3D DC resistivity modeling of steel casing for reservoir monitoring using equivalent resistor network
Bibliographic record
Abstract
Energized steel casings in oilfields channel electric currents generated for a surface resistivity survey down to the depth of target reservoir, enabling the use of electric methods in reservoir monitoring. Numerical simulation of such a survey often requires refined meshes to simulate the casing. In order to avoid the use of small cells, we propose a method that treats the earth’s conductivity model as a 3D equivalent resistor network (ResNet), and a casing as a parallel-circuit wire conductor. Numerical comparisons with a cylindrically symmetric code and with a finite element code show that ResNet provides accurate and efficient solutions to the current along the casing and to the electric field on the surface. Using ResNet, we further study how the current distributes along a casing: (1) The casing current approaches a nearly linear decay if the casing conductivity is sufficiently high; (2) The casing current is more sensitive to variation in the extent of a conductive injectate than that of a resistive one; (3) Under special circumstances the current can flow into the casing from the surrounding. Presentation Date: Wednesday, October 19, 2016 Start Time: 1:30:00 PM Location: 174 Presentation Type: ORAL
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".