scholarly journals Data-space inversion using a recurrent autoencoder for time-series parameterization

2020 ◽  
Author(s):  
Su Jiang ◽  
Louis J. Durlofsky
Keyword(s):  
Time Series ◽  
Data Space ◽  
Space Inversion

scholarly journals Data-Space Inversion With a Recurrent Autoencoder for Naturally Fractured Systems

2021 ◽  
Vol 7 ◽  
Author(s):  
Su Jiang ◽  
Mun-Hong Hui ◽  
Louis J. Durlofsky
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Time Series Data ◽  
Flow Simulation ◽  
Series Data ◽  
Model Parameters ◽  
Fractured Reservoir ◽  
Data Space ◽  
Naturally Fractured ◽  
Space Inversion

Data-space inversion (DSI) is a data assimilation procedure that directly generates posterior flow predictions, for time series of interest, without calibrating model parameters. No forward flow simulation is performed in the data assimilation process. DSI instead uses the prior data generated by performing O(1000) simulations on prior geomodel realizations. Data parameterization is useful in the DSI framework as it enables representation of the correlated time-series data quantities in terms of low-dimensional latent-space variables. In this work, a recently developed parameterization based on a recurrent autoencoder (RAE) is applied with DSI for a real naturally fractured reservoir. The parameterization, involving the use of a recurrent neural network and an autoencoder, is able to capture important correlations in the time-series data. RAE training is accomplished using flow simulation results for 1,350 prior model realizations. An ensemble smoother with multiple data assimilation (ESMDA) is applied to provide posterior DSI data samples. The modeling in this work is much more complex than that considered in previous DSI studies as it includes multiple 3D discrete fracture realizations, three-phase flow, tracer injection and production, and complicated field-management logic leading to frequent well shut-in and reopening. Results for the reconstruction of new simulation data (not seen in training), using both the RAE-based parameterization and a simpler approach based on principal component analysis (PCA) with histogram transformation, are presented. The RAE-based procedure is shown to provide better accuracy for these data reconstructions. Detailed posterior DSI results are then presented for a particular “true” model (which is outside the prior ensemble), and summary results are provided for five additional “true” models that are consistent with the prior ensemble. These results again demonstrate the advantages of DSI with RAE-based parameterization for this challenging fractured reservoir case.

scholarly journals An improved data space inversion method to predict reservoir state fields via observed production data

2021 ◽  
Author(s):  
Deng Liu ◽  
Xiang Rao ◽  
Hui Zhao ◽  
Yun-Feng Xu ◽  
Ru-Xiang Gong
Keyword(s):  
Production Data ◽  
Inversion Method ◽  
Data Space ◽  
Reservoir State ◽  
Space Inversion

scholarly journals Piecewise Trend Approximation: A Ratio-Based Time Series Representation

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Jingpei Dan ◽  
Weiren Shi ◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Representation ◽  
Feature Space ◽  
Original Data ◽  
Series Data ◽  
High Dimensional ◽  
Original Time Series ◽  
Data Space ◽  
Original Time

A time series representation, piecewise trend approximation (PTA), is proposed to improve efficiency of time series data mining in high dimensional large databases. PTA represents time series in concise form while retaining main trends in original time series; the dimensionality of original data is therefore reduced, and the key features are maintained. Different from the representations that based on original data space, PTA transforms original data space into the feature space of ratio between any two consecutive data points in original time series, of which sign and magnitude indicate changing direction and degree of local trend, respectively. Based on the ratio-based feature space, segmentation is performed such that each two conjoint segments have different trends, and then the piecewise segments are approximated by the ratios between the first and last points within the segments. To validate the proposed PTA, it is compared with classical time series representations PAA and APCA on two classical datasets by applying the commonly used K-NN classification algorithm. For ControlChart dataset, PTA outperforms them by 3.55% and 2.33% higher classification accuracy and 8.94% and 7.07% higher for Mixed-BagShapes dataset, respectively. It is indicated that the proposed PTA is effective for high dimensional time series data mining.

scholarly journals History Matching and Production Prediction of Steam Drive Reservoir Based on Data-Space Inversion Method

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
RuXiang Gong ◽  
JingSong Li ◽  
ZiJun Huang ◽  
Fei Wang ◽  
Hao Yang ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Simulation Model ◽  
History Matching ◽  
Production Optimization ◽  
Production Performance ◽  
Inversion Method ◽  
Data Space ◽  
Steam Flooding ◽  
Space Inversion ◽  
Production Prediction

Recently, a data-space inversion (DSI) method has been proposed and successfully applied for the history matching and production optimization for conventional waterflooding reservoir. Under Bayesian framework, DSI can directly and effectively obtain posterior flow predictions without inverting any geological parameters of reservoir model. In this paper, we integrate the numerical simulation model with DSI method for rapid history matching and production prediction for steam flooding reservoir. Based on the finite volume method, a numerical simulation model is established and it is used to provide production data samples for DSI by the simulation of ensemble geological models. DSI-based production prediction model is then established and get trained by the historical data through the random maximum likelihood principle. The posterior production estimation can be obtained fast by training the DSI-based model with history data, but without any posterior geological parameters. The proposed method is applied for history matching and estimating production performance prediction in some numerical examples and a field case, and the results prove its effectiveness and reliability.

scholarly journals Data-space inversion with ensemble smoother

2020 ◽  
Vol 24 (3) ◽  
pp. 1179-1200 ◽  
Author(s):  
Mateus M. Lima ◽  
Alexandre A. Emerick ◽  
Carlos E. P. Ortiz
Keyword(s):  
Data Space ◽  
Ensemble Smoother ◽  
Space Inversion

Large-scale 3D gravity data space inversion in hydrocarbon exploration

2014 ◽  
Author(s):  
P. Marchetti ◽  
F. Coraggio* ◽  
G. Gabbriellini ◽  
S. Ialongo ◽  
M. Fedi
Keyword(s):  
Large Scale ◽  
Gravity Data ◽  
Hydrocarbon Exploration ◽  
Data Space ◽  
3D Gravity ◽  
Space Inversion
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