An Expert Software System has Accomplished Full Implementation of Well Integrity Management System WIMS Resulting to Excellent Safety and Production Sustainability for Critical Sour HPHT Field in Offshore Madura Strait, Indonesia
Bibliographic record
Abstract
Abstract Four high-pressure-high temperature (HPHT) and sour gas wells are currently operating at Madura offshore as the only productive assets for Husky-CNOOC Madura Limited (HCML). Each well performance is very crucial to fulfill the demand of the gas customers in East Java, Indonesia. Since starting production in 2017, the wells experienced two main well integrity challenges, high annulus pressure and wellhead growth. Both challenges are very dependent to the well flow rate and the flow duration. A continuous operation monitoring is highly required in order to keep the wells operating safely. To overcome the challenges, HCML established a Well Integrity Management System (WIMS) document that approached several international standards as its basis. As company grows, development plan challenged the WIMS to perform faster and more efficient as compared to the existing manual system. From there, the journey of WIMS digitalization began. The journey started with the alignment of the existing WIMS document to the ISO-16530-1 at Operational Phase with more stringent boundary to operate the wells safely. The alignment covers, but not limited to the organizational structure, well barriers and criteria, monitoring and surveillance, annulus pressure management, and maintenance. The document also covered risk assessment and management of well integrity failure, which was the backbone of the WIMS digitalization. The current digital solutions allow production data to be accessed and retrieved directly from the system for analysis purposes. It compares the recorded data with pre-determined rules and parameters set in the system. It triggers a notification to the responsible personnel to perform the required action should any anomaly occurs. It also can send a reminder to users to schedule and complete a well Integrity test to ensure that a well is always in compliance with the WIMS. All test reports and documentation are stored in the system as preparation for any future audit. A key requirement of the expert software system was access to future developments that can offer enhanced functionality of the well integrity platform through additional near time capabilities such as predictive erosion and corrosion for downhole flow wetted components. This is being developed to enhance workover scheduling for existing wells and material selection for new wells and is planned to update automatically critical well integrity criteria such as tubing burst, collapse and MAASP.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".