Reservoir characterization of a mixed siliciclastic-carbonate succession, Middle Cretaceous, southeastern Iraq
Bibliographic record
Abstract
Mixed siliciclastic-carbonate deposits accumulated in the passive continental margin of the Arabian plate are commonly found in the Mesozoic stratigraphic interval in southeastern Iraq. As important hydrocarbon reservoirs in the area, the Cretaceous strata have attracted great interests of researchers to study their sedimentary features, depositional processes and petrophysical properties. However, in-depth studies considering the systematic evolution of a mixed siliciclastic-carbonate succession in the context of sequence stratigraphy and its control on the petrophysical properties of the rocks are still rare. In this study, two representative formations (i.e., the Nahr Umr and the Mishrif formations) that constitute a seismic-scale strata mixing (sensu Chiarella et al., 2017) were fully investigated. An integrated dataset, including core and thin section descriptions, well logs, and results from core experiments (e.g., XRD analysis, grain size distribution, porosity, and permeability measurements), was employed to perform a detailed analysis of sedimentary facies, interpret depositional environments, and evaluate petrophysical properties. The Nahr Umr Formation is dominated by siliciclastic sediments with sedimentary facies interpreted as distributary channel, tidal channel, tidal flat, tide-modified mouth bar, bay, lower shoreface, offshore transition and offshore. The assemblage of sedimentary structures observed in this formation, including cross-bedding, ripple cross-lamination, lenticular bedding, and wavy bedding, indicates a tide-influenced deltaic environment. This interpretation is further supported by the bimodal grain size distribution revealed through analysis, reflecting the combined influences of fluvial and tidal processes. The Mishrif Formation is dominated by carbonate sediments deposited in a carbonate platform environment, with sedimentary facies interpreted as high-energy shoal, low-energy shoal, tidal channel, lagoon, subtidal, swamp and incised valley. Local depositional environments transitioned among open platform, semi-restricted platform, and platform margin settings in response to relative sea-level fluctuations. The petrophysical properties of the studied formations are influenced by both sedimentary and diagenetic processes. High-quality reservoirs are typically associated with sedimentary facies such as distributary channels, tidal channels, and bioclastic shoals. Diagenetic dissolution has played an important role in enhancing reservoir quality.
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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) | 0.001 | 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".