Rapid Forecast Calibration Using Nonlinear Simulation Regression With Localization
The industry increasingly relies on forecasts from reservoir models for reservoir management and decision making. However, because forecasts from reservoir models carry large uncertainties, calibrating them as soon as data come in is crucial.
Multilevel Strategies Improve History Matching of Complex Reservoir Models
The complete paper explores the use of multilevel derivative-free optimization for history matching, with model properties described using principal component analysis (PCA) -based parameterization techniques.
Model Error Estimation Improves Forecasting
The results of the authors’ research showed promising benefits from the use of a systematic procedure of model diagnostics, model improvement, and model-error quantification during data assimilations.
Pattern-Based History Matching Maintains Consistency for Complex-Facies Reservoirs
A challenging problem of automated history-matching work flows is ensuring that, after applying updates to previous models, the resulting history-matched models remain consistent geologically.
Correlation-Based Localization Effective in Ensemble-Based History Matching
To enhance the applicability of localization to various history-matching problems, the authors adopt an adaptive localization scheme that exploits the correlations between model variables and observations.
Uncertainty Quantification for History-Matching Problems
This paper presents a novel approach to generate approximate conditional realizations using the distributed Gauss-Newton (DGN) method together with a multiple local Gaussian approximation technique.
Field-Scale Assisted History Matching Using a Systematic Ensemble Kalman Smoother
This work presents a systematic and rigorous approach of reservoir decomposition combined with the ensemble Kalman smoother to overcome the complexity and computational burden associated with history matching field-scale reservoirs in the Middle East.
Drill and Learn: A Decision-Making Work Flow To Quantify Value of Learning
This paper presents a comparison of existing work flows and introduces a practically driven approach, referred to as “drill and learn,” using elements and concepts from existing work flows to quantify the value of learning (VOL).
Young Technology Showcase—Top-Down Modeling: A Shift in Building Full-Field Models for Mature Fields
Data-driven, or top-down, modeling uses machine learning and data mining to develop reservoir models based on measurements, rather than solutions of governing equations.
A Data-Driven Model for History Matching and Prediction
In this paper, the authors derive and implement an interwell numerical simulation model (INSIM) that can be used as a calculation tool to approximate the performance of a reservoir under waterflooding.
Bayesian-Style History Matching: Another Way To Underestimate Forecast Uncertainty?
This paper critically investigates the impact of using realistic, inaccurate simulation models. In particular, it demonstrates the risk of underestimating uncertainty when conditioning real-life models to large numbers of field data.
Streamline-Based History Matching for Multicomponent Compositional Systems
In this paper, the authors introduce a novel semianalytic approach to compute the sensitivity of the bottomhole pressure (BHP) data with respect to gridblock properties.
History Matching and Forecasting
History matching is only one part of something more comprehensive—reservoir modeling.
Model-Based Evaluation of Surveillance-Program Effectiveness With Proxies
This paper proposes a framework based on proxies and rejection sampling (filtering) to perform multiple history-matching runs with a manageable number of reservoir simulations.
History Matching the Norne Full-Field Model Using an Iterative Ensemble Smoother
This paper describes the application of the iterative ensemble smoother to the history matching of the Norne field in the North Sea.
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04 August 2020