On Estimating Irreducible Water Saturation in Tight Formations Using Nuclear Magnetic Resonance Relaxometry
Bibliographic record
Abstract
Abstract Most of the Nuclear Magnetic Resonance (NMR) log based permeability models require the estimation of the irreducible water saturation (Swirr). Several methods are available for calculating this parameter using NMR relaxometry. The most straightforward method with the lowest accuracy is to consider a fixed relaxation time (T2) value. It has been suggested to use a T2-cutoff equal to 10 ms for tight reservoirs. Another traditional experimental method involves centrifuging core plugs to Swirr. In this paper, an additional approach to separate free and bound water using NMR relaxation time is introduced. This method involves the area under the amplitude-T2 relaxation time graph. A series of experiments were conducted on 81 core plugs. These samples are mainly from the Western Canadian Sedimentary Basin. Core plugs are from Montney, Nordegg, Mist Mountain, Red Beds, Doig, Killam, Lathom, York River, Wapiti, Teslimkoy, Kesan, and Ordivician Quartz formations. NMR measurements were obtained initially on the dry cores to establish the presence of any liquids that were not cleaned or any isolated porosity. The air permeability was measured using an in-house permeameter. The cores were then brine saturated in two steps of spontaneous imbibition followed by forced imbibition under vacuum. The Archimedes principle was used to measure the sample pore volumes. Porosity was subsequently calculated. NMR relaxation data were then acquired on the brine-saturated cores. Then the core plugs were centrifuged under air to an expected irreducible saturation. NMR relaxation times were obtained on all cores at Swirr. NMR porosity, T2gm, Irreducible Bulk Volume (BVI), and Free Fluid Index (FFI) were calculated. Swirr was calculated with the three aforementioned methods. Excel Visual Basic for Applications (VBA) programming language was employed for analyzing the relaxation times. The Timur-Coates model was applied for permeability calculation using all the aforementioned Swirr estimation methods. Data were analyzed, and discrepancy analysis was conducted. The implemented area analysis method has been used previously in reservoir typing based on formation types and also as a factor in one permeability model. However, this is the first time this approach is used in calculating FFI/BVI exclusively. This method is faster than conventional estimators, and it is the only method that can implement Timur-Coates based permeability models for logging tools. From the experimental point of view, only a single NMR measurement is needed. Centrifuging the cores is not necessary. The possibility of cracking these cores due to spinning is eliminated. This new approach is less computationally demanding, and calculations are easier to perform. It is proven that the fast peak area method is more accurate than the fixed T2-cuttoff and in some cases the centrifuge method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".