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Deep learning-aided image-oriented history matching of geophysical data

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Abstract

Various types of geophysical measurements have been made available to illuminate different characteristics of subsurface reservoir formations. It becomes crucial to both efficiently and effectively utilize the information contained in these measurements for enhanced reservoir characterization and more informative decision-making support. To address the problem, image-oriented history matching approaches were proposed, which have shown promising results in efficiently assimilating multiple types of geophysical measurements. Relying on the interpretability of geophysical measurements, image-oriented strategies seek to match the extracted features or patterns (e.g., water front positions from the inverted saturation field) instead of the original data in the model update step. Consequently, the quality of the extracted features directly impacts the performance of these methods. However, extracting reliable features from related geophysical data is not straightforward. It requires not only in-depth domain knowledge but also appropriate image processing techniques. This study explores the use of deep learning (DL) based image segmentation techniques as an alternative to assist the feature extraction for an image-oriented ensemble-based history matching workflow. Accounting for the special characteristics of the considered application, a fast unsupervised DL segmentation model based on the convolutional neural network (CNN) is used together with an image denoising algorithm. The developed workflow separates the history matching of geophysical data into two sequential steps. It starts with a (joint) geophysical inversion step for saturation mapping, followed by a featured-oriented assimilation step to match the inverted saturation field, in which water front positions are extracted using the DL-based segmentation model. We test the proposed workflow on a synthetic reservoir model to history match electromagnetic (EM) and seismic data, which illustrates its promising performance in extracting relevant information from the data for efficient history matching.

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Data availability

The data generated or analysed during this study are available from the corresponding authors on reasonable request.

Abbreviations

CNN:

Convolutional neural network

DL:

Deep learning

EM:

Electromagnetic

EnKF:

Ensemble Kalman filter

HM:

History matching

NLM:

Non-local means denoising

SHM:

Seismic history matching

\(\mathbf{c}\) :

Binary map

\({\mathbf{d}}_{obs}\) :

Observed data

\({\mathbf{d}}_{sim }\) :

Simulated data

\({\mathbf{D}}_{obs}\) :

Ensemble of perturbed observations

\(\varDelta \mathbf{D}\) :

Matrix of data misfit

\(f\left(\cdot \right)\)  :

Feature segmentation operator

\(g\left(\cdot \right)\)  :

Forward model mapping model parameters to predicted data

\(h\left(\cdot \right)\)  :

Dissimilarity measure

\(\mathbf{K}\) :

Coefficient matrix similar to the Kalman gain

\(l\) :

Loss function

\(\mathbf{L}\) :

Localization matrix

\(\mathbf{M}\) :

Ensemble of model realizations

\({\text{N}}_{d}\) :

Number of data

\({\text{N}}_{e}\) :

Ensemble size

\({\text{N}}_{m}\) :

Number of model parameters

\({\text{N}}_{s}\) :

Training dataset size

\({\mathbf{r}}^{{\prime }}\) :

Normalized response map

\({\text{s}}_{NLM}\) :

Denoised estimate by NLM

\(std\) :

Standard deviation

\({\mathbf{T}}_{*\to *}\) :

Directed distance maps

\(w\left(\cdot \right)\)  :

NLM weight

\(\mathbf{x}\) :

Input image

\({\lambda }_{\mathcal{l}}\) :

Tuning parameter for the iterative ensemble smoother

\({\upsigma }\) :

Degree of smoothing for NLM

\(\mu\) :

Weighting parameter in CNN loss function

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Acknowledgements

Support of this work was provided by the office of sponsored research at King Abdullah University of Science and Technology and by the research project titled as “AI aided probabilistic reservoir characterization using ensemble history matching and big data” (Grant No. RGC/3/4290-01-01) funded by Saudi Aramco.

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Authors and Affiliations

  1. King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia

    Yanhui Zhang, Tao Zhang & Ibrahim Hoteit

  2. Saudi Aramco, Dhahran, 31311, Saudi Arabia

    Klemens Katterbauer & Abdallah A. AlShehri

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  1. Yanhui Zhang
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Correspondence to Ibrahim Hoteit.

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Zhang, Y., Katterbauer, K., Zhang, T. et al. Deep learning-aided image-oriented history matching of geophysical data. Comput Geosci 27, 591–604 (2023). https://doi.org/10.1007/s10596-023-10227-0

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