A novel method for quantitatively identifying driving forces and evaluating their contributions to oil and gas accumulation
Bibliographic record
Abstract
Different driving forces govern the formation of distinct types of oil and gas accumulation and yield diverse oil and gas distributions. Complex oil and gas reservoirs in basins are commonly formed by the combination of multiple forces. It is very difficult but essential to identify driving forces and evaluate their contributions in predicting the type and distribution of oil and gas reservoirs. In this study, a novel method is proposed to identify driving forces and evaluate their contribution based on the critical conditions of porosity and permeability corresponding to buoyancy-driven hydrocarbon accumulation depth (BHAD). The application of this method to the Nanpu Sag of the Bohai Bay Basin shows that all oil and gas accumulations in the reservoirs are jointly formed by four driving forces: buoyance (I), non-buoyance (II), tectonic stress (III1) and geofluid activity (III2). Their contributions to all proven reserves are approximately 63.8%, 16.2%, 2.9%, and 17.0%, respectively. The contribution of the driving forces is related to the depth, distance to faults and unconformity surfaces. Buoyancy dominates the formation of conventional reservoirs above BHAD, non-buoyant dominate the formation of unconventional reservoirs below BHAD, tectonic stress dominates the formation of fractured reservoirs within 300 m of a fault, and geofluids activity dominates the formation of vuggy reservoirs within 100 m of an unconformity surface.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".