Correcting Underestimation of Optimal Fracture Length by Modeling Proppant Conductivity Variations in Hydraulically Fractured Gas/Condensate Reservoirs
Bibliographic record
Abstract
Correcting Underestimation of Optimal Fracture Length by Modeling Proppant Conductivity Variations in Hydraulically Fractured Gas/Condensate Reservoirs A. H. Akram; A. H. Akram Schlumberger Search for other works by this author on: This Site Google Scholar A. Samad A. Samad Schlumberger Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, May 2006. Paper Number: SPE-100321-MS https://doi.org/10.2118/100321-MS Published: May 15 2006 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Akram, A. H., and A. Samad. "Correcting Underestimation of Optimal Fracture Length by Modeling Proppant Conductivity Variations in Hydraulically Fractured Gas/Condensate Reservoirs." Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, May 2006. doi: https://doi.org/10.2118/100321-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Unconventional Resources Conference / Gas Technology Symposium Search Advanced Search Abstract A study was carried out to forecast the productivity of a hydraulically fractured well in a retrograde gas-condensate sandstone reservoir using a numerical model. The fracture was explicitly modeled as a set of high-conductivity cells.At the gas velocities normally encountered in hydraulic fracture proppant packs, non-Darcy pressure drops dominate, and the apparent proppant permeability is one or two orders of magnitude lower than the Darcy permeability measured at single phase low-rate conditions. This is particularly true if a liquid phase is also flowing. The apparent permeability of the proppant is a function of: Gas velocity (hence: rate and flowing pressure)Ratio of free liquid rate to gas rateStress on the proppantType of proppantThus, apparent proppant permeability will vary with distance from the wellbore, increasing towards the tip of the fracture where liquid ratio and velocity are lower.This variation of permeability was explicitly modeled in the proppant pack by dividing it into segments and calculating the permeability in each segment. As a result of this modeling, the impact of increased fracture length on productivity was found to be more significant than in simpler modeling where one permeability value is used for the entire proppant pack.The variation of apparent proppant permeability along the length of the fracture and its impact on well productivity are discussed in this paper. A comparison of predicted well productivity is also made with the use of a constant permeability value for the proppant in numerical and analytic simulators. We will show that using a constant proppant permeability value results in an estimate of optimal fracture length that is too short. Keywords: hydraulic fracturing, Fluid Dynamics, variation, apparent proppant permeability, fracturing materials, fracturing fluid, fracture length, flow in porous media, Upstream Oil & Gas, permeability value Subjects: Hydraulic Fracturing, Well & Reservoir Surveillance and Monitoring, Reservoir Fluid Dynamics, Formation Evaluation & Management, Unconventional and Complex Reservoirs, Fracturing materials (fluids, proppant), Production logging, Flow in porous media, Drillstem/well testing, Gas-condensate reservoirs Copyright 2006, 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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".