Utilizing CO2-Resistant Self-Healing Cement System for Gas Wells: A Case History from East Malaysia
Bibliographic record
Abstract
Abstract Three offshore development gas wells were drilled in East Malaysia. The reservoir zones were identified to potentially contain high levels of carbon dioxide (CO2), as well as given the potential to be utilize as CCS wells. CO2 is corrosive and may cause degradation of the cement sheath, compromising well integrity. This paper presents the decision process for selecting a suitable cement system, as well as the execution, evaluation, and improvement results applied to the three wells. The operator was committed to ensuring safer operations over the lifespan of the well by maintaining long-term well integrity. Degradation of conventional Portland cement due to the corrosive effects of CO2 can result in compromised cement sheaths, leading to reduced gas production rates and serious environmental hazards. Implementing the appropriate technology requires well-structured planning. To achieve consistent blending of the innovative self-healing CO2-resistant cement, a series of quality control procedures were established, supported by thorough laboratory testing and comprehensive management of the blend lifecycle. Due to its superior mechanical properties and self-healing capabilities, the operator opted to use a novel self-healing CO2-resistant (SHCR) cement system for the gas field development. Blend homogeneity was maintained throughout the transfer process from the bulk plant to the sea vessel and then to the offshore rig, with minimal changes in specific gravity and blend composition between samples from the bulk plant and those collected at the rig. Several key logistical challenges were addressed to ensure successful delivery, including the complexities of offshore operations, job frequency, and handling requirements, as well as the setting of custom equipment specific to this rig. These challenges were effectively managed through quality control and improvement processes developed during the campaign. This paper shares lessons learned from the execution and evaluation methods deployed. Ultimately, cementing operations were successfully completed without incidents using conventional equipment. Moderate to good cement bonding was achieved across the target zones, enabling the rig to proceed with perforation, well testing, and production operations. Because of its superior mechanical properties and self-repair capabilities when exposed to CO2-containing fluids, this innovative system is highly effective in maintaining zonal isolation and ensuring long-term well integrity. Additionally, this technology is applicable in any field globally where CO2 regulation is enforced and safety risks exist for the operators or the public. It also enables operators to adopt proven processes for successful operations, leveraging insights gained from meticulous planning, implementation, and lessons learned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".