Improved Drilling Efficiency Technique Using Integrated PDM and PDC Bit Parameters
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».