Improved Drilling Efficiency Technique Using Integrated PDM and PDC Bit Parameters
Bibliographic record
Abstract
Abstract 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 flowrates, 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 parameters 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. From the new optimization technique, a pre-estimated motor differential pressure versus depth log can be generated for the optimized operational parameters. This information can further be used in hydraulics optimization and proper selection of bit nozzles. The optimization method can not only be applied to determine optimum operational parameters in drilling for one motor, it is also can be used to select the optimum PDM to drill a section most economically. 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, PDM differential pressure log is generated and compared to field recorded on bottom differential pressure values. In this paper, a method to optimize the drilling operation with PDMs is presented with a field sample application. It is concluded that consideration of PDM performance/selection 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 also proves that by optimizing WOB and surface RPM values for a constant mud flow rate and pre defined bit wear at total depth, a maximum average ROP for the section can be reached for any PDM. The PDM with the lowest $/ft can then be selected based on the analysis. Introduction Drilling simulation has gained a leading position in the well planning and the follow up process. The capability of applying different drilling scenarios in simulators is a powerful tool for drilling engineer to prepare an optimized drilling plan regarding basic criteria such as cost, time and safety. The rising application of Positive Displacement Motors (PDMs) in recent directional and horizontal drilling operations creates a demand for a tool which enables drilling engineer to simulate these operations in a pre-planning mode. Despite of similarities existing between conventional drilling simulation and PDM drilling simulation, there are some differences which can alterthe approach and goals. These dissimilarities are resultant of inseparable characteristics of PDM which exposes limitation on the drilling system hydraulics.
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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".