Machine Learning to Reduce Cycle Time for Time-Lapse Seismic Data Assimilation into Reservoir Management

Xue, Yunfei; Araújo, María; Wang, K.; López, Julio Amador Díaz; Kumar, G. S. Naveen; Brew, Graham

doi:10.3997/2214-4609.201901217

Cited by 2 publications

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“…The authors worked on synthetic data modelled at well locations using time-lapse reservoir properties from the simulation model and seismic attributes such as 4D time shift and 4D amplitude. Xue et al (2019) proposed a data-driven, machine-learning-based, inversion workflow for the estimation of water saturation changes from 4D attribute maps. They used Monte Carlo sampling (no physics based) to generate the training samples related to the reservoir properties.…”

Section: Introductionmentioning

confidence: 99%

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A machine‐learning framework to estimate saturation changes from 4D seismic data using reservoir models

Maleki

Cirne

Schiozer

et al. 2022

Geophysical Prospecting

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Time‐lapse seismic (four‐dimensional seismic) data play a preeminent role in closed‐loop reservoir management by providing a full‐field image of dynamic reservoir behaviour during production. Nonetheless, the multidisciplinary nature of four‐dimensional closed‐loop approaches demands more quantitative and fast methods to integrate rock physics models, reservoir flow simulation models and four‐dimensional seismic analysis. In this work, we tackle this time‐consuming and expensive process and develop a data‐driven quantitative approach to leverage the inherent physics between four‐dimensional seismic and reservoir property changes. We propose an inversion flow method using machine learning strategies to estimate changes in reservoir properties directly from a fast‐derived four‐dimensional seismic attribute. This study was carried out in a Brazilian deep‐water field where production started in 2013 with 3 years of production and injection history. For this reservoir, the estimation of fluid saturation maps is a critical objective to assist engineers with data assimilation procedures. We also generated millions of training data samples using 200 simulation models from the field mentioned above (before history matching) to highlight the benefits of restricting training samples to proper fluid flow consistent combinations. Results demonstrate high prediction accuracy for the targeted reservoir property changes. Additionally, it provides insights for the detection of sweet spots and positioning of infill wells. We significantly simplify the four‐dimensional seismic integration process, allowing initial engagements of reservoir engineers with decision‐making processes and data assimilation applications.

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Section: Introductionmentioning

confidence: 99%

“…Xue et al. (2019) proposed a data‐driven, machine‐learning‐based, inversion workflow for the estimation of water saturation changes from 4D attribute maps. They used Monte Carlo sampling (no physics based) to generate the training samples related to the reservoir properties.…”

Section: Introductionmentioning

confidence: 99%

A machine‐learning framework to estimate saturation changes from 4D seismic data using reservoir models

Maleki

Cirne

Schiozer

et al. 2022

Geophysical Prospecting

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Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets

Côrte

Dramsch

Amini

et al. 2020

Geophysical Prospecting

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In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four‐dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four‐dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics‐based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks.

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Machine Learning to Reduce Cycle Time for Time-Lapse Seismic Data Assimilation into Reservoir Management

Cited by 2 publications

References 0 publications

A machine‐learning framework to estimate saturation changes from 4D seismic data using reservoir models

A machine‐learning framework to estimate saturation changes from 4D seismic data using reservoir models

Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets

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