{"id":"https://openalex.org/W4395083507","doi":"https://doi.org/10.2118/218836-ms","title":"Enhancing Pressure Transient Analysis with Automatic Model Identification: A Machine Learning Approach","display_name":"Enhancing Pressure Transient Analysis with Automatic Model Identification: A Machine Learning Approach","publication_year":2024,"publication_date":"2024-04-09","ids":{"openalex":"https://openalex.org/W4395083507","doi":"https://doi.org/10.2118/218836-ms"},"language":"en","primary_location":{"id":"doi:10.2118/218836-ms","doi":"https://doi.org/10.2118/218836-ms","is_oa":false,"landing_page_url":"https://doi.org/10.2118/218836-ms","pdf_url":null,"source":{"id":"https://openalex.org/S4306530718","display_name":"SPE Western Regional Meeting","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPE Western Regional Meeting","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114152856","display_name":"Sai Teja Nukala","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S. T. Nukala","raw_affiliation_strings":["SLB, Mumbai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SLB, Mumbai, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075482231","display_name":"Arvind Kumar","orcid":"https://orcid.org/0000-0002-2264-8743","observed_orcids":["https://orcid.org/0000-0002-2264-8743"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"A. Kumar","raw_affiliation_strings":["SLB, Abu Dhabi, United Arab Emirates"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SLB, Abu Dhabi, United Arab Emirates","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"S. Rajput","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S. Rajput","raw_affiliation_strings":["SLB, Mumbai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SLB, Mumbai, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110344104","display_name":"Valdimeire Silvestre Lopes","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"V. S. Lopes","raw_affiliation_strings":["SLB, Mumbai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SLB, Mumbai, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048789760","display_name":"Tety Benedicta Wydiabhakti","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"T. B. Wydiabhakti","raw_affiliation_strings":["SLB, Abu Dhabi, United Arab Emirates"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SLB, Abu Dhabi, United Arab Emirates","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.3199,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.86333928,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.9771000146865845,"subfield":{"id":"https://openalex.org/subfields/2103","display_name":"Fuel Technology"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.9771000146865845,"subfield":{"id":"https://openalex.org/subfields/2103","display_name":"Fuel Technology"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10635","display_name":"Hydraulic Fracturing and Reservoir Analysis","score":0.00559999980032444,"subfield":{"id":"https://openalex.org/subfields/2103","display_name":"Fuel Technology"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10399","display_name":"Hydrocarbon exploration and reservoir analysis","score":0.004800000227987766,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pressure-transient-analysis","display_name":"pressure transient analysis","score":0.9990000128746033},{"id":"https://openalex.org/keywords/triplet-loss","display_name":"triplet loss","score":0.921999990940094},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9380000233650208},{"id":"https://openalex.org/keywords/reservoir-modeling","display_name":"reservoir modeling","score":0.49300000071525574},{"id":"https://openalex.org/keywords/wellbore-model","display_name":"wellbore model","score":0.6600000262260437},{"id":"https://openalex.org/keywords/boundary-model","display_name":"boundary model","score":0.7059999704360962},{"id":"https://openalex.org/keywords/self-supervised-learning","display_name":"self-supervised learning","score":0.8510000109672546},{"id":"https://openalex.org/keywords/synthetic-training-data","display_name":"synthetic training data","score":0.4970000088214874},{"id":"https://openalex.org/keywords/experimental-design","display_name":"experimental design","score":0.6940000057220459},{"id":"https://openalex.org/keywords/permeability","display_name":"permeability","score":0.5329999923706055},{"id":"https://openalex.org/keywords/skin-factor","display_name":"skin factor","score":0.8799999952316284},{"id":"https://openalex.org/keywords/horizontal-wells","display_name":"horizontal wells","score":0.6439999938011169}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6708807945251465},{"id":"https://openalex.org/C2780799671","wikidata":"https://www.wikidata.org/wiki/Q17087362","display_name":"Transient (computer programming)","level":2,"score":0.6546897292137146},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6298413276672363},{"id":"https://openalex.org/C2989121073","wikidata":"https://www.wikidata.org/wiki/Q1309019","display_name":"Transient analysis","level":3,"score":0.5231962203979492},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.404336541891098},{"id":"https://openalex.org/C85761212","wikidata":"https://www.wikidata.org/wiki/Q1974593","display_name":"Transient response","level":2,"score":0.2172054946422577},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17886915802955627},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2118/218836-ms","doi":"https://doi.org/10.2118/218836-ms","is_oa":false,"landing_page_url":"https://doi.org/10.2118/218836-ms","pdf_url":null,"source":{"id":"https://openalex.org/S4306530718","display_name":"SPE Western Regional Meeting","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPE Western Regional Meeting","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1946093182","https://openalex.org/W1967125730","https://openalex.org/W1967946010","https://openalex.org/W1976413973","https://openalex.org/W2000378016","https://openalex.org/W2005736533","https://openalex.org/W2008772556","https://openalex.org/W2012883561","https://openalex.org/W2043797537","https://openalex.org/W2071547780","https://openalex.org/W2075600060","https://openalex.org/W2096733369","https://openalex.org/W2180913742","https://openalex.org/W2470394683","https://openalex.org/W2962803599","https://openalex.org/W3017034739","https://openalex.org/W3020673698","https://openalex.org/W3021134342","https://openalex.org/W3044220684","https://openalex.org/W3091905774","https://openalex.org/W4200410293"],"related_works":["https://openalex.org/W4402079510","https://openalex.org/W2900071276","https://openalex.org/W6996954257","https://openalex.org/W4410932560","https://openalex.org/W4388051417","https://openalex.org/W2000378016","https://openalex.org/W4200410293","https://openalex.org/W4410034767","https://openalex.org/W7081974236","https://openalex.org/W4232353549"],"abstract_inverted_index":{"Abstract":[0],"Over":[1],"its":[2,77],"historical":[3],"trajectory,":[4],"Pressure":[5,104],"Transient":[6,105],"Analysis":[7,106],"(PTA)":[8,107],"has":[9,67],"experienced":[10],"a":[11,111,149,209],"notable":[12],"evolution,":[13],"commencing":[14],"with":[15,327,342,361],"early":[16],"techniques":[17],"like":[18,367],"straight-line":[19],"analysis":[20,66,134,255],"and":[21,56,76,93,168,186,216,240,250,333,372],"type":[22],"curve":[23],"matching.":[24],"The":[25,139,204,227,253,269,348],"mid-20th":[26],"century":[27],"witnessed":[28],"the":[29,35,47,60,69,72,85,89,103,132,159,189,196,223,231,235,238,245,248,258,266,279,290,298,305,309,319,322,338,351],"introduction":[30],"of":[31,37,63,74,91,135,152,198,275,321,330,340,353],"pressure":[32,75,136,201,323],"derivatives,":[33],"enhancing":[34],"sophistication":[36],"reservoir":[38,57],"behavior":[39],"interpretation.":[40],"Deconvolution":[41],"methods":[42],"took":[43],"center":[44],"stage":[45],"in":[46,84,195,277],"late":[48],"20th":[49],"century,":[50],"offering":[51],"intricate":[52],"insights":[53],"into":[54],"wellbore":[55],"responses.":[58,203],"In":[59],"contemporary":[61],"era":[62],"PTA,":[64],"log-log":[65],"become":[68],"norm,":[70],"featuring":[71],"plotting":[73],"derivatives":[78],"on":[79,88,114,285,345],"logarithmic":[80],"scales.":[81],"Recent":[82],"strides":[83],"field":[86],"concentrate":[87],"integration":[90,360],"automation":[92],"machine":[94],"learning":[95],"to":[96,188,233,337],"expedite":[97,318],"PTA":[98],"processes.":[99],"Our":[100],"methodology":[101,349],"enhances":[102],"process":[108],"by":[109],"leveraging":[110],"framework":[112,207],"based":[113],"triplet":[115,205],"loss.":[116],"This":[117,156,315],"architecture":[118],"seamlessly":[119],"integrates":[120],"Convolutional":[121],"Neural":[122],"Network":[123],"(CNN)":[124],"layers,":[125],"providing":[126,278],"robust":[127],"feature":[128],"extraction":[129],"capabilities":[130],"for":[131,313,355],"automated":[133],"transient":[137],"data.":[138],"model":[140,170,174,260,270,281],"is":[141],"trained":[142],"using":[143],"simulated":[144,224],"experimental":[145,254],"data":[146],"generated":[147,221],"through":[148],"systematic":[150],"Design":[151],"Experiments":[153],"(DOE)":[154],"approach.":[155],"involves":[157],"incorporating":[158],"ten":[160],"most":[161,299],"prevalent":[162],"interpretation":[163,292,356],"scenarios,":[164],"encompassing":[165],"well,":[166,331],"reservoir,":[167,332],"boundary":[169,190,334],"types.":[171],"For":[172],"each":[173],"type,":[175],"critical":[176],"parameters":[177],"such":[178],"as":[179],"permeability,":[180],"horizontal":[181],"well":[182,301],"length,":[183],"skin":[184],"factor,":[185],"distance":[187,236,246],"are":[191,219],"systematically":[192],"sampled,":[193],"resulting":[194],"computation":[197],"800":[199],"distinct":[200],"derivative":[202,324],"loss":[206,228],"adopts":[208],"self-supervised":[210],"training":[211,225],"strategy,":[212],"where":[213],"anchor,":[214],"positive":[215,241],"negative":[217,251],"pairs":[218],"dynamically":[220],"from":[222,289,364],"dataset.":[226],"function":[229],"encourages":[230],"network":[232],"reduce":[234],"between":[237,247],"anchor":[239,249],"examples":[242],"while":[243],"increasing":[244],"examples.":[252],"revealed":[256],"that":[257],"actual":[259],"class":[261],"consistently":[262],"ranked":[263],"high":[264],"among":[265],"top":[267,306],"classes.":[268],"exhibits":[271],"an":[272],"accuracy":[273],"rate":[274],"90%":[276],"top-ranked":[280],"recommendations":[282],"when":[283],"evaluated":[284],"100":[286],"samples":[287],"derived":[288],"8":[291],"scenarios.":[293],"Having":[294],"prior":[295],"knowledge":[296],"about":[297],"probable":[300],"test":[302],"models":[303,341,354],"at":[304],"ranks":[307],"diminishes":[308],"manual":[310],"effort":[311],"required":[312],"analysis.":[314],"approach":[316],"can":[317],"identification":[320],"response":[325],"associated":[326],"specific":[328],"combinations":[329],"models,":[335],"leading":[336],"generation":[339],"reduced":[343],"reliance":[344],"user":[346],"interaction.":[347],"streamlines":[350],"recognition":[352],"engineers,":[357],"enabling":[358],"faster":[359],"additional":[362],"information":[363],"diverse":[365],"sources":[366],"geophysics,":[368],"geology,":[369],"petrophysics,":[370],"drilling,":[371],"production":[373],"logging.":[374]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2024-04-25T00:00:00"}