Work With Chemical Additives Advances Understanding of Relative Permeability Shifts
Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201520, “Advances in Understanding Relative Permeability Shifts by Imbibition of Surfactant Solutions Into Tight Plugs,” by Mohammad Yousefi, Lin Yuan, and Hassan Dehghanpour, SPE, University of Alberta, prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, Colorado, 5–7 October. The paper has not been peer reviewed. Various chemical additives have been proposed recently to enhance imbibition oil recovery from tight formations during shut-in periods after hydraulic fracturing operations. In the complete paper, the authors develop and apply a laboratory protocol mimicking leakoff, shut-in, and flowback processes to evaluate the effects of fracturing-fluid additives on oil regained permeability. A conventional coreflooding apparatus is modified to measure oil effective permeability (koeff) before and after the surfactant-imbibition experiments. Methodology Proposed Technique for Measuring Oil Effective Permeability. Despite the simplicity of the steady-state method, measuring permeability of tight rocks with this technique is challenging because of its time-consuming nature and the fact that accurate measurement is necessary of extremely low flow rates corresponding to low injectivity of tight rocks. The authors use a pair of plugs from a well drilled in the Montney formation that is a stratigraphic unit of the Lower Triassic age in the western Canadian sedimentary basin located in British Columbia and Alberta. It is mainly a low-permeability siltstone reservoir. In the modified coreflooding apparatus, the authors reduce the effect of compressibility in order to reduce the duration of the transient period by approximately one order of magnitude. Because monitoring changes in pressure is much easier and more accurate than monitoring flow-rate changes, a constant flow-rate mode is used and pressure is recorded with time. Oil is injected at different constant flow rates (qo), and the inlet pressure is monitored. The stable pressure difference across the plug is recorded for each flow rate. After steady-state conditions are reached based on the pressure profile, the qo is increased. This process is repeated until four stable pressure differences corresponding to four different qo are obtained. After the highest qo is reached, it is decreased in similar steps to check the repeatability of each data point. The permeability is calculated with the Darcy equation and slope of the qo vs. stable pressure difference across the plug.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".