Interpretation of formation permeability and pressure responses from wireline formation testing with consideration of interlayers
Bibliographic record
Abstract
We have developed a workflow to interpret formation permeability in a hydrocarbon reservoir with consideration of interlayers by numerically simulating the measured pump-out flow and pressure responses from wireline formation testing (WFT). With the field data obtained from a dual packer tool in the deepwater Gulf of Mexico, we have developed and validated a high-resolution numerical model to simulate the fluid-sampling process together with transient pressure. History matching has been performed with field data to assess the effective thickness and then interpret the permeability for each flow unit. In addition to generating eight cases under various configurations of laminated layers, we use pressure buildup derivatives obtained from packers and observation probes as a diagnosis tool to examine the effect of the interlayer on WFT measurements. Oil-based mud-filtrate invasion affects the early-time behavior of pressure transients because of the associated changes in fluid viscosity and compositions. Low vertical permeability can behave as a vertical barrier for the flow in a WFT tool, indicating the difference contrast in permeability between individual flow units. As for the field case, effective water horizontal permeabilities for tests 1 and 2 are 14.0 and 10.6 mD, respectively. Low vertical permeability results in a distortion in the derivatives, particularly during the transition between flow regimes. In a laminated reservoir, a radial flow regime will develop when the radial length of interlayer is greater than the vertical formation interval and when the complete circular shape of interlayer is formed. It is recommended that any observation probe be positioned in or below the interlayer to accurately define the vertical communication of interlayers and its configuration. If dual packers and observation probes are located in the same zone, their pressure responses exhibit the same flow regimes; otherwise, different pressure responses can be developed in the observation probes when a partially sealing interlayer exists.
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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 | 0.001 |
| 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".