Ekofisk 4D Seismic - Seismic History Matching Workflow
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This presentation outlines an integrated workflow that incorporates 4D seismic data into the Ekofisk field reservoir model history matching process. Successful application and associated benefits of the workflow benefits are also presented. A seismic monitoring programme has been established at Ekofisk with 4D seismic surveys that were acquired over the field in 1989, 1999, 2003, 2006 and 2008. Ekofisk 4D seismic data is becoming a quantitative tool for describing the spatial distribution of reservoir properties and compaction. The seismic monitoring data is used to optimize the Ekofisk waterflood by providing water movement insights and subsequently improving infill well placement. Reservoir depletion and water injection in Ekofisk lead to reservoir rock compaction and fluid substitution. These changes are revealed in space and time through 4D seismic differences. Inconsistencies between predicted 4D differences (calculated from reservoir model output) and actual 4D differences are therefore used to identify reservoir model shortcomings. This process is captured using the following workflow: (1) prepare and upscale a geologic model, (2) simulate fluid flow and associated rockphysics using a reservoir model, (3) generate a synthetic 4D seismic response from fluid and rock physics forecasts, and (4) update the reservoir model to better match actual production/injection data and/or the 4D seismic response. The above-mentioned Seismic History Matching (SHM) workflow employs rock-physics modeling to quantitatively constrain the reservoir model and develop a simulated 4D seismic response. Parameterization techniques are then used to constrain and update the reservoir model. This workflow updates geological parameters in an optimization loop through minimization of a misfit function. It is an automated closed loop system, and optimization is performed using an in-house computer-assisted history matching tool using evolutionary algorithm. In summary, the Ekofisk 4D SHM workflow is a multi-disciplinary process that requires collaboration between geological, geomechanical, geophysical and reservoir engineering disciplines to optimize well placement and reservoir management.
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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 | 0.000 | 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 it