Okorie Ekwe Agwu , Julius Udoh Akpabio , Adewale Dosunmu
{"title":"基于多基因遗传规划的钻井泥浆井下密度建模","authors":"Okorie Ekwe Agwu , Julius Udoh Akpabio , Adewale Dosunmu","doi":"10.1016/j.upstre.2020.100030","DOIUrl":null,"url":null,"abstract":"<div><p><span><span>The main objective of this paper is to use experimental measurements of downhole pressure, temperature and initial mud density to predict downhole density using multigene genetic programming. From the results, the mean square error for the WBM density model was 0.0012, with a </span>mean absolute error<span> of 0.0246 and the square of correlation coefficient (R</span></span><sup>2</sup>) was 0.9998; while for the OBM, the MSE was 0.000359 with MAE of 0.01436 and R<sup>2</sup> of 0.99995. In assessing the OBM model's generalization capability, the model had an MSE of 0.031, MAE of 0.138 and mean absolute percentage error (MAPE) of 0.95%.</p></div>","PeriodicalId":101264,"journal":{"name":"Upstream Oil and Gas Technology","volume":"6 ","pages":"Article 100030"},"PeriodicalIF":2.6000,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.upstre.2020.100030","citationCount":"4","resultStr":"{\"title\":\"Modeling the downhole density of drilling muds using multigene genetic programming\",\"authors\":\"Okorie Ekwe Agwu , Julius Udoh Akpabio , Adewale Dosunmu\",\"doi\":\"10.1016/j.upstre.2020.100030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span><span>The main objective of this paper is to use experimental measurements of downhole pressure, temperature and initial mud density to predict downhole density using multigene genetic programming. From the results, the mean square error for the WBM density model was 0.0012, with a </span>mean absolute error<span> of 0.0246 and the square of correlation coefficient (R</span></span><sup>2</sup>) was 0.9998; while for the OBM, the MSE was 0.000359 with MAE of 0.01436 and R<sup>2</sup> of 0.99995. In assessing the OBM model's generalization capability, the model had an MSE of 0.031, MAE of 0.138 and mean absolute percentage error (MAPE) of 0.95%.</p></div>\",\"PeriodicalId\":101264,\"journal\":{\"name\":\"Upstream Oil and Gas Technology\",\"volume\":\"6 \",\"pages\":\"Article 100030\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2021-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.upstre.2020.100030\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Upstream Oil and Gas Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S266626042030030X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Upstream Oil and Gas Technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266626042030030X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Modeling the downhole density of drilling muds using multigene genetic programming
The main objective of this paper is to use experimental measurements of downhole pressure, temperature and initial mud density to predict downhole density using multigene genetic programming. From the results, the mean square error for the WBM density model was 0.0012, with a mean absolute error of 0.0246 and the square of correlation coefficient (R2) was 0.9998; while for the OBM, the MSE was 0.000359 with MAE of 0.01436 and R2 of 0.99995. In assessing the OBM model's generalization capability, the model had an MSE of 0.031, MAE of 0.138 and mean absolute percentage error (MAPE) of 0.95%.
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