Using Near Wellbore Upscaling to Improve Reservoir Characterization and Simulation in Highly Heterogeneous Carbonate Reservoirs
Bibliographic record
Abstract
Abstract Carbonate reservoirs host a major portion of the world's remaining conventional and unconventional hydrocarbon reserves, typically containing multi-scale geological heterogeneities varying over many orders of magnitude in size. Characterizing and representing them robustly in reservoir models is a prime challenge in carbonate reservoir simulation. One of the key aims of this paper is, hence, to present a novel near wellbore upscaling (NWU) workflow that addresses the challenges associated with conventional carbonate modelling workflows. The NWU workflow provides a systematic geostatistical approach to obtain more realistic representation of multi-scale geological-petrophysical heterogeneities in complex carbonate reservoir simulation models. Using well log and core data, near wellbore regions were recreated to represent the core scale heterogeneities via high resolution geostatistical models. These core/centimeter scale permeability models were then upscaled into wireline/decametre scale using flow-based upscaling. The results, coupled with wireline data were used to generate global porosity-permeability and vertical-horizontal permeability relationships for reservoir simulation. Importantly, the workflow mitigates sample bias, which is frequently observed in the core data for carbonate reservoirs. We have applied our approach to a mature carbonate field, to model and upscale crucial multi-scale heterogeneities ubiquitous in the reservoir. These heterogeneities, such as mechanically weak zones of enhanced micro- and macro-porosity, leached stylolites and associated tension gashes, were caused by diagenetic corrosion. Core plugs representivity is always an issue in carbonates and these highly corroded features were very difficult, if not impossible, to sample due to their fragility. As a result, the field suffers from inherent sample biasing and insufficiency of Routine Core Analysis (RCA) data, consequently underestimating the permeability in the simulation model. The workflow presented here has enabled the authors to re-evaluate the reservoir permeability model by accounting for as yet under-sampled geological heterogeneities. The paper represents a focused individual study addressing this specific issue and doesn't necessarily reflect the operator's full understanding of this multifaceted field. Our new permeability model has addressed the need for artificial permeability multipliers and provided insight on the potential causes of the original mismatch. As a result, a new alternative model scenario has been built to help guide the on-going development plans and forecasting incremental oil recovery.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".