Simulating Oil and Water Production in Reservoirs with Generative Deep Learning

Abstract

Summary This study investigated the ability to produce accurate multiphase flow profiles simulating the response of producing reservoirs, using generative deep learning (GDL) methods. Historical production data from numerical simulators were used to train a variational autoencoder (VAE) algorithm that was then used to predict the output of new wells in unseen locations. This work describes a procedure in which data analysis techniques can be applied to existing historical production profiles to gain insight into field-level reservoir flow behavior. The procedure includes clustering, dimensionality reduction, correlation, in addition to novel interpretation methodologies that synthesize the results from reservoir simulation output, characterizing flow conditions. The insight was then used to build and select samples to train a VAE algorithm that reproduces the multiphase reservoir behavior for unseen operational conditions with high accuracy. Furthermore, using deep feature space interpolation, the trained algorithm can be used to further generate new predictions of the reservoir response under operational conditions for which we do not have previous examples in the training data set. It is found that VAE can be used as a robust multiphase flow simulator. Applying the methodology to the problem of determining multiphase production rate from new producing wells in undrilled locations showed positive results. The methodology was tested successfully in predicting multiphase production under different scenarios including multiwell channelized and heterogeneous reservoirs. Comparison with other shallow supervised algorithms demonstrated improvements realized by the proposed methodology. The study developed a novel methodology to interpret both data and GDL algorithms, geared toward improving reservoir management. The method was able to predict the performance of new wells in previously undrilled locations, potentially without using a reservoir simulator.

A Reservoir Model Characterization with a Bayesian Framework and a Modulus based Physics constrained Neural Operator

“…Furthermore, Wang et al [48] outlined an integrated method that combines predictions of fluid flow with direct flow simulation, significantly reducing computation time without compromising accuracy. Alakeely and Horne [2] examined the effectiveness of generative deep learning methods in predicting multiphase flow profiles of new wells in unseen locations using historical production data and a variational autoencoder algorithm. Dong et al [51] introduced a deep reinforcement learning based approach for automatic curve matching for well test interpretation, utilizing the double deep Q-network.…”

Fast Well Control Optimization with Two-Stage Proxy Modeling

“…Apart from these, a variant of the gradient boosting machine, e.g., extreme gradient boosting machine (XGBoost), was implemented for fast analysis of well placements in a heterogeneous reservoir [4]. The articles [5,6] also discussed the use of some more advanced ML methods in simulating the behavior of reservoirs and production trends, which is an important criterion to be manifested by a proxy model. The potential implementation of ML methods in proxy modeling was also further highlighted in the domain of secondary recovery.…”