Characterization of the Microstructure of the Cement-Rock Interface Using Environmental Scanning Electron Microscopy and Micro-Computed Tomography Scan
Bibliographic record
Abstract
Summary Cement-rock interface is a major component of the wellbore barrier system. Leakage may result from the poor bonding between cement and rock interface. In this paper we investigate possible factors that may affect the cement-rock interface bonding. More specifically, integrity of the cement-rock interface was characterized using micro-computed tomography (CT) and environmental scanning electron microscopy (ESEM). Hollow cylinder rock samples were prepared by using rock samples (e.g., Banff dolostone, Pekisko limestone, Doig sandstone, Notikewin siltstone, Montney siltstone, and Wilrich siltstone) collected from different Alberta wells at various depths. Two abandonment cement blends were injected into the rock open hole. After curing the cement-rock samples in water at ambient temperature (≈21°C) and 1,500 psi for 7 days, the samples were then processed into thin sections. By using ESEM (0.05-µm resolution) and micro-CT (11.92-µm resolution) techniques, the 2D and 3D models of the cement-rock interface were developed. Energy-dispersive X-ray spectroscopy (EDS) was conducted to analyze chemical characteristic of the cement-rock samples. Using the CT images, computational fluid dynamics (CFD) models were built to simulate fluid flow through the cement-rock samples. For both cement and rock, there is a nonuniform porosity distribution in radial and axial directions. For most of the cement-rock samples, the highest porosity region in the cement column was found at the cement-rock interface. Optimizing the chemistry of the cement system enhances the cement-rock interface bond by effectively reducing the gap between cement and rock observed in ESEM images. Although cement migration was observed in the rough rock surface in porous rocks, the rock interface and matrix zones have almost identical element concentrations. For the investigated samples, the chance for significant chemical reaction at the cement-rock interface is minimal. CFD simulation based on digital cement models showed that the cement-rock interface has more chance to act as the main flow pathway when intact (low permeability) caprock exists. The sample preparation, image analysis and simulation methods used in this study can be also applied to other cement interface studies (e.g., cement-casing, casing-cement-rock). From the practical field application point of view, the results presented here would help to have a better understanding of the requirements for designing optimum cement formulations to establish effective zonal isolation and reduce the greenhouse gas emissions from oil and gas wells.
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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 | 0.000 |
| 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".