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Speaker at Petroleum Engineering Conferences - Shruti Malik
Kaunas University of Technology - KTU, Lithuania
Title : Deep learning models for history matching and forecasting reservoir performance

Abstract:

Advances in new technologies particularly artificial intelligence (AI) and machine learning (ML) have now made it possible to use AI and ML based approaches for building reservoir models, which do not rely on conventional simulation tools for history matching and production forecasting. These new methods have the capabilities to eventually replace numerical simulator as a fast history matching and forecasting tool. Objective of this work is to use new AI and ML based deep learning algorithms for performing history matching and forecasting on a series of industrial dataset from middle east and North Sea.  The results show that AL and ML based deep learning algorithm-based models are very good and gives close to 85% accuracy in history matching a few well patterns. In total model is evaluated on 3 different datasets with high accuracy. The models are also extended to generate long term forecast.

Audience Take Away: 

  • Use of machine learning in oil and gas application
  • AI and ML based time series history matching and forecasting of oil and gas production for reservoirs 
  • Ai and ML based forecasting can reduce the time taken for history matching and forecasting from months to days. Computationally fast and accurate approach can reduce reliance on expensive history matching and forecasting tools.

Biography:

Ms. Shruti Malik earned her Ph.D. degree from Indian Institute of Technology, Roorkee, India. In her Ph.D. research work, she performed the rock property estimation on the digital volumes of the rock samples obtained through Micro x-ray CT-scanning technique. Subsequently, she performed measurements on core samples from carbonate reservoirs to map their heterogeneity and determine their static and dynamic properties. Currently, she is working as a post-doctoral researcher in the Department of Mathematical Modelling, Kaunas University of Technology (KTU), Lithuania, supported by a research grant from Research Council of Lithuania (RCL). Her work is focused on blending the digital rock physics with AI & ML techniques to assess the impact of CO2 & H2 storage on the sub-surface reservoirs.

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