An Innovative Procedure for an Integrated Reservoir Simulation Study for a Reservoir Below Its Oil Bubble Point Pressure
Bibliographic record
Abstract
Abstract An innovative procedure was developed for an integrated reservoir simulation study for an oil field that is depleted below its oil bubble point pressure. The approach used was to complete a black-oil history match model, then convert the black-oil model to a compositional model to predict miscible or solution gas re-injection performance. An important element of the conversion to a compositional model is to preserve the history match results from the black oil model. This approach is unique in that equation of state (EOS) characterization and special core analysis results were used to determine the range of gas-oil ratios for a blackoil model history matching a reservoir pressure below its oil bubble point. Using the black-oil model for history matching helped reduce the CPU time requirement, thus resulting in quicker turnarounds. The converted compositional model was then used to develop future depletion strategies beyond primary production, such as solution gas re-injection or other EOR schemes. This paper describes the black-oil model setup, history match results and compositional model results for the base case and cycling produced gas schemes. The difference between the black oil and the compositional models is small for these two schemes because the produced gas-oil ratio is still within the validated GOR range. However the black oil model could not be used in the schemes for different injection composition streams, as the black-oil model can not utilize input compositions. Introduction Theoretically, a compositional model should be used to model a reservoir being depleted below its oil bubble point pressure, because the reservoir fluid undergoes compositional changes. However, a compositional model takes a lot of CPU time and a longer time to run, hence a black oil model is used hoping that the results are the same as the compositional model. From our previous experience(1), it is difficult to conduct a full field fully compositional simulation, hence a pseudocomponent model will be used. Again, there might be some deviation from the full field model. The purpose of this study is to develop an innovative procedure for (1) checking that the black oil model has the same results as the compositional model for a reservoir that is depleted below its oil bubble point pressure, and (2) confirming the pseudocomponent model gives a similar result to the fully compositional model. In this study, an equation of state (EOS) was used to characterize the reservoir fluid into a fully compositional model (FC) and then grouped into a 4 to 6 component pseudocomponent model (PC) to be used in the compositional reservoir simulator. In the meantime, a black oil PVT model was generated by the tuned EOS (fully compositional PVT model). To validate the EOS characterization, three reservoir simulation models were constructed using a core displacement data, black oil and two compositional models (FC and PC). After matching this special core analysis and available data such as recovery and injection pressure, the range of gas-oil ratios was determined for a black oil model that could be used in history matching a reservoir with pressure below its oil bubble point pressure.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".