Integrated Pressure Management Processes for Reducing the Risk Associated with Well Instability Issues: Implications for HPHT Wells
Bibliographic record
Abstract
Abstract Optimizing well control processes are critical in high-temperature/high-pressure (HPHT) drilling operations so they do not encounter high cost overruns and compromise safety. The key to success is recognizing and mitigating challenges and associated risks early to adequately optimize drilling operations. This leads to a more effective drilling operation with reduced risk, increased safety margins and increased probability of successfully achieving the well’s objectives. This case describes an integrated work process that has been implemented, incorporating pre-drill and real-time pore pressure prediction with proactive equivalent circulating density (ECD) management during well planning and drilling operations. This work process is especially important for optimizing drilling fluid properties to retain the drilling parameters within a safe operating mud window identified by real-time pore pressure and wellbore stability prediction. Operating in this safe window enables reduction in wellbore instability, formation damage, hole cleaning inefficiencies and poor drilling performance, resulting in improved safety margins, reduced risk, improved drilling performance and reduction in non-productive-time (NPT). Several recent examples from Suncor Energy Norge HPHT wells are presented to illustrate the success of utilizing this integrated approach, resulting in drilling HPHT wells with no formation pressure-related NPT. The process begins with identifying pressure-related challenges in the pre-drill planning phase, optimizing the drilling process by validating, defining and maintain the drilling parameters within the safe operational window through an integration of proactive real-time pore pressure prediction and ECD management using all available LWD measurements: acoustic, gamma, resistivity, density, formation pressure while drilling, imaging, ECD, and temperature. Analysis is performed on mud logging data such as gas, the drilling exponent, cuttings and borehole caving and surface drilling data. Finally, lessons learned are captured that will further improve drilling efficiency and best practices in upcoming drilling campaigns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".