Application of SEM Imaging and MLA Mapping Method as a Tool for Wettability Restoration in Reservoir Core Samples for SCAL Experiments
Bibliographic record
Abstract
In reservoir engineering, special core analysis experiments (SCAL) are performed in the lab to evaluate the production capabilities of an oil reservoir. A critical component of SCAL experiments is core wettability restoration to its original wettability, i.e., oil wet condition. Typically, aging is performed by saturating the core with oil and aging at reservoir temperature where time is the variable in question dictating whether the resulting restored core is strongly or weakly oil-wet. In the lab, core wettability is often experimentally validated using contact angle measurements or USBM (United States Bureau of Mines) wettability tests, which are often time consuming, expensive and prone to error. In this study we developed a novel method by using Scanning Electron Microscope (SEM) and mineral liberation analysis (MLA) imaging (at low vacuum conditions) to determine the wettability of rocks saturated with reservoir fluids such as oil and brine. For this work a systematic approach was applied with comparing the SEM-MLA method against conventional methods to quantify the degree of uncertainty linked to a) wettability estimation and b) the aging time. We have used a comprehensive suite of core samples such as Berea, Silurian Dolomite and Chalk to represent the bulk of oil reservoirs in the world.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".