Geomodeling of Giant Carbonate Oilfields with a New Multipoint Statistics Workflow
Bibliographic record
Abstract
Abstract Geomodeling is the integrating activity when developing a reservoir description. The static and dynamic models derived during geomodeling are vital in improving recovery, understanding the various uncertainties associated with it, and, ultimately, maximizing the profitability of any oilfield. A case study of a giant heterogeneous carbonate brownfield is presented, in which the conceptual geological model built from detailed core analysis is used to drive the facies population during the geomodeling stage. Reservoir heterogeneity mapping is captured at different scales, from rock-types identified on core and advanced log analysis to joint stochastic inversion of reservoir properties derived from seismic prestack data. A novel geomodeling workflow is presented to merge and optimize this set of multiscale data within a geological conceptual model using several geostatistical facies modeling schemes. Two of these are based on new technologies, namely truncated Gaussian simulation with 3D trend and multipoint geostatistics, which was developed to model complex geometries. The multipoint technique allows for more flexible integration of soft and hard data compared to traditional pixel- or object-based modeling approach. The paper compares the two approaches and shows their respective advantages. It is the first time that the two new algorithms have been implemented in a giant carbonate oilfield. The outcome of the study shows that the new multipoint geostatistics facies simulation implementation performed a smooth integration of all available data. This included log data from several hundreds of wells, high-resolution seismic properties, and the conceptual geological model.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".