Three-Dimensional Inversion of Borehole Gravity Measurements for Reservoir Fluid Monitoring
Bibliographic record
Abstract
Abstract The fluids in the reservoir are redistributed in response to pressure gradients caused by hydrocarbon production, which is often coupled with injection of water or gas. It is known from density logging that the density of pore fluids is an important physical property useful in inferring oil, water, or gas saturation in a rock. Thanks to the density difference of the different phases, the borehole gravity measurement is a candidate for tracking the movement of fluids hundreds of feet away from wellbores. Such data are measured in time lapse to provide the three-dimensional distribution of density changes in time through an inversion procedure. Because gravity is a potential field, the inversion of borehole gravity data is inherently non-unique. The decrease of the sensitivity to the recorded data away from the measurement location is also a challenge for the inversion problem. The approach implemented in this work uses an iterative algorithm that minimizes a global objective function. The objective function includes the data misfit functional and a three-dimensional regularization that is needed to constrain the inversion to reasonable solutions. A weighting function based on the distance between each gravity source and the recording instrument is introduced to help mitigate the geometric decay of gravity kernels with distance from the sensor. Synthetic inversions are presented that indicate borehole gravity can be instrumental in detecting and monitoring a large water front. This study also points out the optimal conditions for using borehole gravity for fluid front monitoring, in terms of number of wells, relative well positions to the target, vertical sampling, and measurement accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".