Proceedings ArticleDOI
Artificial Intelligence (AI) Assisted History Matching
TLDR
The pattern recognition capabilities of Artificial Intelligence and Data Mining are used to develop Surrogate Reservoir Model (SRM) for utilization as the engine to drive the history matching process.Abstract:
History matching is the process of adjusting uncertain reservoir parameters until an acceptable match with the measured production data is obtained. Complexity and insufficient knowledge of reservoir characteristics makes this process timeconsuming with high computational cost. In the recent years, many efforts mainly referred as assisted history matching have attempted to make this process faster; nevertheless, the degree of success of these techniques continues to be a subject for debate. This study aims to examine the application of a unique pattern recognition technology to improve the time and efforts required for completing a successful history matching project. The pattern recognition capabilities of Artificial Intelligence and Data Mining (AI&DM) are used to develop Surrogate Reservoir Model (SRM) for utilization as the engine to drive the history matching process. SRM is an intelligent prototype of the full-field reservoir simulation model that runs in fractions of a second. SRM is built using a handful of geological realizations. In this study, a synthetic reservoir model of a heterogeneous oilfield with 24 production wells and 30 years of production history was used as the ground truth (the subject and the goal of the history match). An SRM was created to accurately represent this reservoir model. The history matching process for this field was performed using the SRM and by tuning static data (Permeability). The result of this study demonstrates the capabilities of SRM for fast track and accurate reproduction of the numerical model results. Speed and accuracy make SRM a fast and effective tool for assisted history matching.read more
Citations
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Applications of Artificial Intelligence in Oil and Gas Development
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Real Time Determination of Rheological Properties of Spud Drilling Fluids Using a Hybrid Artificial Intelligence Technique
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Application of hybrid support vector regression artificial bee colony for prediction of MMP in CO2-EOR process
TL;DR: A global model to predict MMP in both pure and impure CO2-crude oil in EOR process by combining support vector regression (SVR) with artificial bee colony (ABC), which shows that SVR-ABC MMP model yields excellent results with a low mean absolute percentage error and root mean square error.
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Optimization of WAG Process Using Dynamic Proxy, Genetic Algorithm and Ant Colony Optimization
TL;DR: The results show that the established proxy is found to be robust and an efficient alternative for mimicking the numerical simulator performances in the optimization of the WAG.
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