Utilizing Ultrasonic Imaging Technology to Evaluate a Lateral Entry Module
Bibliographic record
Abstract
Abstract As a result of improved drilling and completion techniques, an increasing number of wells worldwide utilize multilateral systems to minimize the number of surface penetrations required to maximize reservoir contact. However, these systems increase the complexity, which in turn introduces new failure modes and challenges related to inspection of erroneous completions. The scanning range and measurement capabilities utilizing ultrasound imaging techniques provide a new solution for well diagnosis of multi-lateral completions. Several attempts to enter the upper lateral of a multi-lateral well operated by a major oil company in Alaska, USA had been unsuccessful. Different technologies were attempted to diagnose the problem but no conclusive results were obtained. In May 2017, an ultrasonic imaging technique based on medical ultrasound imaging was used to inspect the Lateral Entry Modules (LEMs). This paper presents the data collected by an ultrasound downhole scanner demonstrating a novel method for diagnosing multi-lateral wells. The ultrasound downhole scanner utilizes established technology applied in medical ultrasound imaging (e.g. Angelsen 2000) to obtain images and measurements of downhole completion components. A 288 element, 3.3MHz circumferential ultrasound transducer array combined with electronic beamforming allows the flexibility to optimize image quality for different tubing sizes with no moving parts. The transducer operates in pulse-echo mode. Logging is performed dynamically with images obtained real-time. In 2011, the scanner was used to measure damages in sand screens (Hyde-Barber et al.) and has since 2009 been used to image and measure downhole completion components worldwide. The possibly defective LEM was investigated by the scanner. A reference scan of a fully functional LEM in the same well was also made and the results from the two compared. The ultrasound data, visualized both as 2D grey-scale images and 3D-rendered images, clearly show that the upper LEM assembly was not properly aligned with the window of the lateral. Thus, explaining the past unsuccessful attempts to enter the completion. Measurements were made directly on the ultrasound images to document the findings. The results from the survey helped the customer to understand the situation of their well and gave information which was valuable for the decision making process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".