Effect of Natural Fracture Properties on Production Variability of Individual Wells in Multiphase Oil Reservoirs
Bibliographic record
Abstract
Abstract Fracture properties that are responsible for production variability of individual wells in oil reservoirs are investigated with the use of multi-phase dual porosity models. Simulation work and sensitivity analyses are performed using Top-Down reservoir modeling, i.e., starting with a simple case that increases in complexity based on the understanding of basic reservoir properties. Fractional production variability plots (FPVP) are used to measure the degree of heterogeneity of the naturally fractured reservoir considered in this study. Straight lines or smooth curves close to 45 degrees in the FPVP indicate homogeneous reservoirs which can be single porosity or dual porosity with uniform fracture distribution. An increase in curvature in the FPVP indicates higher heterogeneity, particularly in the fracture system. Fracture characteristics such as fracture distribution, aperture, geometry and orientation are part of the sensitivity analyses as well as fluid characteristics and fluid-rock interaction. Fracture density and length are found to have the largest impact. Results of this work are corroborated when compared with real data from a naturally fractured multi phase oil reservoir in Mexico. A variability distribution model (VMD) is used to match the simulated production history of the field on a FPVP plot. The FPVP curvature is found to be proportional to the simulated normalized standard deviation (NSD) of the cumulative well production. As a result two equations are developed that correlate both fracture density and length with the NSD, respectively. Scatter plots are used to identify the effect of each parameter on the NSD. It is concluded that the variable distribution model provides a realistic assessment of heterogeneity in naturally fractured multi phase oil reservoirs and helps with characterization of the reservoir.
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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 | 0.000 | 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".