FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1990297255> ?p ?o ?g. }

• W1990297255 abstract "Abstract Adding new wells and new production in existing fields under Enhanced Oil Recovery (EOR) is particularly important in mature fields that are characterized by a long history of field activity. Different drilling programs, a variety of field treatments, well conversions and new injectors add many layers of complexity and uncertainty to the existing effects of geological, completion and production factors. Surveillance and prediction of responses caused by injected fluids in fields with dozens of patterns and hundreds of wells calls for computer-based systems that estimate responses based on numerical and statistical solutions. This is especially important when geological understanding is very weak (i.e. no core, no log data). This paper shows how results from EOR surveillance programs can be integrated with geological data. Furthermore, this paper shows how to build predictive models for production estimates based on injection responses and geology. These models support a two to three times more accurate selection of wells with high oil production during EOR than historically implemented selections. Included in the paper are practical tips on how to select the best model and derive solutions with decision trees that are equivalent to sets of English-based rules. Solutions from decision trees are compared with solutions from logistic regression and neural networks. This comparison deals with the statistical accuracy of model predictions, interpretation ability and assisting in applying these models to support field decisions. Introduction The main goal of the study was to develop and test a model that would predict production performance during waterfloods. In predictive modelling, regression is traditionally applied to calculate continuous target variables(1). Models that predict binary response variables use logistic regression(2). A binary target variable is characterized by two events. They can be of a numerical nature (0 and 1) where zero represents a non-event and one represents an event. Alternatively, a character string with two outcomes (e.g. No and Yes) is often applied. In the case of a continuous target variable, we may predict the fluid rates of oil, water, gas or the total fluids. The binary 0 or 1 target variable can represent a low or high production output, respectively. A production cutoff can be based on economical or engineering criteria applied to the actual rates or volumes in a specific time period. This paper presents the principles of a numerical model development for predicting well performance during waterflood in the Pekisko B Field. The production performance predictions were based on geological and injection response parameters. The injection response parameter definition and non-numerical integration with geological data was presented in an earlier paper(3). In this paper, the performance predictions were done for a binary target variable that identified wells with good performance. The performance was based on the actual normalized volume of production. A well's normalized production was defined as a ratio of the well production in a specific time period, to the total field production in the same time period. A binary target variable (indicator) of two levels (0 and 1) was assigned based on the normal-ized production." @default.
• W1990297255 created "2016-06-24" @default.
• W1990297255 creator A5017027755 @default.
• W1990297255 creator A5046052756 @default.
• W1990297255 creator A5056935243 @default.
• W1990297255 date "2007-05-01" @default.
• W1990297255 modified "2023-09-27" @default.
• W1990297255 title "Predicting Waterflood Responses With Decision Trees" @default.
• W1990297255 doi "https://doi.org/10.2118/07-05-02" @default.
• W1990297255 hasPublicationYear "2007" @default.
• W1990297255 type Work @default.
• W1990297255 sameAs 1990297255 @default.
• W1990297255 citedByCount "4" @default.
• W1990297255 countsByYear W19902972552016 @default.
• W1990297255 countsByYear W19902972552018 @default.
• W1990297255 crossrefType "journal-article" @default.
• W1990297255 hasAuthorship W1990297255A5017027755 @default.
• W1990297255 hasAuthorship W1990297255A5046052756 @default.
• W1990297255 hasAuthorship W1990297255A5056935243 @default.
• W1990297255 hasConcept C119857082 @default.
• W1990297255 hasConcept C124101348 @default.
• W1990297255 hasConcept C127313418 @default.
• W1990297255 hasConcept C136197465 @default.
• W1990297255 hasConcept C139719470 @default.
• W1990297255 hasConcept C151730666 @default.
• W1990297255 hasConcept C154945302 @default.
• W1990297255 hasConcept C162324750 @default.
• W1990297255 hasConcept C183250156 @default.
• W1990297255 hasConcept C202444582 @default.
• W1990297255 hasConcept C2776364302 @default.
• W1990297255 hasConcept C2778348673 @default.
• W1990297255 hasConcept C2778668878 @default.
• W1990297255 hasConcept C2779681308 @default.
• W1990297255 hasConcept C2984309096 @default.
• W1990297255 hasConcept C33923547 @default.
• W1990297255 hasConcept C41008148 @default.
• W1990297255 hasConcept C45804977 @default.
• W1990297255 hasConcept C548895740 @default.
• W1990297255 hasConcept C78762247 @default.
• W1990297255 hasConcept C84525736 @default.
• W1990297255 hasConcept C9652623 @default.
• W1990297255 hasConceptScore W1990297255C119857082 @default.
• W1990297255 hasConceptScore W1990297255C124101348 @default.
• W1990297255 hasConceptScore W1990297255C127313418 @default.
• W1990297255 hasConceptScore W1990297255C136197465 @default.
• W1990297255 hasConceptScore W1990297255C139719470 @default.
• W1990297255 hasConceptScore W1990297255C151730666 @default.
• W1990297255 hasConceptScore W1990297255C154945302 @default.
• W1990297255 hasConceptScore W1990297255C162324750 @default.
• W1990297255 hasConceptScore W1990297255C183250156 @default.
• W1990297255 hasConceptScore W1990297255C202444582 @default.
• W1990297255 hasConceptScore W1990297255C2776364302 @default.
• W1990297255 hasConceptScore W1990297255C2778348673 @default.
• W1990297255 hasConceptScore W1990297255C2778668878 @default.
• W1990297255 hasConceptScore W1990297255C2779681308 @default.
• W1990297255 hasConceptScore W1990297255C2984309096 @default.
• W1990297255 hasConceptScore W1990297255C33923547 @default.
• W1990297255 hasConceptScore W1990297255C41008148 @default.
• W1990297255 hasConceptScore W1990297255C45804977 @default.
• W1990297255 hasConceptScore W1990297255C548895740 @default.
• W1990297255 hasConceptScore W1990297255C78762247 @default.
• W1990297255 hasConceptScore W1990297255C84525736 @default.
• W1990297255 hasConceptScore W1990297255C9652623 @default.
• W1990297255 hasIssue "05" @default.
• W1990297255 hasLocation W19902972551 @default.
• W1990297255 hasOpenAccess W1990297255 @default.
• W1990297255 hasPrimaryLocation W19902972551 @default.
• W1990297255 hasRelatedWork W1994048780 @default.
• W1990297255 hasRelatedWork W2045960411 @default.
• W1990297255 hasRelatedWork W2086022025 @default.
• W1990297255 hasRelatedWork W2133430576 @default.
• W1990297255 hasRelatedWork W2793474721 @default.
• W1990297255 hasRelatedWork W2898042188 @default.
• W1990297255 hasRelatedWork W2943150014 @default.
• W1990297255 hasRelatedWork W3047516726 @default.
• W1990297255 hasRelatedWork W4205446653 @default.
• W1990297255 hasRelatedWork W4214908478 @default.
• W1990297255 hasVolume "46" @default.
• W1990297255 isParatext "false" @default.
• W1990297255 isRetracted "false" @default.
• W1990297255 magId "1990297255" @default.
• W1990297255 workType "article" @default.