Improved Drilling Efficiency Technique Using Integrated PDM and PDC Bit Parameters
Bibliographic record
Abstract
Summary In this paper, a new drilling optimization procedure is presented that is designed to improve the drilling efficiency with positive displacement motors (PDMs) and PDC bits. This developed optimization method is based on predicting rate of penetration (ROP) from PDM outputs for any PDC bit design. More specifically, optimization is done for a hole section and optimum values of weight on bit (WOB) and surface RPM are obtained for the section. For given flow rates, estimated values of optimum WOB and surface RPM are used to calculate the corresponding motor differential pressures and the foot by foot ROP values. Also, the method is used to show how improper operational parameter selection can affect total drilling time. A case study was done to consider different PDMs with different lobe configurations and a set of fixed operational parameters. The presented method is verified by generating a confined rock strength log based on drilling data for a previously drilled well in Alberta. This foot-by-foot strength log is compared to a confined rock strength log generated as a follow-up analysis by a commercially available drilling simulator package. Also, a PDM differential pressure log is generated and compared to field-recorded on-bottom differential pressure values. The method's application is best demonstrated by simulating the drilling operation of the Alberta well with three different PDMs. It is shown that consideration of PDM performance/selection in the drilling planning phase will help to perform a safe and cost-effective operation by preventing motor stalls and maintaining highest average ROP for the section. It is also shown that by optimizing WOB and surface RPM values for a constant mud flow rate and predefined bit wear at total depth, a maximum average ROP for the section can be reached for any PDM.
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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 | 0.000 | 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) | 0.001 | 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".