Construction of a Carbonate Reservoir Model Using Pressure Transient Data (Field Case Study)
Bibliographic record
Abstract
Abstract A geological model is usually constructed using geophysical and petrophysical data by virtue of geological information. In this work, we built a carbonate reservoir model (Iranian offshore field) not only by seismic and well log data but also by the integration of 99 well test data available for this field. Many features such as sealing faults, aquifer, fracturing and layering systems were observed during the well test interpretation. Some of the above features were identified only by pressure transient data such as one sealing fault in which it was not observed in the seismic data. The existence of this fault is later confirmed by geological information acquired during drilling of a horizontal well in the field. In addition to the identification of structural features, permeability data from well testing were also used for the construction of iso-permeability map instead of depending on permeability data from core analysis. This is very significant assignment in a heterogeneous carbonate reservoir and when a single porosity model is used. Permeability obtained from well testing is the effective permeability in the drainage area of each well while permeability from core analysis does not represent the property of a heterogeneous reservoir. Numerical simulation of the fluid flow in the field then validated the permeability values from well testing. Introduction Reaching to higher recovery factor during the production of a reservoir requires a relatively accurate reservoir description. Reservoir description has a significant effect on the design, operation and economic success of optimum depletion or application of any EOR method. Using pressure transient test for describing reservoir heterogeneity has been the subject of several authors. Lefkovites et al.1studied the behavior of bounded reservoirs composed of stratified layers communicating only through the well bore. Russel and Prates 2 studied the practical aspects of interflow cross flow. Kazemi and Seth 3 studied the effect of anisotropy and stratification on pressure transient analysis of wells with restricted flow entry. Bixel et al. 4obtained solutions for the pressure behavior of a well located near a linear discontinuity where the reservoir properties are uniform on either side. Heterogeneities may be small scale as in carbonate reservoirs where the rock has fractures or may be large scale such as faults, fluid contacts, thickness changes, lithology changes and multi layers with different properties in each layer. Warren and Root 5, and Kazemi 6 studied the transient testing in naturally fractured reservoirs. Integrated well testing and geological efforts fo building a static model were presented by some authors. Ayestaran and Nurmi 7 presented a reservoir description of a heterogeneous reservoir using well testing. They confirmed the existence of reservoir boundaries, faults, and low permeability region in a reservoir. Massonnat and Bandizoil 8 integrated the geological and well test data in order to construct a better modeling of a heterogeneous reservoir. Osman 9studied the effects of geometry and the type of reservoir boundaries on drawdown testing. Kabir 10used the integration of well test and geological data in construction of a geological model for the Greater Burgan field of Kuwait.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".