Quantitative Properties From Drill Cuttings To Improve the Design of Hydraulic-Fracturing Jobs in Horizontal Wells
Bibliographic record
Abstract
Summary The study proposes a method for quantitative determination of porosity, permeability, and rock-mechanics properties from drill cuttings of at least 1 mm. The porosity value is used for determining a brittleness index by implementing sonic-derived porosity theory and a dipole sonic log from an offset well. A new parameter is introduced in this work to give a quantitative value to microscopic observations related to natural-fracture features in drill cuttings. It is called “frac value” and in conjunction with the brittleness index and permeability constitutes the main result of the methodology: the cut log. Quantitative data extracted from drill cuttings are important because the amount of information collected in horizontal wells drilled through tight formations, including cores and well logs, is rather limited in most instances. This paper is based on a Canadian case study with implications for selecting optimum intervals for hydraulic fracturing in a tight gas reservoir. However, the method should also be suitable for global applications in all types of reservoirs (unconventional and conventional) where good-quality drill cuttings might be available. Data extracted from the previous steps are useful for multistage hydraulic-fracturing 3D simulation of horizontal wells. This provides additional information to stimulation designers for deciding where to initiate hydraulic fractures and how to optimize fracture spacing and fracture size per stage instead of considering a homogeneous reservoir volume throughout the whole lateral section. It is concluded that the proposed method provides a useful tool for evaluation of direct sources of information that are available in many cases (drill cuttings) but are rarely evaluated quantitatively. The proposed method allows improved design of multistage hydraulic-fracturing jobs in horizontal wells.
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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 | 0.004 |
| 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 | 0.000 |
| 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".