Application of Electromagnetic Scanning to an Australian Frontier Basin with Complex Bathymetry
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Bibliographic record
Abstract
Apache is exploring a large block offshore Australia. Part of the area is covered with good quality 3D seismic and a number of prospects have been identified from this data. Additional potential prospects were inferred from 2D seismic in the relatively under-explored deep water area of the block. With the current high cost of 3D seismic and drilling programs, it was considered very important to high-grade the prospects and to correctly assess the prospectivity of the deep water area.Initially it was thought that traditional target-oriented CSEM surveys over each prospect would provide the required information. However, after further study, it was decided to cover the entire area with electromagnetic scanning. This approach provides a coarse 3D view of the entire area providing information not just about prospects identified from seismic, but potentially also revealing new hydrocarbon leads.A highly rugose seafloor in combination with a high resistive overburden of varying thickness appeared as a major concern for the scanning survey. A wide, deep submarine canyon provided both operational challenges and data processing issues. The rugosity of the seafloor and varying overburden thickness constitute significant local as well as regional variations to the background resistivity distribution, making it difficult to extract potentially hydrocarbon related anomalies from the scanning data.A novel approach to dealing with this problem was adopted, which takes advantage of complementary information from existing well logs and the available seismic data. A number of 1D inversions constrained by resistivity logs were performed at various locations across the survey. The results of the 1D inversions were then used to build a reference resistivity model that conforms to the bathymetry and the seismically derived overburden thickness. Detailed 3D simulation of the scanning survey for this resistivity model generated synthetic reference responses, which adequately account for most of the bathymetry and overburden related variations in the scanning data. Using these synthetic reference responses to normalize the scanning data, a number of interesting anomalies became apparent, one of which coincides with a known oil reservoir. The same anomalies had been masked by regional trends in previous results obtained by conventional single-receiver referencing.The results obtained significantly increased our confidence in the interpretation of the scanning data and highlight the increased value obtained from an integrated analysis with complementary geophysical data.Introduction. Electromagnetic scanning is a reconnaissance application of seabed logging [1] which employs coarse grid acquisition to assess the prospectivity of large areas [2], [3]. The idea is to detect potentially hydrocarbon-bearing resistive formations independent of their seismic expression and high-grade those prospects already identified from seismic data.We present results from a recent scanning survey conducted in an Australian offshore basin as part of a major exploration campaign involving modern 3D seismic acquisition and the drilling of exploration wells. The survey covered an extensive area of 1650 square km where water depths range from approximately 100 m to more than 2500 m. The area hosts a producing oil reservoir and a number of exploration prospects previously identified from 2D and 3D seismic, which had been available over part of the survey area.The scanned basin is characterized by a highly rugose seafloor topography and a complex geology with rock properties that have challenged seismic interpretation in the past. From a seabed logging perspective, the complexity of the geology manifests itself mainly through a carbonate-dominated overburden of largely varying thickness, where resistivity data from wells indicate a sharp increase in resistivity with depth. The prospects are located in the stratigraphic sequence below the overburden at 400-3500 m depth below seafloor and range from about 10 to 20 square km in size.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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