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商品描述
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.
商品描述(中文翻譯)
這本 Springer Brief 專注於人工智慧（AI）在地球科學和油藏工程中的應用。這部簡明而全面的著作探討了 AI 驅動的代理模型如何有效應對與油藏模擬、歷史匹配、生產優化和不確定性分析相關的計算挑戰。
在油藏工程中，一個主要挑戰是使用前向模擬模型（稱為油藏模擬器）重現觀察到的生產和壓力數據。然而，歷史匹配的反問題需要運行數百次模擬，每次都需要大量的計算資源。全規模的油藏模擬器通常耗時過長，因此代理模型——如二次多項式、克里金（kriging）和人工神經網絡（ANN）——成為必要的替代方案。
這本 Springer Brief 強調了 AI 的力量，特別是 ANN，作為解決現實世界油藏工程問題的最務實方法。由於 ANN 能夠建模非線性，因此在航空航天、國防和安全等計算密集型領域已獲得廣泛接受。考慮到油藏模擬的高度非線性特性，本書展示了基於人工神經網絡的代理如何提供高效且準確的解決方案。
為了說明這些概念，該方法論應用於一個受真實數據啟發的合成場：布魯赫（Brugge）油田數據集。這個廣泛使用的開源數據集使從業者能夠熟悉 AI 驅動的油藏模擬工作流程。該 Brief 涵蓋了關鍵應用，包括歷史匹配、生產優化（例如，井位佈置和生產率）以及不確定性分析，並對每個案例的工作流程進行詳細解釋。
這本 Brief 提供了與國際研究標準相符的高品質科學內容，現在已經以印刷和數位格式提供。
