Optimizing Hydraulic Fracturing Treatments for CBM Production Using Data From Post-Frac Analysis
Bibliographic record
Abstract
Abstract Commercial coal bed methane (CBM) development is currently in its infancy in Canada. Often the challenge to commercial development of this unconventional resource is to locate reservoir sweet spots and to economically optimize completion and stimulation design for coal gas extraction. Most CBM wells require hydraulic fracturing to produce at commercial gas rates. It is important to understand that fracturing of coals is very different from fracturing conventional sandstone reservoirs. Coal is a naturally fractured material (cleat system) with very unique rock mechanical properties, and is inherently prone to complex and/or multiple fractures. This may include T-shaped, ‘railroad track’, or branched fractures. This paper will present data from a regional CBM project in Alberta to illustrate the complexity and the challenges of designing fracture treatments in a tectonically active area. Regional differences in in-situ stress are shown to have a large influence on the producibility of a particular coal zone. The frac gradient information derived from the post-frac analysis can be used to locate sweet spots for piloting. Also, the same information can be used for optimizing the hydraulic fracture design and placement in the pilot project. In multiple coal zones, the indirect vertical fracture connectivity (IVFC) technique has been successfully used to reduce the probability of complex fracturing. Comparisons of this technique to the conventional completion technique will be discussed in this paper. Other key technologies in the frac design include formulation of a special nitrogen foam surfactant frac fluid that minimizes frac face damage, orientated perforations in deviated well bores that minimize complex frac geometry, and radioactive tracer logs that are used to verify and optimize frac design. Introduction While the first commercial San Juan Basin coal bed methane well was drilled in 1952(1), the commercial development of coal bed methane reservoirs in North America is a relatively recent phenomenon. The first major development period for CBM reservoirs in the US occurred during the 1984 to 1992 period and was driven by tax incentives. The next major expansion period started in 1998 to present, mainly driven by higher gas prices and improved technology to profitably develop these unconventional reservoirs. Coal reservoirs are not homogeneous and can have very different reservoir and geological characteristics, within the same general area. Many thousands of wells were drilled before the ‘Fairway’ area or sweet spot was found in the San Juan Basin Fruitland coal reservoir. Reservoir characteristics such as permeability, pressure, saturation, and gas content are all important. Geological characteristics such as coal maturity, thickness, frequency of cleating and tectonic setting also help determine the producibility of a CBM reservoir(2,3,4). Completion, stimulation and production techniques can also be very important to the commerciality of a CBM reservoir. These facts lead to the importance of proper technical analysis of the coal and the reservoir quality. This is best accomplished by drilling numerous wells over an area prior to deciding where to attempt a pilot project or commercial venture. Proper laboratory analysis of the coal properties is also extremely important, but will not be covered here.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".