Managing clastic reservoir heterogeneity II: Geological modelling and reservoir characterisation of the Minjur Sandstone at the Khashm al Khalta type locality (Central Saudi Arabia)
Bibliographic record
Abstract
ABSTRACT The increasing demand on geological reservoirs, whether for developing geothermal energy or for CO2 geological storage, raises questions on how reservoir heterogeneity might increase or decrease reservoir performance. To address this issue we selected the Minjur Sandstone Formation, a groundwater-bearing formation of Triassic age in Central Saudi Arabia, for complex reservoir modelling, simulation and prediction of the spatial distribution of sand bodies in a fluvio-deltaic system. This paper builds on a previous study that focused on the facies, stratigraphy, and reservoir characterisation of the Minjur Sandstone at the Khashm al Khalta type locality. Its purpose is to construct a deterministic 3-D model for (1) studying the connectivity of the Minjur Sandstone, and (2) illustrating a typical fluvio-deltaic reservoir and its associated heterogeneity. A first model simulates the spatial distribution of the depositional environments, which were further coded into relative proportions of sand, shale, evaporites and carbonates. This leads to a second model that contributes to reservoir applications through estimating the reservoir’s volume and storage capacities. Sequences 1 to 4 of the succession (Upper Jilh Formation–Lower Minjur Member), with a net-to-gross sand/shale ratio (NG) of ca. 8%, consist of poorly connected sandstone reservoir bodies. In contrast, sequences 5 to 9 (Upper Minjur Member), with an average NG of ca. 42%, consist of well-interconnected sandstone reservoir bodies. The NG depends on the tectonic influence and on relative sea-level variations. The best Minjur Sandstone reservoir bodies are at the base of each sequence, where limited available space favours a stack of deposits: interconnected fluvial channels which form wide spreads of coarse sandstone showing little diagenesis. The greatest potential is in the Upper Minjur Member. The effective reservoir volume was isolated using a sand content of > 85%. Rock volume and pore volume for an average porosity of 17% were subsequently calculated from the outcrop model. A representative block of 600 m x 600 m x 144 m was selected in order to simulate a fraction of the reservoir with the same properties as the whole. The block’s CO2 storage capacity was 57,000 tonne (in the International System, ‘SI’) for an arbitrary CO2 density of 0.7 (supercritical). This result was then transposed to the aquifer in the Riyadh area where similar conditions are assumed to exist. To obtain a ‘reservoir scale’ estimation, the block dimensions were upscaled to 20 km x 20 km x 80 m (the last figure being the effective thickness given by hydrogeological studies). The inferred storage capacity here was 30.5 Mt (million tonnes, International unit System, ‘SI’), which is an excellent figure when one considers the large-scale projects of Europe (Sleipner: 20 Mt) and Canada (Weyburn: 14 Mt).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".