Application of J-Functions to Prepare a Consistent Tight Gas Reservoir Simulation Model: Bossier Field
Bibliographic record
Abstract
Abstract For a tight gas field with a pressure depletion drive mechanism like Bossier Field, it is critical to promptly develop an accurate reservoir simulation model that will help the reservoir engineer understand reservoir behavior, estimate well reserves and evaluate infill and acceleration opportunities. This paper describes a technique that calculates accurate fluid distributions honoring the physics of the rock such as capillary forces, rock texture, permeability and pore volume distributions. Saturations that make physical sense enable the reservoir simulation model to more accurately estimate reserve and production forecasts that yield a higher level of confidence in the economic evaluations and optimum well planning. The technique uses laboratory capillary pressure curves coupled with permeability-porosity measurements to generate J-Functions. A J-Function is an average data method proposed by Leverett for correlating capillary pressure data. (Leverett, 1941). The average correlation is based on the fact that geometrically similar rocks have capillary pressure with similar behaviors. The J-Function of geometrically similar rocks will overlay for samples that may differ in permeability by an order of magnitude. The Leverett J-Function is a powerful tool to average data from several samples and to look for outliers of different rock types. Once these J-Functions are identified, they are input into the reservoir simulation model for flow simulation and history matching. Using this technique the transition from model initialization to flow simulation proved very smooth. The historical production was easily matched increasing the confidence in the outlook. Some of the wells were not in boundary dominated flow; the initial forecasts were underestimated because some of the wells were not in boundary dominated. This technique indicated these reservoir volumes would be produced through existing wells. New infill and acceleration opportunities were developed. Down spacing was determined to be uneconomic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".