Reservoir Fluid Characterization for Tight Fractured Reservoirs: Effect of Diffusion
Bibliographic record
Abstract
Abstract Molecular diffusion effect has been ignored in many conventional reservoir studies, but it can play a significant role in tight fractured reservoirs and is crucial for an appropriate reservoir evaluation. We have characterized diffusion coefficients of methane gas in hydrocarbon fluid samples by combining experiment and simulation. We employ the theory of Fickian diffusive flux to evaluate molecular diffusion behavior. It is described as a product of concentration gradient, molar density and Fickian diffusion coefficient. Diffusion coefficients, although commonly estimated by existing correlations, shall be calibrated against actual molecular diffusion behavior for practical use. There have been few published papers showing Fickian multicomponent diffusion coefficients, because a simple and reliable measurement method has not been available. We hereby propose a method for computer-assisted multicomponent diffusion coefficient evaluation based on experiments proposed by Riazi that uses a PVT cell apparatus. We measure molecular diffusion behavior in a PVT cell and evaluate diffusion coefficients using Leahy-Dios and Firoozabadi's model with the assumption of local equilibrium described by the Peng-Robinson EOS. Volume shift parameters, binary interaction coefficients, initial pressure, initial liquid volume and diffusion coefficients are optimized to fit measurements by a new global optimization algorithm named iterative Latin hypercube samplings. Simulation case studies are performed to show the effect of molecular diffusion in tight fractured reservoirs. Results indicate that injected gas diffuses deeply into rock matrices when diffusive flux is properly represented. Consequently, better sweep efficiency is achieved compared to the cases without considering diffusion. In this paper, we present a systematic method to evaluate diffusion coefficients of reservoir fluids using EOS. It will be useful for reservoir simulation of oil and gas recovery in tight fractured reservoirs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".