This Springer Brief focuses on the use of artificial intelligence (AI) in geosciences and reservoir engineering. This concise yet comprehensive work explores how AI-driven proxy models can effectively tackle the computational challenges associated with reservoir simulations, history matching, production optimization, and uncertainty analysis.
In reservoir engineering, a key challenge is reproducing observed production and pressure data using forward simulation models, known as reservoir simulators. However, the inverse problem of history matching requires running hundreds of simulations, each demanding significant computational resources. Full-scale reservoir simulators are often too time-consuming, making proxy models--such as second-order polynomials, kriging, and artificial neural networks (ANN)--essential alternatives. This Springer Brief emphasizes the power of AI, particularly ANN, as the most pragmatic approach for addressing real-world reservoir engineering problems. ANN has already gained widespread acceptance in computationally intensive fields such as aerospace, defense, and security due to its ability to model nonlinearities. Given the highly nonlinear nature of reservoir simulations, this book demonstrates how artificial neural networks-based proxies provide efficient and accurate solutions. To illustrate these concepts, the methodology is applied to a synthetic field inspired by real-world data: the Brugge field dataset. This widely used open-source dataset enables practitioners to familiarize themselves with AI-driven workflows in reservoir simulation. The Brief covers key applications, including history matching, production optimization (e.g., well placement and production rates), and uncertainty analysis, with detailed explanations of the workflows for each case. This Brief offers high-quality scientific content aligned with international research standards. It is now available in both print and digital formats.Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This Springer Brief focuses on the use of artificial intelligence (AI) in geosciences and reservoir engineering. This concise yet comprehensive work explores how AI-driven proxy models can effectively tackle the computational challenges associated with reservoir simulations, history matching, production optimization, and uncertainty analysis.In reservoir engineering, a key challenge is reproducing observed production and pressure data using forward simulation models, known as reservoir simulators. However, the inverse problem of history matching requires running hundreds of simulations, each demanding significant computational resources. Full-scale reservoir simulators are often too time-consuming, making proxy models such as second-order polynomials, kriging, and artificial neural networks (ANN) essential alternatives.This Springer Brief emphasizes the power of AI, particularly ANN, as the most pragmatic approach for addressing real-world reservoir engineering problems. ANN has already gained widespread acceptance in computationally intensive fields such as aerospace, defense, and security due to its ability to model nonlinearities. Given the highly nonlinear nature of reservoir simulations, this book demonstrates how artificial neural networks-based proxies provide efficient and accurate solutions.To illustrate these concepts, the methodology is applied to a synthetic field inspired by real-world data: the Brugge field dataset. This widely used open-source dataset enables practitioners to familiarize themselves with AI-driven workflows in reservoir simulation. The Brief covers key applications, including history matching, production optimization (e.g., well placement and production rates), and uncertainty analysis, with detailed explanations of the workflows for each case.This Brief offers high-quality scientific content aligned with international research standards. It is now available in both print and digital formats. 51 pp. Englisch. N° de réf. du vendeur 9783031904462
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This Springer Brief focuses on the use of artificial intelligence (AI) in geosciences and reservoir engineering. This concise yet comprehensive work explores how AI-driven proxy models can effectively tackle the computational challenges associated with reservoir simulations, history matching, production optimization, and uncertainty analysis.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 80 pp. Englisch. N° de réf. du vendeur 9783031904462
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This Springer Brief focuses on the use of artificial intelligence (AI) in geosciences and reservoir engineering. This concise yet comprehensive work explores how AI-driven proxy models can effectively tackle the computational challenges associated with reservoir simulations, history matching, production optimization, and uncertainty analysis.In reservoir engineering, a key challenge is reproducing observed production and pressure data using forward simulation models, known as reservoir simulators. However, the inverse problem of history matching requires running hundreds of simulations, each demanding significant computational resources. Full-scale reservoir simulators are often too time-consuming, making proxy models such as second-order polynomials, kriging, and artificial neural networks (ANN) essential alternatives.This Springer Brief emphasizes the power of AI, particularly ANN, as the most pragmatic approach for addressing real-world reservoir engineering problems. ANN has already gained widespread acceptance in computationally intensive fields such as aerospace, defense, and security due to its ability to model nonlinearities. Given the highly nonlinear nature of reservoir simulations, this book demonstrates how artificial neural networks-based proxies provide efficient and accurate solutions.To illustrate these concepts, the methodology is applied to a synthetic field inspired by real-world data: the Brugge field dataset. This widely used open-source dataset enables practitioners to familiarize themselves with AI-driven workflows in reservoir simulation. The Brief covers key applications, including history matching, production optimization (e.g., well placement and production rates), and uncertainty analysis, with detailed explanations of the workflows for each case.This Brief offers high-quality scientific content aligned with international research standards. It is now available in both print and digital formats. N° de réf. du vendeur 9783031904462
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Taschenbuch. Etat : Neu. Artificial Intelligence Proxy Models: Applications in Geosciences | Dominique Guérillot | Taschenbuch | SpringerBriefs in Applied Sciences and Technology | x | Englisch | 2025 | Springer | EAN 9783031904462 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 133584010
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