Dynamic reservoir monitoring using similarity analysis of passive source time-lapse seismic images: Application to waterflooding front monitoring in Shengli Oilfield, China
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Bibliographic record
Abstract
In common practice in the oil fields, the injection of water and gas into reservoirs is a crucial technique to increase production. The control of the waterflooding front in oil/gas exploitation is a matter of great concern to reservoir engineers. Monitoring the waterflooding front in oil/gas wells plays a very important role in adjusting the well network and later in production, taking advantage of the remaining oil potential and ultimately achieving great success in improving the recovery rate. For a long time, microseismic monitoring, numerical simulation, four-dimensional seismic and other methods have been widely used in waterflooding front monitoring. However, reconciling their reliability and cost poses a significant challenge. In order to achieve real-time, reliable and cost-effective monitoring, we propose an innovative method for waterflooding front monitoring through the similarity analysis of passive source time-lapse seismic images. Typically, passive source seismic data collected from oil fields have extremely low signal-to-noise ratio (SNR), which poses a serious problem for obtaining structural images. The proposed method aims to visualize and analyze underground changes by highlighting time-lapse images and provide a strategy for underground monitoring using long-term passive source data under low SNR conditions. First, we verify the feasibility of the proposed method by designing a theoretical model. Then, we conduct an analysis of the correlation coefficient (similarity) on the passive source time-lapse seismic imaging results to enhance the image differences and identify the simulated waterflooding fronts. Finally, the proposed method is applied to the actual waterflooding front monitoring tasks in Shengli Oilfield, China. The research findings indicate that the monitoring results are consistent with the actual development conditions, which in turn demonstrates that the proposed method has great potential for practical application and is very suitable for monitoring common development tasks in oil fields.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it