (IP), interference effects, types of decline, and decline rates (production diagnostics);
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".