Rapid Assessment of Petroleum Potential of Bantumilli Marginal Oil Field by Using Infrasonic Passive Differential Spectrascopy (IPDS), an Indian Scenario
Bibliographic record
Abstract
Abstract Bantumilli oil field is located in the Krishna - Godavari Basin, in the east coast of India. The field is considered marginal. Quantifying potential in marginal fields involves several challenges like heterogeneous reservoir quality, incomplete reservoir data, production and completion practices. Perhaps the most effective way to establish potential is to conduct a detailed integrated reservoir study, which is often time consuming, expensive and applicable to fields with existing flowing wells. Hence, there is a need for rapid turn around methods that characterize and predict actual volume of oil. Infrasonic Passive Differential Spectroscopy (IPDS) is one such technology that quantifies the hydrocarbon content in a field. The omni present infrasonic passive sound of the earth, transmitted through a reservoir containing differential media i.e., oil, gas and water, produces unique spectral signatures in the frequency range of 0 to 6 Hz. These signatures are used as direct hydrocarbon indicators. Bantumilli field contains hydrocarbons in multiple reservoirs. These reservoirs are thin and sporadic; their petrophysical characters vary conspicuously from one location to the other, do not indicate any specific trend and are not discernible in the seismic sections. IPDS technology was considered the apt solution as it would indicate the presence or absence of hydrocarbon accumulations in the combination traps of structure and stratigraphy. IPDS was executed for delineation of the oil field. Measurements conducted after calibration of the existing wells, resulted in the identification of prospects. Additionally, IPDS reaffirmed the faults, earlier envisaged from the seismic data. The potential of the field was established through IPDS survey. Application of the IPDS technology in marginal fields and by-passed oil in brown fields is significant as it eliminates the risk of drilling dry holes.
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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) | 0.002 | 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".