A Systematic Approach to Mitigate Drilling Hazards and Improve Horizontal Well Drilling Efficiency in a Conglomerate Reservoir
Bibliographic record
Abstract
Abstract Drilling long horizontal development wells in the conglomerate reservoir of the Junggar Basin, onshore China, has posed significant challenges. Operators have faced various drilling issues including stuck pipe, mud losses, and pack-off events, which impeded development efforts and necessitate the formulation of effective drilling strategies aimed at rapid and safe operations with minimal non-productive time. Understanding the subsurface characteristics of the formation is essential for developing appropriate engineering solutions. To optimize the drilling process, a systematic approach was established by integrating multiple technologies to reduce severe wellbore instability caused by abnormal formation pressures, wellbore collapse, and other complex drilling challenges. This comprehensive workflow consists of three stages: pre-drill modeling and assessment, real-time monitoring, and post-drill validation. The pre-drill geomechanical analysis provides insights into subsurface characteristics of the formation including in situ stress and rock mechanical properties. This information is utilized to optimize mud weights, mud designs, and casing setting depths so as to maintain wellbore stability during drilling. Real-time operations focus on monitoring drilling parameters, cavings, and logging data, offering updated recommendations to field drilling engineers to mitigate wellbore instability. In the post-drill phase, the refined geomechanical model will be used for the optimization of drilling designs for subsequent wells in the area. This paper presents a typical case characterized by a high risk of instability due to several shale intervals in the build-up section and the existence of nature fractures in the horizontal interval which had been known to cause significant wellbore instability. Geomechanical analysis reveals a narrow safe mud weight window in the 6½″ hole section as the collapse pressure in the shaly interval approaches the leakage pressure in conglomerate layers. To enable cost-effective horizontal drilling, a systematic workflow was implemented in the Mahu block, resulting in a 30% improvement in rate of penetration (ROP) and a 32% reduction in wellbore instability related drilling issues compared to offset wells in the same field that were drilled without a risk mitigation strategy. The systematic approach not only effectively reduced drilling hazards related to wellbore instability but also significantly increased ROP. With the attainment of major flat time reduction and lesser rock failures, borehole quality had been improved. However, the effective application of this systematic method requires ongoing learning and refinement. Continuous improvement necessitates regular updates to the geomechanical model as further data and insights are gathered in the Xinjiang Oil Field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".