A machine learning approach to predict drilling rate using petrophysical and mud logging data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-25

AUTHORS

Mohammad Sabah, Mohsen Talebkeikhah, David A. Wood, Rasool Khosravanian, Mohammad Anemangely, Alireza Younesi

ABSTRACT

Predicting the drilling rate of penetration (ROP) is one approach to optimizing drilling performance. However, as ROP behavior is unique to specific geological conditions its application is not straightforward. Moreover, ROP is typically affected by various operational factors (e.g. bit type, weight-on-bit, rotation rate, etc.) as well as the geological characteristics of the rocks being penetrated. This makes ROP prediction an intricate and multi-faceted problem. Here we compare data mining methods with several machine learning algorithms to evaluate their accuracy and effectiveness in predicting ROP. The algorithms considered are: artificial neural networks (ANN) applying a multi-layer perceptron (MLP); ANN applying a radial basis function (RBF); support vector regression (SVR), and an hybrid MLP trained using a particle swarm optimization algorithm (MLP-PSO). Data preparation prior to executing the algorithms involves applying a Savitzky–Golay (SG) smoothing filter to remove noise from petrophysical well-logs and drilling data from the mud-logs. A genetic algorithm is applied to tune the machine learning algorithms by identifying and ranking the most influential input variables on ROP. This tuning routine identified and selected eight input variables which have the greatest impact on ROP. These are: weight on bit, bit rotational speed, pump flow rate, pump pressure, pore pressure, gamma ray, density log and sonic wave velocity. Results showed that the machine learning algorithms evaluated all predicted ROP accurately. Their performance was improved when applied to filtered data rather than raw well-log data. The MLP-PSO model as a hybrid ANN demonstrated superior accuracy and effectiveness compared to the other ROP-prediction algorithms evaluated, but its performance is rivalled by the SVR model. More... »

PAGES

1-21

References to SciGraph publications

  • 2016-10. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia in LANDSLIDES
  • 2017-05. Wavefield analysis of crosswell seismic data in ARABIAN JOURNAL OF GEOSCIENCES
  • 2016-04. Prediction of Drillability of Rocks with Strength Properties Using a Hybrid GA-ANN Technique in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2008-10. An Example of Artificial Neural Network (ANN) Application for Indirect Estimation of Rock Parameters in ROCK MECHANICS AND ROCK ENGINEERING
  • 2013-03. Viscosity prediction in selected Iranian light oil reservoirs: Artificial neural network versus empirical correlations in PETROLEUM SCIENCE
  • 2011-06. Extreme learning machines: a survey in INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
  • 1995-09. Support-vector networks in MACHINE LEARNING
  • 2013-02. Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks in ENVIRONMENTAL EARTH SCIENCES
  • 2019-04. Performance assessment of rotary drilling using non-linear multiple regression analysis and multilayer perceptron neural network in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2009-01. A random forest approach to the detection of epistatic interactions in case-control studies in BMC BIOINFORMATICS
  • 2015-05. Estimation of asphaltene precipitation from titration data: a hybrid support vector regression with harmony search in NEURAL COMPUTING AND APPLICATIONS
  • 2016-04. Estimating the Penetration Rate in Diamond Drilling in Laboratory Works Using the Regression and Artificial Neural Network Analysis in NEURAL PROCESSING LETTERS
  • 2013. An Introduction to Statistical Learning, with Applications in R in NONE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s12145-019-00381-4

    DOI

    http://dx.doi.org/10.1007/s12145-019-00381-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112987918


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Amirkabir University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.411368.9", 
              "name": [
                "Department of Petroleum Engineering, Amirkabir University of Technology (Tehran polytechnic), 424 Hafez Avenue, 15875-4413, Tehran, Iran"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sabah", 
            "givenName": "Mohammad", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Amirkabir University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.411368.9", 
              "name": [
                "Department of Petroleum Engineering, Amirkabir University of Technology (Tehran polytechnic), 424 Hafez Avenue, 15875-4413, Tehran, Iran"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Talebkeikhah", 
            "givenName": "Mohsen", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "DWA Energy Limited, Lincoln, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wood", 
            "givenName": "David A.", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Amirkabir University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.411368.9", 
              "name": [
                "Department of Petroleum Engineering, Amirkabir University of Technology (Tehran polytechnic), 424 Hafez Avenue, 15875-4413, Tehran, Iran"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Khosravanian", 
            "givenName": "Rasool", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Shahrood", 
              "id": "https://www.grid.ac/institutes/grid.440804.c", 
              "name": [
                "Faculty of mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Anemangely", 
            "givenName": "Mohammad", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Amirkabir University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.411368.9", 
              "name": [
                "Department of Petroleum Engineering, Amirkabir University of Technology (Tehran polytechnic), 424 Hafez Avenue, 15875-4413, Tehran, Iran"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Younesi", 
            "givenName": "Alireza", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1186/1471-2105-10-s1-s65", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001793384", 
              "https://doi.org/10.1186/1471-2105-10-s1-s65"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11063-015-9424-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002402286", 
              "https://doi.org/10.1007/s11063-015-9424-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1590/s1415-47572004000400031", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002569994"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jngse.2017.01.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003656628"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1155/2016/3575380", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008281941"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.powtec.2011.12.058", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012041174"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.5402/2012/324194", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012468544"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.eswa.2010.04.045", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013100442"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.supflu.2012.12.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014692344"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12665-012-1783-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017168196", 
              "https://doi.org/10.1007/s12665-012-1783-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.eswa.2010.11.027", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017724349"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.tust.2016.12.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022113557"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00603-007-0138-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024289044", 
              "https://doi.org/10.1007/s00603-007-0138-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00603-007-0138-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024289044", 
              "https://doi.org/10.1007/s00603-007-0138-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.enggeo.2014.02.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024534558"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1010933404324", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024739340", 
              "https://doi.org/10.1023/a:1010933404324"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00994018", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025150743", 
              "https://doi.org/10.1007/bf00994018"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neucom.2003.08.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025394358"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0122827", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027463138"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-014-1766-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029157900", 
              "https://doi.org/10.1007/s00521-014-1766-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jappgeo.2013.06.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029185867"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13042-011-0019-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031892380", 
              "https://doi.org/10.1007/s13042-011-0019-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10346-015-0614-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032244444", 
              "https://doi.org/10.1007/s10346-015-0614-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10706-015-9970-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034231673", 
              "https://doi.org/10.1007/s10706-015-9970-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/269012.269025", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037814739"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/cjce.22387", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038222258"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neucom.2014.10.085", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040905680"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.fluid.2012.03.015", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041210430"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1044216575", 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4614-7138-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044216575", 
              "https://doi.org/10.1007/978-1-4614-7138-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4614-7138-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044216575", 
              "https://doi.org/10.1007/978-1-4614-7138-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.sandf.2012.01.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045809650"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/269012.269023", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047317141"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/neco.1991.3.2.246", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048705139"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.compeleceng.2013.11.024", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048813730"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12182-013-0259-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048981993", 
              "https://doi.org/10.1007/s12182-013-0259-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12182-013-0259-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048981993", 
              "https://doi.org/10.1007/s12182-013-0259-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jngse.2014.05.029", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051719593"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/ac60214a047", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1055048783"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1088/1742-2132/9/3/336", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1059162847"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpami.2004.71", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061742749"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.14311/nnw.2011.21.012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1067263251"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2118/13259-pa", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1068949589"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2118/141651-pa", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1068950249"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2118/166472-pa", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1068952108"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.molliq.2017.01.098", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083425675"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1515/amsc-2017-0010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084322362"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2118/175564-pa", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085289654"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12517-017-2964-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085381217", 
              "https://doi.org/10.1007/s12517-017-2964-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12517-017-2964-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085381217", 
              "https://doi.org/10.1007/s12517-017-2964-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.petrol.2017.06.039", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1086064600"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.petrol.2017.09.020", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091891111"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.powtec.2017.10.038", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092323786"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.scitotenv.2017.10.323", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092649789"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10064-017-1192-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092809503", 
              "https://doi.org/10.1007/s10064-017-1192-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10064-017-1192-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092809503", 
              "https://doi.org/10.1007/s10064-017-1192-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cherd.2017.12.017", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099734120"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1088/1742-2140/aaac5d", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100781103"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1515/geo-2015-0054", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103764529"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1515/geo-2015-0054", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103764529"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.petrol.2018.11.032", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1109992387"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.petrol.2018.11.032", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1109992387"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-03-25", 
        "datePublishedReg": "2019-03-25", 
        "description": "Predicting the drilling rate of penetration (ROP) is one approach to optimizing drilling performance. However, as ROP behavior is unique to specific geological conditions its application is not straightforward. Moreover, ROP is typically affected by various operational factors (e.g. bit type, weight-on-bit, rotation rate, etc.) as well as the geological characteristics of the rocks being penetrated. This makes ROP prediction an intricate and multi-faceted problem. Here we compare data mining methods with several machine learning algorithms to evaluate their accuracy and effectiveness in predicting ROP. The algorithms considered are: artificial neural networks (ANN) applying a multi-layer perceptron (MLP); ANN applying a radial basis function (RBF); support vector regression (SVR), and an hybrid MLP trained using a particle swarm optimization algorithm (MLP-PSO). Data preparation prior to executing the algorithms involves applying a Savitzky\u2013Golay (SG) smoothing filter to remove noise from petrophysical well-logs and drilling data from the mud-logs. A genetic algorithm is applied to tune the machine learning algorithms by identifying and ranking the most influential input variables on ROP. This tuning routine identified and selected eight input variables which have the greatest impact on ROP. These are: weight on bit, bit rotational speed, pump flow rate, pump pressure, pore pressure, gamma ray, density log and sonic wave velocity. Results showed that the machine learning algorithms evaluated all predicted ROP accurately. Their performance was improved when applied to filtered data rather than raw well-log data. The MLP-PSO model as a hybrid ANN demonstrated superior accuracy and effectiveness compared to the other ROP-prediction algorithms evaluated, but its performance is rivalled by the SVR model.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s12145-019-00381-4", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1049211", 
            "issn": [
              "1865-0473", 
              "1865-0481"
            ], 
            "name": "Earth Science Informatics", 
            "type": "Periodical"
          }
        ], 
        "name": "A machine learning approach to predict drilling rate using petrophysical and mud logging data", 
        "pagination": "1-21", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "95a4d51e4ae0104d78ae12fca0ffafb742a042d2ffddcadf27e14bcd74d84ee7"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s12145-019-00381-4"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1112987918"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s12145-019-00381-4", 
          "https://app.dimensions.ai/details/publication/pub.1112987918"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T13:05", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000366_0000000366/records_112069_00000001.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs12145-019-00381-4"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12145-019-00381-4'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12145-019-00381-4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12145-019-00381-4'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12145-019-00381-4'


     

    This table displays all metadata directly associated to this object as RDF triples.

    267 TRIPLES      21 PREDICATES      79 URIs      16 LITERALS      5 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s12145-019-00381-4 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Nd9ccaafc3b32401ca000aabc9fa81f02
    4 schema:citation sg:pub.10.1007/978-1-4614-7138-7
    5 sg:pub.10.1007/bf00994018
    6 sg:pub.10.1007/s00521-014-1766-y
    7 sg:pub.10.1007/s00603-007-0138-7
    8 sg:pub.10.1007/s10064-017-1192-3
    9 sg:pub.10.1007/s10346-015-0614-1
    10 sg:pub.10.1007/s10706-015-9970-9
    11 sg:pub.10.1007/s11063-015-9424-7
    12 sg:pub.10.1007/s12182-013-0259-4
    13 sg:pub.10.1007/s12517-017-2964-6
    14 sg:pub.10.1007/s12665-012-1783-z
    15 sg:pub.10.1007/s13042-011-0019-y
    16 sg:pub.10.1023/a:1010933404324
    17 sg:pub.10.1186/1471-2105-10-s1-s65
    18 https://app.dimensions.ai/details/publication/pub.1044216575
    19 https://doi.org/10.1002/cjce.22387
    20 https://doi.org/10.1016/j.cherd.2017.12.017
    21 https://doi.org/10.1016/j.compeleceng.2013.11.024
    22 https://doi.org/10.1016/j.enggeo.2014.02.006
    23 https://doi.org/10.1016/j.eswa.2010.04.045
    24 https://doi.org/10.1016/j.eswa.2010.11.027
    25 https://doi.org/10.1016/j.fluid.2012.03.015
    26 https://doi.org/10.1016/j.jappgeo.2013.06.006
    27 https://doi.org/10.1016/j.jngse.2014.05.029
    28 https://doi.org/10.1016/j.jngse.2017.01.003
    29 https://doi.org/10.1016/j.molliq.2017.01.098
    30 https://doi.org/10.1016/j.neucom.2003.08.006
    31 https://doi.org/10.1016/j.neucom.2014.10.085
    32 https://doi.org/10.1016/j.petrol.2017.06.039
    33 https://doi.org/10.1016/j.petrol.2017.09.020
    34 https://doi.org/10.1016/j.petrol.2018.11.032
    35 https://doi.org/10.1016/j.powtec.2011.12.058
    36 https://doi.org/10.1016/j.powtec.2017.10.038
    37 https://doi.org/10.1016/j.sandf.2012.01.002
    38 https://doi.org/10.1016/j.scitotenv.2017.10.323
    39 https://doi.org/10.1016/j.supflu.2012.12.009
    40 https://doi.org/10.1016/j.tust.2016.12.009
    41 https://doi.org/10.1021/ac60214a047
    42 https://doi.org/10.1088/1742-2132/9/3/336
    43 https://doi.org/10.1088/1742-2140/aaac5d
    44 https://doi.org/10.1109/tpami.2004.71
    45 https://doi.org/10.1145/269012.269023
    46 https://doi.org/10.1145/269012.269025
    47 https://doi.org/10.1155/2016/3575380
    48 https://doi.org/10.1162/neco.1991.3.2.246
    49 https://doi.org/10.1371/journal.pone.0122827
    50 https://doi.org/10.14311/nnw.2011.21.012
    51 https://doi.org/10.1515/amsc-2017-0010
    52 https://doi.org/10.1515/geo-2015-0054
    53 https://doi.org/10.1590/s1415-47572004000400031
    54 https://doi.org/10.2118/13259-pa
    55 https://doi.org/10.2118/141651-pa
    56 https://doi.org/10.2118/166472-pa
    57 https://doi.org/10.2118/175564-pa
    58 https://doi.org/10.5402/2012/324194
    59 schema:datePublished 2019-03-25
    60 schema:datePublishedReg 2019-03-25
    61 schema:description Predicting the drilling rate of penetration (ROP) is one approach to optimizing drilling performance. However, as ROP behavior is unique to specific geological conditions its application is not straightforward. Moreover, ROP is typically affected by various operational factors (e.g. bit type, weight-on-bit, rotation rate, etc.) as well as the geological characteristics of the rocks being penetrated. This makes ROP prediction an intricate and multi-faceted problem. Here we compare data mining methods with several machine learning algorithms to evaluate their accuracy and effectiveness in predicting ROP. The algorithms considered are: artificial neural networks (ANN) applying a multi-layer perceptron (MLP); ANN applying a radial basis function (RBF); support vector regression (SVR), and an hybrid MLP trained using a particle swarm optimization algorithm (MLP-PSO). Data preparation prior to executing the algorithms involves applying a Savitzky–Golay (SG) smoothing filter to remove noise from petrophysical well-logs and drilling data from the mud-logs. A genetic algorithm is applied to tune the machine learning algorithms by identifying and ranking the most influential input variables on ROP. This tuning routine identified and selected eight input variables which have the greatest impact on ROP. These are: weight on bit, bit rotational speed, pump flow rate, pump pressure, pore pressure, gamma ray, density log and sonic wave velocity. Results showed that the machine learning algorithms evaluated all predicted ROP accurately. Their performance was improved when applied to filtered data rather than raw well-log data. The MLP-PSO model as a hybrid ANN demonstrated superior accuracy and effectiveness compared to the other ROP-prediction algorithms evaluated, but its performance is rivalled by the SVR model.
    62 schema:genre research_article
    63 schema:inLanguage en
    64 schema:isAccessibleForFree false
    65 schema:isPartOf sg:journal.1049211
    66 schema:name A machine learning approach to predict drilling rate using petrophysical and mud logging data
    67 schema:pagination 1-21
    68 schema:productId N572559269ee14febbe0d8f81690a93b6
    69 N6bca3e3ddd5243598c3cba6bab338119
    70 Nb16012b422a04110887d10f1c939d7f2
    71 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112987918
    72 https://doi.org/10.1007/s12145-019-00381-4
    73 schema:sdDatePublished 2019-04-11T13:05
    74 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    75 schema:sdPublisher N2ead9572f13d4918ac66f8e5e2b32e67
    76 schema:url https://link.springer.com/10.1007%2Fs12145-019-00381-4
    77 sgo:license sg:explorer/license/
    78 sgo:sdDataset articles
    79 rdf:type schema:ScholarlyArticle
    80 N01275afbeaa84658bd040a6b1e52038b schema:affiliation https://www.grid.ac/institutes/grid.411368.9
    81 schema:familyName Sabah
    82 schema:givenName Mohammad
    83 rdf:type schema:Person
    84 N099fc3ebe3304c74af6d63a4b10e74f0 rdf:first N3585dff3fc0b4cae9e92c696b86eb662
    85 rdf:rest Nba3a960a0d9846f193d38b657473a8d6
    86 N0ba4ab73546f4378b5af6b5d3d0134e6 rdf:first Neb6d55e6eca34cf9a5767fd6c655a877
    87 rdf:rest rdf:nil
    88 N2ead9572f13d4918ac66f8e5e2b32e67 schema:name Springer Nature - SN SciGraph project
    89 rdf:type schema:Organization
    90 N3585dff3fc0b4cae9e92c696b86eb662 schema:affiliation https://www.grid.ac/institutes/grid.411368.9
    91 schema:familyName Khosravanian
    92 schema:givenName Rasool
    93 rdf:type schema:Person
    94 N3f18d3012c44446483dbf4b756b2e2bc schema:affiliation https://www.grid.ac/institutes/grid.440804.c
    95 schema:familyName Anemangely
    96 schema:givenName Mohammad
    97 rdf:type schema:Person
    98 N572559269ee14febbe0d8f81690a93b6 schema:name doi
    99 schema:value 10.1007/s12145-019-00381-4
    100 rdf:type schema:PropertyValue
    101 N64fb76d68bbc48b48afc1df214062104 rdf:first N74e084d256294f3385b79794a6761987
    102 rdf:rest N099fc3ebe3304c74af6d63a4b10e74f0
    103 N6bca3e3ddd5243598c3cba6bab338119 schema:name dimensions_id
    104 schema:value pub.1112987918
    105 rdf:type schema:PropertyValue
    106 N6c61e3197f064dcc9d2c7b1abf064c66 rdf:first N751a7c77b4f1467a8d468f5ee791948b
    107 rdf:rest N64fb76d68bbc48b48afc1df214062104
    108 N6f61cc515dd44d72a090dc37f36aeaae schema:name DWA Energy Limited, Lincoln, UK
    109 rdf:type schema:Organization
    110 N74e084d256294f3385b79794a6761987 schema:affiliation N6f61cc515dd44d72a090dc37f36aeaae
    111 schema:familyName Wood
    112 schema:givenName David A.
    113 rdf:type schema:Person
    114 N751a7c77b4f1467a8d468f5ee791948b schema:affiliation https://www.grid.ac/institutes/grid.411368.9
    115 schema:familyName Talebkeikhah
    116 schema:givenName Mohsen
    117 rdf:type schema:Person
    118 Nb16012b422a04110887d10f1c939d7f2 schema:name readcube_id
    119 schema:value 95a4d51e4ae0104d78ae12fca0ffafb742a042d2ffddcadf27e14bcd74d84ee7
    120 rdf:type schema:PropertyValue
    121 Nba3a960a0d9846f193d38b657473a8d6 rdf:first N3f18d3012c44446483dbf4b756b2e2bc
    122 rdf:rest N0ba4ab73546f4378b5af6b5d3d0134e6
    123 Nd9ccaafc3b32401ca000aabc9fa81f02 rdf:first N01275afbeaa84658bd040a6b1e52038b
    124 rdf:rest N6c61e3197f064dcc9d2c7b1abf064c66
    125 Neb6d55e6eca34cf9a5767fd6c655a877 schema:affiliation https://www.grid.ac/institutes/grid.411368.9
    126 schema:familyName Younesi
    127 schema:givenName Alireza
    128 rdf:type schema:Person
    129 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    130 schema:name Information and Computing Sciences
    131 rdf:type schema:DefinedTerm
    132 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    133 schema:name Artificial Intelligence and Image Processing
    134 rdf:type schema:DefinedTerm
    135 sg:journal.1049211 schema:issn 1865-0473
    136 1865-0481
    137 schema:name Earth Science Informatics
    138 rdf:type schema:Periodical
    139 sg:pub.10.1007/978-1-4614-7138-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044216575
    140 https://doi.org/10.1007/978-1-4614-7138-7
    141 rdf:type schema:CreativeWork
    142 sg:pub.10.1007/bf00994018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025150743
    143 https://doi.org/10.1007/bf00994018
    144 rdf:type schema:CreativeWork
    145 sg:pub.10.1007/s00521-014-1766-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1029157900
    146 https://doi.org/10.1007/s00521-014-1766-y
    147 rdf:type schema:CreativeWork
    148 sg:pub.10.1007/s00603-007-0138-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024289044
    149 https://doi.org/10.1007/s00603-007-0138-7
    150 rdf:type schema:CreativeWork
    151 sg:pub.10.1007/s10064-017-1192-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092809503
    152 https://doi.org/10.1007/s10064-017-1192-3
    153 rdf:type schema:CreativeWork
    154 sg:pub.10.1007/s10346-015-0614-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032244444
    155 https://doi.org/10.1007/s10346-015-0614-1
    156 rdf:type schema:CreativeWork
    157 sg:pub.10.1007/s10706-015-9970-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034231673
    158 https://doi.org/10.1007/s10706-015-9970-9
    159 rdf:type schema:CreativeWork
    160 sg:pub.10.1007/s11063-015-9424-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002402286
    161 https://doi.org/10.1007/s11063-015-9424-7
    162 rdf:type schema:CreativeWork
    163 sg:pub.10.1007/s12182-013-0259-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048981993
    164 https://doi.org/10.1007/s12182-013-0259-4
    165 rdf:type schema:CreativeWork
    166 sg:pub.10.1007/s12517-017-2964-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085381217
    167 https://doi.org/10.1007/s12517-017-2964-6
    168 rdf:type schema:CreativeWork
    169 sg:pub.10.1007/s12665-012-1783-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1017168196
    170 https://doi.org/10.1007/s12665-012-1783-z
    171 rdf:type schema:CreativeWork
    172 sg:pub.10.1007/s13042-011-0019-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1031892380
    173 https://doi.org/10.1007/s13042-011-0019-y
    174 rdf:type schema:CreativeWork
    175 sg:pub.10.1023/a:1010933404324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024739340
    176 https://doi.org/10.1023/a:1010933404324
    177 rdf:type schema:CreativeWork
    178 sg:pub.10.1186/1471-2105-10-s1-s65 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001793384
    179 https://doi.org/10.1186/1471-2105-10-s1-s65
    180 rdf:type schema:CreativeWork
    181 https://app.dimensions.ai/details/publication/pub.1044216575 schema:CreativeWork
    182 https://doi.org/10.1002/cjce.22387 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038222258
    183 rdf:type schema:CreativeWork
    184 https://doi.org/10.1016/j.cherd.2017.12.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099734120
    185 rdf:type schema:CreativeWork
    186 https://doi.org/10.1016/j.compeleceng.2013.11.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048813730
    187 rdf:type schema:CreativeWork
    188 https://doi.org/10.1016/j.enggeo.2014.02.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024534558
    189 rdf:type schema:CreativeWork
    190 https://doi.org/10.1016/j.eswa.2010.04.045 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013100442
    191 rdf:type schema:CreativeWork
    192 https://doi.org/10.1016/j.eswa.2010.11.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017724349
    193 rdf:type schema:CreativeWork
    194 https://doi.org/10.1016/j.fluid.2012.03.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041210430
    195 rdf:type schema:CreativeWork
    196 https://doi.org/10.1016/j.jappgeo.2013.06.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029185867
    197 rdf:type schema:CreativeWork
    198 https://doi.org/10.1016/j.jngse.2014.05.029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051719593
    199 rdf:type schema:CreativeWork
    200 https://doi.org/10.1016/j.jngse.2017.01.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003656628
    201 rdf:type schema:CreativeWork
    202 https://doi.org/10.1016/j.molliq.2017.01.098 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083425675
    203 rdf:type schema:CreativeWork
    204 https://doi.org/10.1016/j.neucom.2003.08.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025394358
    205 rdf:type schema:CreativeWork
    206 https://doi.org/10.1016/j.neucom.2014.10.085 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040905680
    207 rdf:type schema:CreativeWork
    208 https://doi.org/10.1016/j.petrol.2017.06.039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086064600
    209 rdf:type schema:CreativeWork
    210 https://doi.org/10.1016/j.petrol.2017.09.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091891111
    211 rdf:type schema:CreativeWork
    212 https://doi.org/10.1016/j.petrol.2018.11.032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109992387
    213 rdf:type schema:CreativeWork
    214 https://doi.org/10.1016/j.powtec.2011.12.058 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012041174
    215 rdf:type schema:CreativeWork
    216 https://doi.org/10.1016/j.powtec.2017.10.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092323786
    217 rdf:type schema:CreativeWork
    218 https://doi.org/10.1016/j.sandf.2012.01.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045809650
    219 rdf:type schema:CreativeWork
    220 https://doi.org/10.1016/j.scitotenv.2017.10.323 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092649789
    221 rdf:type schema:CreativeWork
    222 https://doi.org/10.1016/j.supflu.2012.12.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014692344
    223 rdf:type schema:CreativeWork
    224 https://doi.org/10.1016/j.tust.2016.12.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022113557
    225 rdf:type schema:CreativeWork
    226 https://doi.org/10.1021/ac60214a047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055048783
    227 rdf:type schema:CreativeWork
    228 https://doi.org/10.1088/1742-2132/9/3/336 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059162847
    229 rdf:type schema:CreativeWork
    230 https://doi.org/10.1088/1742-2140/aaac5d schema:sameAs https://app.dimensions.ai/details/publication/pub.1100781103
    231 rdf:type schema:CreativeWork
    232 https://doi.org/10.1109/tpami.2004.71 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061742749
    233 rdf:type schema:CreativeWork
    234 https://doi.org/10.1145/269012.269023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047317141
    235 rdf:type schema:CreativeWork
    236 https://doi.org/10.1145/269012.269025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037814739
    237 rdf:type schema:CreativeWork
    238 https://doi.org/10.1155/2016/3575380 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008281941
    239 rdf:type schema:CreativeWork
    240 https://doi.org/10.1162/neco.1991.3.2.246 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048705139
    241 rdf:type schema:CreativeWork
    242 https://doi.org/10.1371/journal.pone.0122827 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027463138
    243 rdf:type schema:CreativeWork
    244 https://doi.org/10.14311/nnw.2011.21.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067263251
    245 rdf:type schema:CreativeWork
    246 https://doi.org/10.1515/amsc-2017-0010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084322362
    247 rdf:type schema:CreativeWork
    248 https://doi.org/10.1515/geo-2015-0054 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103764529
    249 rdf:type schema:CreativeWork
    250 https://doi.org/10.1590/s1415-47572004000400031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002569994
    251 rdf:type schema:CreativeWork
    252 https://doi.org/10.2118/13259-pa schema:sameAs https://app.dimensions.ai/details/publication/pub.1068949589
    253 rdf:type schema:CreativeWork
    254 https://doi.org/10.2118/141651-pa schema:sameAs https://app.dimensions.ai/details/publication/pub.1068950249
    255 rdf:type schema:CreativeWork
    256 https://doi.org/10.2118/166472-pa schema:sameAs https://app.dimensions.ai/details/publication/pub.1068952108
    257 rdf:type schema:CreativeWork
    258 https://doi.org/10.2118/175564-pa schema:sameAs https://app.dimensions.ai/details/publication/pub.1085289654
    259 rdf:type schema:CreativeWork
    260 https://doi.org/10.5402/2012/324194 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012468544
    261 rdf:type schema:CreativeWork
    262 https://www.grid.ac/institutes/grid.411368.9 schema:alternateName Amirkabir University of Technology
    263 schema:name Department of Petroleum Engineering, Amirkabir University of Technology (Tehran polytechnic), 424 Hafez Avenue, 15875-4413, Tehran, Iran
    264 rdf:type schema:Organization
    265 https://www.grid.ac/institutes/grid.440804.c schema:alternateName University of Shahrood
    266 schema:name Faculty of mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
    267 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...