Gas-Condensate Well Performance Using Compositional Simulator: A Case Study
Bibliographic record
Abstract
Gas-Condensate Well Performance Using Compositional Simulator: A Case Study Mourad Bengherbia; Mourad Bengherbia Sonatrach, Inc. Search for other works by this author on: This Site Google Scholar Djebbar Tiab Djebbar Tiab U. of Oklahoma Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, April 2002. Paper Number: SPE-75531-MS https://doi.org/10.2118/75531-MS Published: April 30 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Bengherbia, Mourad, and Djebbar Tiab. "Gas-Condensate Well Performance Using Compositional Simulator: A Case Study." Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, April 2002. doi: https://doi.org/10.2118/75531-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Unconventional Resources Conference / Gas Technology Symposium Search Advanced Search AbstractThe TFT gas-cap field in Algeria produces a lean gas condensate from an Ordovician reservoir. The dewpoint pressure (2619 psi) is approximately the initial reservoir pressure (2683 psi). The pressure reaches the dewpoint within a short production time, and then drops below the dewpoint. This pressure drops causes a loss of productivity, and high condensate saturation builds up near the wellbore could lower gas productivity.To simulate the depletion process, the Peng-Robinson EOS was developed to characterize the fluid from a well data. A tuned model matched the experimental PVT data like Constant Composition Expansion (CCE), and Constant Volume Depletion (CVD) that are reliable and give a high degree of match results. This is performed by pseudoizing the EOS components from 11 to 7. A splitting method is discussed, and refers to the procedures of dividing the heptanes-plus fraction into hydrocarbon groups with a single carbon number, which are described by the same physical properties used for components.A compositional simulation is performed to study the behavior of gas condensate fluid, and to show the phenomenon of condensation of liquid in the reservoir when the pressure drops below the dewpoint pressure. Because the compositional computing time increases significantly with the number of component used, the pseudoized fluid components discussed before are used to reduce the computing time.An agreement between the tuned Peng-Robinson EOS and experimental PVT data leads to a good depletion process for use in production simulation. The simulation model yields high-quality match with the test data, such as the average pressure, gas production, and liquid (condensation), which leads to good performance predictions.A good understanding of the gas condensate reservoir, when operating above and below the dew-point, and how this reservoir is affected by three zones, the inner, the middle, and the outer, where the pressure drops below the dew-pressure, and the variation of liquid and vapor phases for each component in an other hand.IntroductionGas Condensate wells behavior is unique in a sense, it is characterized by a rapid loss of well productivity.It is well known that when the flowing bottomhole pressure drops below the dewpoint, a region of high condensate saturation builds-up in the wellbore causing lower gas deliverability, due to the reduction in gas permeability.Accurate laboratory studies of PVT and phase equilibrium behavior of reservoir fluids are necessary for characterizing these fluids and evaluating their volumetric performance at various pressure levels.Due to the amount of data desired, the Constant Volume Depletion (CVD), and the Constant Composition Expansion (CCE) tests were performed in our case.Gas condensate wells are simulated frequently with black-oil models. This study presents a gas condensate well simulated with compositional model, and a pseudoization procedure that reduces the multicomponent condensate fluid to a pseudo-component is used. This pseudoization allows the use of a simpler, less expensive compositional simulator. The model used gives a good match for the gas produced, and a good match for the liquid produced (condensate fluid) from this gas. The compositional calculations reported here use the Peng-Robinson equation of state. Keywords: reservoir simulation, fraction, fluid modeling, liquid, equation of state, mole fract, regression, bengherbia, dewpoint pressure, modeling & simulation Subjects: Fluid Characterization, Reservoir Simulation, Unconventional and Complex Reservoirs, Phase behavior and PVT measurements, Fluid modeling, equations of state, Gas-condensate reservoirs This content is only available via PDF. 2002. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".