Predicting and Optimizing ROP in Competent Shale by Utilizing MPD Technology
Bibliographic record
Abstract
Abstract Advanced Managed Pressure Drilling (MPD) technology was used onshore in Western Canada to optimize ROP while drilling horizontally through the potentially sour shale formation using a low solids, low density synthetic base mud system. The Montney shale formation is very challenging to drill conventionally and has the potential for drilling into high pressure fractures which may contain sour gas. The overall objective consisted of using a lower density synthetic base mud system to optimize the ROP, thereby saving on overall drilling costs by reducing the number of drilling days and also drill the well safely due to the possibility of high pressure fractures and potentially sour gas. The main challenge was to increase ROP while still maintaining a constant bottomhole pressure to prevent any gas inflow into the wellbore. In order to optimize drilling efficiencies and save on drilling time, advanced MPD technology was applied in combination with a low density (770 kg/m3) synthetic based mud while holding surface back pressure (SBP) in dynamic and static conditions to maintain a constant bottomhole equivalent circulating density (ECD). The system's annular pressure control mode was used to hold a desired ECD at a pre-determined position within the wellbore; In this case the heel was chosen. Constant bottomhole pressure operations were maintained throughout the entire project. The desired ECD was achieved and the well was kept at balance with the pore pressure. The advanced MPD system also provided the ability to monitor, detect and control an influx at the bit much earlier than conventional MPD and drilling systems which provided additional safety to the location. While drilling conventionally the ROP was extremely slow averaging 3.32 m/hr. The results greatly improved using advanced MPD technology with average ROP's between 7.4–10.5 m/hr. This application also brought value to the project by reducing mud costs, increasing overall ROP as compared to conventional offset ROP data, maintaining constant bottomhole pressure with flexibility to adjust pressures as the well dictated, increasing safety through detection and control of micro influxes while drilling, reducing bits used as compared to conventional offset bit record data and minimizing any gas to surface. Achieving the goal of increasing the ROP and safely drilling the well to TD reduced the number of drilling days by at least 13 days, thereby reducing the overall AFE of the well. This paper will include engineering analysis of the project compared to offset data along with graphs and tables. It will further include detailed explanation of pre-engineering and execution methods of advanced MPD in order to achieve ROP optimization and reduce your drilling cost.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".