Matches in SemOpenAlex for { <https://semopenalex.org/work/W4289100944> ?p ?o ?g. }
Showing items 1 to 79 of
79
with 100 items per page.
- W4289100944 abstract "Abstract The demand for cost-effective drilling operations in oil and gas exploration is ever growing. One of the important aspects to tackling the aforementioned difficulty is determining the optimal rate of penetration (ROP) of the drill bit. The most important optimization objective is to achieve a high optimal rate of penetration in safe and stable drilling conditions. Several machine learning models have been developed to predict ROP, however, there have been few studies that consider the different optimization algorithms needed to optimize the conventional developed models other than the conventional grid search and random search techniques. Genetic algorithm (GA) has gained much attention as methods of optimizing the predictions of machine learning algorithms in different fields of study. In this study, GA optimization algorithm was implemented to optimize 5 machine learning algorithms: Linear Regression, Decision Tree, Support Vector Machine, Random Forest, and Multilayer Perceptron algorithm while using torque, weight on bit, surface RPM, mud flow, pump pressure, downhole temperature and pressure, etc, as input parameters. Three scenarios were analyzed using a train-test split ratio of 70-30, 80-20 and 85-15 percent on all the developed models. The results from the comparative study of all models developed shows that the implementation of the GA optimization algorithms increased the individual ROP models, with the multilayer perceptron model having the highest coefficient of determination of 0.989% after GA optimization." @default.
- W4289100944 created "2022-08-01" @default.
- W4289100944 creator A5046702260 @default.
- W4289100944 creator A5047368648 @default.
- W4289100944 creator A5085101435 @default.
- W4289100944 date "2022-08-01" @default.
- W4289100944 modified "2023-10-03" @default.
- W4289100944 title "Application of Genetic Algorithm on Data Driven Models for Optimized ROP Prediction" @default.
- W4289100944 cites W2000101341 @default.
- W4289100944 cites W2048204509 @default.
- W4289100944 cites W2129272928 @default.
- W4289100944 cites W2137983211 @default.
- W4289100944 cites W2895179101 @default.
- W4289100944 cites W2897975904 @default.
- W4289100944 cites W2899790004 @default.
- W4289100944 cites W2903823226 @default.
- W4289100944 cites W2930783316 @default.
- W4289100944 cites W2966908144 @default.
- W4289100944 cites W2974397275 @default.
- W4289100944 cites W3096017037 @default.
- W4289100944 cites W4230686035 @default.
- W4289100944 cites W4238514559 @default.
- W4289100944 doi "https://doi.org/10.2118/212016-ms" @default.
- W4289100944 hasPublicationYear "2022" @default.
- W4289100944 type Work @default.
- W4289100944 citedByCount "3" @default.
- W4289100944 countsByYear W42891009442023 @default.
- W4289100944 crossrefType "proceedings-article" @default.
- W4289100944 hasAuthorship W4289100944A5046702260 @default.
- W4289100944 hasAuthorship W4289100944A5047368648 @default.
- W4289100944 hasAuthorship W4289100944A5085101435 @default.
- W4289100944 hasConcept C11413529 @default.
- W4289100944 hasConcept C119857082 @default.
- W4289100944 hasConcept C12267149 @default.
- W4289100944 hasConcept C126255220 @default.
- W4289100944 hasConcept C127413603 @default.
- W4289100944 hasConcept C154945302 @default.
- W4289100944 hasConcept C179717631 @default.
- W4289100944 hasConcept C25197100 @default.
- W4289100944 hasConcept C2776497017 @default.
- W4289100944 hasConcept C33923547 @default.
- W4289100944 hasConcept C41008148 @default.
- W4289100944 hasConcept C50644808 @default.
- W4289100944 hasConcept C60908668 @default.
- W4289100944 hasConcept C78519656 @default.
- W4289100944 hasConcept C84525736 @default.
- W4289100944 hasConcept C8880873 @default.
- W4289100944 hasConceptScore W4289100944C11413529 @default.
- W4289100944 hasConceptScore W4289100944C119857082 @default.
- W4289100944 hasConceptScore W4289100944C12267149 @default.
- W4289100944 hasConceptScore W4289100944C126255220 @default.
- W4289100944 hasConceptScore W4289100944C127413603 @default.
- W4289100944 hasConceptScore W4289100944C154945302 @default.
- W4289100944 hasConceptScore W4289100944C179717631 @default.
- W4289100944 hasConceptScore W4289100944C25197100 @default.
- W4289100944 hasConceptScore W4289100944C2776497017 @default.
- W4289100944 hasConceptScore W4289100944C33923547 @default.
- W4289100944 hasConceptScore W4289100944C41008148 @default.
- W4289100944 hasConceptScore W4289100944C50644808 @default.
- W4289100944 hasConceptScore W4289100944C60908668 @default.
- W4289100944 hasConceptScore W4289100944C78519656 @default.
- W4289100944 hasConceptScore W4289100944C84525736 @default.
- W4289100944 hasConceptScore W4289100944C8880873 @default.
- W4289100944 hasLocation W42891009441 @default.
- W4289100944 hasOpenAccess W4289100944 @default.
- W4289100944 hasPrimaryLocation W42891009441 @default.
- W4289100944 hasRelatedWork W2940336242 @default.
- W4289100944 hasRelatedWork W3028499805 @default.
- W4289100944 hasRelatedWork W3097220695 @default.
- W4289100944 hasRelatedWork W3168994312 @default.
- W4289100944 hasRelatedWork W3185179407 @default.
- W4289100944 hasRelatedWork W4231994957 @default.
- W4289100944 hasRelatedWork W4282977429 @default.
- W4289100944 hasRelatedWork W4285741730 @default.
- W4289100944 hasRelatedWork W4320802194 @default.
- W4289100944 hasRelatedWork W4361795583 @default.
- W4289100944 isParatext "false" @default.
- W4289100944 isRetracted "false" @default.
- W4289100944 workType "article" @default.