Mapping Fluid Flow in a Reservoir Using Tiltmeter-Based Surface-Deformation Measurements
Bibliographic record
Abstract
Abstract Surface deformation measurements have been used for years in oilfields to monitor production, waterflooding, waste injection, steam flooding, and Cyclic Steam Stimulation (CSS). They have been proven to be a very effective way to monitor the field operations and save money for operators wishing to avoid unwanted surface breeches, casing failures and excessive subsidence due to production. This paper demonstrates that more information can be extracted from surface deformation measurements by inverting the surface deformation for the volumetric deformation at the reservoir level, so the areal distribution of volumetric deformation can be identified. First, a poroelastic model is presented to calculate the deformation due to the volumetric change in the reservoir. Then, a linear geophysical model is formulated to invert for the reservoir volumetric deformation from the measured surface deformation (or tilt). Constraints are added into the procedure as necessary to better resolve the inversion problem. After each inversion, the theoretical surface deformation (displacement, tilt, reservoir compaction and volumetric strain) can be calculated from the inverted volumetric deformation distribution which best fits the measured deformation data (or tilt) at the surface. The technique of mapping fluid flow using surface deformation was applied to real data from a cyclic steam injection project.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".