Bibliographic record
Abstract
Technology Focus Brant Bennion, in the Journal of Canadian Petroleum Technology Distinguished Authors series, titled his 1999 article on formation damage “The Impairment of the Invisible by the Inevitable and Uncontrollable, Resulting in an Indeterminate Reduction in the Unquantifiable.” This is a brilliant definition of formation damage because it reflects very well the lack of relevant data (in particular, permeability data) that are essential for adequate design of drilling and completion fluids. In addition, the opening sentence in Bennion’s article is as relevant today as it was in 1999: “Formation damage is a hot topic these days—with justifiable reason as we move to the exploitation of more challenging oil and gas reservoirs in tighter, deeper, and more depleted conditions.” In order to avoid some of the detrimental effects of formation damage, a key aspect is laboratory testing of representative core material under representative downhole conditions. Thin sections, dry scanning electron microscopy (SEM), cryogenic SEM, and X-ray diffraction have been used for a number of years to identify the main damage mechanisms, mechanical or liquid, contributing to the observed returned permeability measurement from coreflooding. High-resolution images can be obtained from dry and cryogenic SEM, but they offer only a limited view at any one time. One technique that has been applied recently to identify and quantify potential formation damage is that of microcomputed tomography (CT). This provides high-resolution scans of whole plugs and allows, for example, the identification of changes in pore structure because of fines mobilization, and the visualization of the filter cake after cleanup, depth of mud solids, and filtrate invasion. The combination of micro- CT with techniques previously used for formation-damage analysis is providing new understandings in the interaction of drilling and completion fluids with core material. One of the more frustrating aspects of coreflooding has been how to relate the results obtained to potential well-inflow performance. Computational fluid dynamics is one technique that appears to have bridged this gap. This technique incorporates the data obtained from coreflooding into a model that recreates the actual well geometry, depth of formation damage, mud thickness, and distribution of restrictions in the tubing, such as safety valves. With this approach, production rates can be obtained that provide useful insights into selecting appropriate drilling and completion fluids, for example. Enjoy the papers selected. Formation damage is still a “hot topic.” JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 165092 Case-Study Analysis of Formation Damage Induced by Brine Workover Fluid on Burcioaia Reservoir (Romania) and Research on Damage- Removal Methods by A. Dragomir, OMV Petrom, et al. SPE 165169 Formation Damage and the Importance of a Rigorous Diagnostic: A Case History in Nigerian Deep Water by Jean-Noel Furgier, Total, et al. SPE 169435 Integrated Analysis To Identify and Prevent Formation Damage Caused by Completion Brines: A Colombian Field Application by M.G. Jaimes, Ecopetrol, et al.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.032 |
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".