An Integrated Approach to Well Leak Diagnostics: Case Study of the Successful Application of the Latest Leak Detection Technology and Interpretation Offshore Timor Sea, South East Asia
Bibliographic record
Abstract
The identification of low-rate leaks along with low annular-pressure buildup rates in any type of completion presents challenges in the well-integrity domain. This paper emphasizes the importance of understanding the well-diagnostic problem to determine feasibility, isolate interest zones, enhance stimulation strategies, and ultimately optimize the acquisition of high-resolution acoustical data from the wellbore with a latest-generation advanced leak-detection tool. This case study discusses the methodology that underlies the successful determination of the depths and the radial locations in the outer casing strings of multiple leaks in an offshore well. In the study presented, emphasis had been placed on the job planning to provide adequate or substantial leak stimulation for the accurate determination of the leak points in terms of radial distance away from the tool axis within the wellbore. Rather than a shut-in and flowing or venting acquisition, it was proposed that the optimal method for the successful determination of an outer casing string leak involved invoking a range of flow rates and, therefore, acoustic levels, across an extended period. The study also demonstrates the advantages of integrating acoustic-based tools with conventional production logging tools. Two outer string casing leaks with annulus to formation communication areas were identified from high-resolution leak-detection logging coupled with conventional pressure and temperature measurements. The interpretation process included the computation of a 2D radial map of the flow activity across each zone of interest. This process resulted in less ambiguity and clearer results obtained in real time during the acquisition. The location of each leak point was triangulated using an error-minimization algorithm from the received acoustic waveforms at the tool receiver array. Further, the optimized stimulation strategy enabled leak-stimulation responses to be tracked in the computed power spectral density (PSD) at each leak. This process enabled the operator to promptly move on with the well abandonment strategy without waiting for further data analysis. Attention to detail from the outset and a complete understanding of the well and its annular pressure and fluid behavior enabled an optimized and focused electric line diagnostic strategy to be used. The use of high-resolution acoustic data from an advanced leak-detection tool with an array of hydrophones ensured that the multiple leak locations were identified and characterized.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".