Application of Gravity-Assisted Wellbore Segregation to Wireline Sampling Operations. A Low Permeability Case Study
Bibliographic record
Abstract
Abstract A common challenge in exploration in the North Slope of Alaska is the formation evaluation of low-permeability formations when near-wellbore damage is caused by water-based muds (WBM). This study describes the novel application of existing technology to collect high-quality hydrocarbon samples efficiently in these challenging conditions. The concept was tested with a wireline formation tester in a well with severe formation damage caused by WBM. The procedure and hardware used are discussed and an example of the effectiveness of the proposed technique is shown. Due to the unfavorable mobility ratio, WBM filtrates tends to move preferentially while attempting oil sampling in low permeability rock leading to long station times during wireline formation testing operations. To overcome this challenge, a target sampling interval was subjected to high drawdown using a 3D radial probe to move the target phase closer to the wellbore. Once hydrocarbon was detected in the fluid analyzer, the 3D radial probe was retracted, and the string repositioned to cover the same interval with a straddle packer assembly. Straddle packers provide wellbore annular space for filtrate and hydrocarbon to segregate after the flow period is resumed. When hydrocarbons are again seen in the fluid analyzer, a simultaneous two-pump flow is used to collect them and discard the filtrate. The combination of 3D radial probe and straddle packer assists with displacing the mud filtrate, bringing the target hydrocarbons to the wellbore, and enables the collection of segregated samples with exceptional quality. After pumping at one sampling station using the 3D Radial probe, the maximum hydrocarbon fraction observed was 5%. When the straddle packer was positioned at the same interval, the fluid analyzer showed that the low velocity in the annular space between tool's mandrel and wellbore enabled hydrocarbon segregation from the filtrate due to the existing density contrast. When the hydrocarbon in the wellbore reached the straddle packer inlet, the lower pump was used to flow most of the filtrate in the down direction at high rate. Meanwhile, the hydrocarbon was "skimmed off" and placed in sample containers at a much lower rate using the upper pump. Laboratory results confirmed that the samples collected with the traditional sampling method contained 95% filtrate whereas the samples collected with our technique contained 90% hydrocarbon. Downhole fluid segregation using single-inlet, wireline straddle packer and dual-pump action has not been found in the literature. Recent developments in wireline formation testing use dual inlets in straddle packer modules to enable downhole segregation. We consider that the technique described here innovatively uses the capabilities of current formation testers to collect high-quality hydrocarbon samples in low permeability conditions. With minor adjustments, this technique can also be applied in gas or water sampling in wells drilled with oil-based muds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".