Estimates of Stress-Dependent Properties in Tight Reservoirs: Their Use With Core and Drill-Cuttings Data
Bibliographic record
Abstract
Summary The objective of this paper is to present a methodology with drill cuttings for making estimates of porosity, permeability, and compressibility as a function of confining pressures in tight formations. An easy-to-use stress-dependent permeability correlation is developed by comparing results from experimental work including hysteresis at various combinations of overburden and pore pressures in vertical and horizontal core plugs, and permeabilities determined in the laboratory from drill cuttings. On the other hand, stress-dependent porosity and compressibility correlations with respect to permeability as a function of net confining stress (NCS) are also introduced. The work is important because of the presence of stress-dependent slot and/or microfracture porosities and permeabilities in tight formations that can significantly affect reservoir performance and forecasting. Recent work has shown that drill cuttings can be used quantitatively for complete petrophysical evaluation and rock-mechanical-properties estimation (Olusola and Aguilera 2013; Ortega and Aguilera 2014). The methods have been shown to be useful in those instances in which cores and specialized well logs are scarce. Porosity and permeability values obtained from the aforementioned works are extended in this paper to quantitative evaluation of stress-dependent properties. It is concluded that drill cuttings are important direct sources of information that can be used for developing estimates of stress-dependent petrophysical properties particularly in those cases in which cores and specialized logs are scarce or not available. The methodology and stress-dependent-permeability, -porosity, and -compressibility correlations are presented in detail, as well as a practical application for the case of drill cuttings. Although the main and novel contribution is the development of easy-to-use correlations for stress-dependent tight reservoirs with drill cuttings, the correlations can obviously be used if only plug data are available.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".