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    Methods for Petroleum Well Optimization

    Methods for Petroleum Well Optimization

    - Automation and Data Solutions

    Af
    Bog E-bog
    • Format
    • Bog, paperback
    • Engelsk

    Beskrivelse

    Drilling and production wells are becoming more digitalized as oil and gas companies continue to implement machine learning and big data solutions to save money on projects while reducing energy and emissions. Up to now there has not been one cohesive resource that bridges the gap between theory and application, showing how to go from computer modeling to practical use. Methods for Petroleum Well Optimization: Automation and Data Solutions gives today’s engineers and researchers real-time data solutions specific to drilling and production assets. Structured for training, this reference covers key concepts and detailed approaches from mathematical to real-time data solutions through technological advances. Topics include digital well planning and construction, moving teams into Onshore Collaboration Centers, operations with the best machine learning (ML) and metaheuristic algorithms, complex trajectories for wellbore stability, real-time predictive analytics by data mining, optimum decision-making, and case-based reasoning. Supported by practical case studies, and with references including links to open-source code and fit-for-use MATLAB, R, Julia, Python and other standard programming languages, Methods for Petroleum Well Optimization delivers a critical training guide for researchers and oil and gas engineers to take scientifically based approaches to solving real field problems.

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    Detaljer
    • SprogEngelsk
    • Sidetal552
    • Udgivelsesdato22-09-2021
    • ISBN139780323902311
    • Forlag Gulf Publishing Company
    • FormatPaperback
    Størrelse og vægt
  • Vægt1130 g
    • coffee cup img
    10 cm
    book img
    19,1 cm
    23,5 cm

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    Classification Data mining Uncertainty Monte-Carlo-Simulation Knowledge discovery in databases Prediction Clustering Machine learning algorithms Adaptive neurofuzzy inference system (ANFIS)Data piping in real time AHP (analytic hierarchy process)Candidate well selection Analyzing trends in MSE Anti-collision BHA configuration Case seat selection criteria Automatic drilling systems Bhattacharyya coefficient Carrying Capacity Index Bit balling Detecting a kick Digitaltwin technology drilling efficiency Drillability Drilling optimization Catenary well path Case-based reasoning (CBR)Digital well construction Casing depth selection Drill string vibrations Friction Modeling Drilling energy Cuttings transport and settling Hole cleaning optimization Hierarchical clustering tree Drilling automation Drilling ROP optimization workflow Layer selection for acidizing and hydraulic fracturing Minimum mechanical specific energy Managed pressure drilling technology Life cycle well integrity model (LCWIM)Ontology engineering Onshorecollaboration center New concept for drilling hydraulics hole cleaning Optimum interval of SMWW hydraulic optimization Optimal well trajectory Real-time well monitoring rate of penetration Probability box (P-box)Safe mud relief well Predrill pressure prediction Real-time drilling Real-time assessment of the hole cleaning efficiency Statistical and data-driven ROP model Rop Ultralong-reach well well placement problem Weight window Wellbore profile energy Well path optimization framework Johancsik model Wellbore friction optimization Preventing wellbore instability Mechanical real-time models in drilling Mechanical specific energy (MSE)Pore pressure predictions Reduced number of casings ROP optimization Trajectories for a long-reach well Support vector regression (SVR) model Well construction Remote operation center well design Well path optimization Well cost estimation Wellbore stability optimization
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