Effect of Lamination on Interpreting Formation Permeability and Pressure Responses from Wirelilne Formation Testing
Bibliographic record
Abstract
Abstract In this paper, techniques have been developed to examine the effect of lamination on interpreting formation permeability in a hydrocarbon reservoir by numerically simulating the measured pumpout flow and pressure responses from wireline formation testing (WFT) measurements. With the field data obtained from a dual packer tool in the Deepwater Gulf of Mexico, a high-resolution near-wellbore numerical model has been developed and validated 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. Subsequently, eight cases have been generated under various configurations of the laminated layers. The pressure buildup derivatives obtained from both packers and observation probes are used as a diagnosis tool to examine the effect of lamination on WFT measurements. Mud-filtrate invasion affects the early-time behaviour of pressure transients because of the associated changes in fluid viscosity and compositions. It is found that low vertical permeability can behave as a vertical barrier for the flow in a WFT tool, indicating difference contrast in permeability between individual flow units. As for the field case, effective water horizontal permeabilities for Tests #1 and #2 are found to be 14.0 mD and 10.6 mD, respectively. A 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 both the radial length of lamination is greater than the vertical formation interval and complete circular shape of lamination is formed. It is recommended that any observation probe be positioned in or below the lamination layer to accurately define the vertical communication of lamination as well as its configuration. If the dual packers and observation probes are located in the same zone, their pressure responses exhibit the same flow regimes. If the dual packer and observation probes are located in different flow units, pressure changes in the observation probes can be developed when a partially sealing lamination exists.
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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 | 0.002 |
| 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) | 0.001 | 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".