Migration of Liquid Hydrocarbons in Organic-Rich Reservoirs Based on Geochemical and Well Log Analysis Explained with the use of New Practical Model
Bibliographic record
Abstract
Abstract This paper develops a new conceptual liquid-hydrocarbons migration model for practical application in organic-rich mudstone reservoirs. The method uses a combination of well log formation evaluation data, micro-resistivity image logs, nuclear magnetic resonance, and geochemical measurements obtained from either drill-cuttings or core data. In the method proposed in this paper, organic matter (OM) geochemical indicators from organic petrography (thin sections), vitrinite reflectance, TOC from Leco analyzer, Rock-Eval pyrolysis including S1, S2, and Tmax are integrated with triple-combo and NMR logs so that both lean and organic-rich intervals can be identified. The addition of natural fracture intensity to the interpretation package makes the methodology quite unique. After applying cut-offs for each individual parameter, two types of migrations are identified that could take place in these source-rock reservoirs: primary and secondary migration. The model is applied in a wildcat vertical well that penetrated several stacked organic-rich mudstones. The formation gross thickness of the evaluated section is around of 2800 ft-TVD divided into six main formations composed mainly by siliciclastic minerals with a moderate carbonate content, and low presence of clay minerals. The source-rock reservoirs penetrated by the well can be sub-divided into (1) naturally-fractured, (2) tight and (3) hybrid reservoirs. Reservoirs in sub-division (1) are very important because they might be able to produce oil without the need of hydraulic fracturing. Reservoirs in sub-division (2) have natural fractures but their scale is very small to allow any oil production. Consequently, they must always be hydraulically fractured. Reservoirs in sub-division (3) might or might not need hydraulic fracturing. Intervals with high organic carbon content, S1, oil saturation index, and geochemical index but low natural fracture intensity indicate tight reservoirs, and they likely correspond to the hydrocarbon source that charged the juxtaposed naturally fractured or hybrid reservoirs. On the other hand, several intervals that present poor TOC, are highly brittle and naturally-fractured, and are connected with tight organic-rich intervals. Thus, the potential of these naturally fractured intervals to produce oil is quite significant. The novelty of the method developed in this paper permits analyzing primary and secondary migration in the source rock. The method further permits identifying the type of reservoir (naturally fractured, tight and/or hybrid) penetrated by the exploratory wells. This allows ranking the most prospective intervals as well as optimum landing zones for future horizontal or geosteered wells to be drilled in neighboring areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".