Permeability, Pore Pressure, and Leakoff-Type Distributions in Rocky Mountain Basins
Bibliographic record
Abstract
Permeability, Pore Pressure, and Leakoff-Type Distributions in Rocky Mountain Basins David P. Craig; David P. Craig Halliburton Search for other works by this author on: This Site Google Scholar Mike J. Eberhard; Mike J. Eberhard Halliburton Search for other works by this author on: This Site Google Scholar Chad E. Odegard; Chad E. Odegard Halliburton Search for other works by this author on: This Site Google Scholar Muthukumarappan Ramurthy; Muthukumarappan Ramurthy Halliburton Search for other works by this author on: This Site Google Scholar Rebekah Mullen Rebekah Mullen Colorado School of Mines 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-75717-MS https://doi.org/10.2118/75717-MS Published: April 30 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Craig, David P., Eberhard, Mike J., Odegard, Chad E., Ramurthy, Muthukumarappan, and Rebekah Mullen. "Permeability, Pore Pressure, and Leakoff-Type Distributions in Rocky Mountain Basins." Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, April 2002. doi: https://doi.org/10.2118/75717-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 permeability, pore pressure, and leakoff-type interpreted from more than 1,200 diagnostic fracture-injection/falloff tests were collected in a database and statistically evaluated for four Rocky Mountain basins. The statistical analysis includes the range of observed permeability and pore pressure and the fracture leakoff-type distribution.Specially designed "mini-frac" or diagnostic fracture-injection/falloff tests were routinely implemented throughout Rocky Mountain basins beginning in late 1998 for the sole purpose of estimating reservoir-engineering parameters. Using three recently developed analysis methodologies, more than 1,200 tests have been analyzed to determine permeability, pore pressure, and leakoff type.The analysis reveals that pressure-dependent leakoff, fracture-tip extension during shut-in, and fracture height-recession during shut-in are the most common leakoff types. Overall, pressure-dependent leakoff, which can be indicative of highly productive fractured reservoirs, is the most common leakoff type in all Rocky Mountain basins. The analysis also shows order-of-magnitude variation in gas permeability within all basins with observed gas permeability ranging from less than 0.001 md to greater than 0.10 md.IntroductionEstimating pore pressure and permeability in multilayered low-permeability gas reservoirs can be time consuming and, in a few cases, cost prohibitive. Because of the incremental costs and time required to implement a testing program, very few conventional well tests are completed in multilayered low-permeability gas reservoirs, even though optimizing completions requires knowledge of permeability and pore pressure.1As an alternative to conventional well testing, Craig and Brown2 suggested that conventional breakdown treatments in multilayered formations could be used to estimate permeability and pore pressure. Their procedure required isolating each reservoir, performing a small-volume injection, and recording the pressure decline during a shut-in period.2 In low-permeability reservoirs, a small-volume, low-rate injection will propagate a hydraulic fracture, and during the shut-in period, the pressure decline can be analyzed to estimate pore pressure and permeability. Craig and Brown2 advocated conventional leakoff analysis for estimating gas permeability, but before-closure pressure-transient analysis3 and after-closure analysis4 provide more realistic estimates of gas permeability.Craig, Eberhard, and Barree5 recently described the use of G-function derivative analysis and modified Mayerhofer permeability analysis for estimating pore pressure and permeability from the before-closure pressure decline following a fracture-injection test. The authors concluded that, when used in conjunction, the two techniques provide "reasonable" pore pressure and permeability estimates that are consistent with well performance based on reservoir simulation.5 Reservoir simulation in other multilayered low-permeability gas reservoirs also confirms that reasonable estimates of pore pressure and permeability are often obtained from before-closure analysis.6,7 Keywords: leakoff-type distribution, upstream oil & gas, reservoir, flow in porous media, fracture-injection falloff test, fracture closure, closure, gas permeability rad, gas permeability gdk, hydraulic fracturing Subjects: Hydraulic Fracturing, Reservoir Fluid Dynamics, Formation Evaluation & Management, Flow in porous media, Drillstem/well testing 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 | 0.000 | 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".