Streamline Approach for History Matching Production Data

A Streamline Approach for History-matching Production Data

A Direct Sequential Simulation Approach to Streamline- Based History Matching

Streamlines have proven useful for visualizing and solving complex history-matching problems that enhance reservoir description. This paper presents a novel, multiple-scale streamline-based technique that perturbs the reservoir model in a manner fully consistent with prior geological data. Streamlines define dynamic drainage zones around production wells and the mismatch between historical and ...

SPE 73144 A Geostatistical Approach to Streamline-Based History Matching

History matching large reservoir models under a realistic geological continuity constraint remains an outstanding problem. We propose a new combined geostatistical and streamline-based method potentially capable of history matching large reservoir models, while accounting consistently for geological continuity as provided by a permeability histogram and variogram, and production data. While mos...

a new approach to credibility premium for zero-inflated poisson models for panel data

هدف اصلی از این تحقیق به دست آوردن و مقایسه حق بیمه باورمندی در مدل های شمارشی گزارش نشده برای داده های طولی می باشد. در این تحقیق حق بیمه های پبش گویی بر اساس توابع ضرر مربع خطا و نمایی محاسبه شده و با هم مقایسه می شود. تمایل به گرفتن پاداش و جایزه یکی از دلایل مهم برای گزارش ندادن تصادفات می باشد و افراد برای استفاده از تخفیف اغلب از گزارش تصادفات با هزینه پائین خودداری می کنند، در این تحقیق ...

Distance Dependent Localization Approach in Oil Reservoir History Matching: A Comparative Study

To perform any economic management of a petroleum reservoir in real time, a predictable and/or updateable model of reservoir along with uncertainty estimation ability is required. One relatively recent method is a sequential Monte Carlo implementation of the Kalman filter: the Ensemble Kalman Filter (EnKF). The EnKF not only estimate uncertain parameters but also provide a recursive estimat...