Rock Type Constrained 3D Reservoir Characterization and Modeling
Bibliographic record
Abstract
Abstract This paper presents the results of a reservoir characterization and modelling study based on reservoir rock typing (RRT) of Lower Cretaceous carbonate reservoirs in one of Abu Dhabi Onshore oil fields. The final goal is to obtain multiple realizations of 3D descriptions of the petrophysical properties, namely porosity and permeability, which match and are consistent with the underlying RRT scheme, at the grid block level. The RRT were described in all sections of the reservoir for all cored-wells. The established reservoir rock types were based on depositional facies sequences, diagenetic overprints and petrophysical properties, including pore throat size distribution, porosity and permeability. The model reveals that the vertical changes in the rock types are a function of depositional facies, while the lateral variation down structure across the same lithofacies unit are controlled mainly by diagenesis. Considering the limited number of cored wells compared to the total number of loged-wells, the characterization started by predicting both permeability and rock type at the non-cored wells. Permeability was predicted using a combination of regression analysis and geostatistics. The use of geostatistics not only has been usefull in capturing the high variability of permeability but also has ensured that the core data is fully honored at plug locations. Rock type was estimated at the non-cored well using discriminant analysis. Consistency checks were been applied to the results of both prediction to ensure consistency between properties and rock types. Non-consistent results were assigned as unestimated-points. A geological conceptual model, in the form of iso-rock type maps, was used to QC the results of the prediction at the non-cored well and as a tool in deriving soft information about the spatial relationship of the different rock types. The 3D descriptions of the properties were generated using a geostatistical technique. The technique not only honors the conditioning data and spatial relationship of each property, but also honors the local relationship between each property and the rock type. Additionally, some constraints derived from the diagenetic model were implemented in the modeling process to ensure that the model follows the geological conceptual model as much as possible. In transforming the well-log data into the model grid, appropriate scale-up methods and grid-block thickness were selected to ensure that reservoir heterogeneities are maintained. Multiple realizations of the properties were generated in order to capture and to quantify the intrinsic variability of the model. The results of this characterization study will be used for flow performance evaluation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".