Reservoir Quality Evaluation for Stimulation Design in Low Permeability Gas Development
Bibliographic record
Abstract
Abstract The exploration of Western Canadian and US Rocky Mountain deep, low permeability gas has seen a recent surge as a result of robust gas prices and supply issues. Most of these tight gas formations require stimulation to produce at commercial rates. However, the unique tight gas reservoir characteristics often result in completion and stimulation practices that are either unsuccessful or unrepeatable. One of the most challenging tasks is to estimate reservoir quality prior to stimulation design, and since most of these wells do not have core, operators often rely solely on well logs to select the pay intervals and to estimate permeability. We have developed a comprehensive reservoir quality evaluation methodology that integrates detailed petrographic and petrophysical core and cutting analyses to greatly improve our understanding of the reservoir rock characteristics, and has resulted in dramatic increases in well stimulation success. The knowledge has been used to:improve the completion design by selecting the appropriate perforation intervals;optimize hydraulic fracturing or acid stimulation design;select proper completion and stimulation fluids to minimize formation damage; andassist in benchmarking well performance in adjacent producing areas. Integrated geologic and engineering approach is also important in developing unconventional tight gas reserves. In one field example the wells exhibit similar log characteristics; however, the productivity was proven to be vastly different due to various degrees of pyrobitumen occlusion that was discernible from thin section analysis of cuttings. In another example, phase trapping of fracturing fluid near the fracture face was prevented by realizing the presence of micro-fractures in the low permeability rock. Judicious selection of frac fluids is paramount in situations where micro-porosity and microfractures are abundant and the rock is undersaturated. The understanding of pore geometry provides estimation to formation permeability, which is essential to optimize hydraulic fracture size, fluid rheology, proppant concentration and size, and pump rate, to ensure maximum economic return on what are typically expensive completions. Introduction The Rocky Mountain region in US contains large accumulations of gas in non-marine unconventional (lowpermeability) reservoir rocks of Tertiary and Cretaceous age. Most of the gas occurs in the pores of fine-grained sandstone and siltstone. In the Canadian foothills, gas is being produced from low permeability formations in the deep basin of NW Alberta and NE British Columbia (1). Although the estimated gas resources in these rocks is very large, individual sandstone gas pools have limited lateral and vertical extent. Variation in the geometry of these reservoir rocks is due to relatively impermeable shale and carbonate beds inter-bedded with the sandstones, abundant cross-cutting channel-formed sandstones, and a complex diagenesis. Most of these tight gas formations require stimulation to produce at commercial rates. Due to the costs of coring deep, multiple pay zones, operators often choose to rely solely on well logs to select pay intervals and to estimate key reservoir properties. However, frequently the highly variable nature of these formations is not reflected by conventional well logs and errors are made.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".