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Time-lapse seismic data inversion for estimating reservoir parameters using deep learning

Journal Article · · Interpretation
 [1];  [2];  [3];  [3]
  1. The University of Texas at Austin, Bureau of Economic Geology, John A. and Katherine G. Jackson School of Geosciences, University Station, Box X, Austin, Texas 78713-8924, USA. (corresponding author)
  2. China University of Geosciences, Wuhan 430074, China.
  3. The University of Texas at Austin, Bureau of Economic Geology, John A. and Katherine G. Jackson School of Geosciences, University Station, Box X, Austin, Texas 78713-8924, USA.
+ Show Author Affiliations

Geologic carbon sequestration involves the injection of captured carbon dioxide ([Formula: see text]) into subsurface formations for long-term storage. The movement and fate of the injected [Formula: see text] plume is of great concern to regulators because monitoring helps to identify potential leakage zones and determines the possibility of safe long-term storage. To address this concern, we design a deep-learning framework for [Formula: see text] saturation monitoring to determine the geologic controls on the storage of the injected [Formula: see text]. We use different combinations of porosities and permeabilities for a given reservoir to generate saturation and velocity models. We train the deep-learning model with a few time-lapse seismic images and their corresponding changes in saturation values for a particular [Formula: see text] injection site. The deep-learning model learns the mapping from the change in the time-lapse seismic response to the change in [Formula: see text] saturation during the training phase. We then apply the trained model to data sets comprising different time-lapse seismic image slices (corresponding to different time instances) generated using different porosity and permeability distributions that are not part of the training to estimate the [Formula: see text] saturation values along with the plume extent. Our algorithm provides a deep-learning assisted framework for the direct estimation of [Formula: see text] saturation values and plume migration in heterogeneous formations using the time-lapse seismic data. Our method improves the efficiency of time-lapse inversion by streamlining the large number of intermediate steps in the conventional time-lapse inversion workflow. This method also helps to incorporate the geologic uncertainty for a given reservoir by accounting for the statistical distribution of porosity and permeability during the training phase. Tests on different examples verify the effectiveness of our approach.

Research Organization:
Pennsylvania State Univ., University Park, PA (United States)
Sponsoring Organization:
USDOE Office of Fossil Energy (FE)
DOE Contract Number:
FE0031544
OSTI ID:
1980959
Journal Information:
Interpretation, Vol. 10, Issue 1; ISSN 2324-8858
Publisher:
Society of Exploration Geophysicists
Country of Publication:
United States
Language:
English

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