Improved Identification of Pay Zones through Integration of Geochemical and Log Data: A Case Study from Upper Assam Basin, India
Bibliographic record
Abstract
Abstract The upper Paleocene-lower Eocene clastic reservoirs constitute one of the most important hydrocarbon-producing horizons in the Upper Assam basin of northeast India. These reservoirs are characterized by the presence of (1) normal gravity oil (23-31° API), (2) low to intermediate gravity oil (13-23° API), and (3) gas and light oil/condensate (> 31° API). The sandstone reservoirs show complex wire-line log signatures, which are commonly misleading. The main impediments to understanding the nature of reservoir fluid include (1) distinguishing low gravity, intermediate gravity, and normal gravity oil-bearing zones and (2) resolving the problem of density (RHOB)-neutron porosity (NPHI) crossover for gas-bearing, as well as oil-bearing, reservoirs. Geochemical analyses of sidewall core extracts by thin layer chromatography with flame ionization detection (TLC-FID) and gas chromatography (GC) provide valuable, cost-effective input for reservoir fluid characterization. The important parameters needed to identify the nature and composition of the reservoir fluid include (1) the bulk composition of the sidewall core extract (i.e., % saturated hydrocarbons, % aromatic hydrocarbons, and % resins + asphaltenes), (2) amount of extract in mg/g of rock, (3) GC fingerprint, and (4) the ratio of pristane to n-C17. These parameters, integrated with geological and geophysical (wire-line logs) evidence, yield more accurate and reliable formation evaluation criteria. The technique is simple and inexpensive and may find application as an additional formation evaluation tool in any geological setting.
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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) | 0.000 | 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".