Title: A learning-based data-driven forecast approach for predicting future reservoir performance

Abstract

Quantification of the predictive uncertainty of subsurface models has long been investigated. The traditional workflow is to calibrate prior models to match observed data, and then use the posterior models to simulate future system performance. Not only are these procedures computationally expensive, but they also have issues in maintaining geological model constraints during the calibration step. Data space inversion (DSI) was introduced recently to predict future system performance without the iterative history matching or model calibration step. In general, DSI approaches seek to establish a statistical relationship between the observed and forecast variables, as well as to quantify the predictive uncertainty of the forecast variables, by using an ensemble of uncalibrated prior models. Existing DSI approaches all require a number of complex transformation and mapping operations, which may deter their widespread use. Here in this study, we introduce a new and simpler DSI approach, the learning-based, data-driven forecast approach (LDFA), by combining dimension reduction and machine learning techniques to quickly provide accurate forecast results and reliably quantify corresponding uncertainty in the results. Our LDFA framework is demonstrated using two supervised learning algorithms, artificial neural network (ANN) and support vector regression (SVR), on two representative examples from reservoir engineering and geologicalmore » carbon storage. Results suggest that our approach provides accurate forecast results (e.g., future oil production rate or cumulative injected CO₂) and reasonable predictive uncertainty intervals. Our framework is generic and may be applied to other surface and subsurface problems.« less

Authors:

Jeong, Hoonyoung ^[1]; Sun, Alexander Y.  ^[2];

• Search DOE PAGES for author "Sun, Alexander Y."
• Search DOE PAGES for ORCID "0000-0002-6365-8526"

Lee, Jonghyun ^[3]; Min, Baehyun ^[4]

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+ Show Author Affiliations

1. Seoul National University (Korea, Republic of)
2. University of Texas, Austin, TX (United States)
3. University of Hawaii at Manoa, Honolulu, HI (United States)
4. Ewha Womans University, Seoul (Korea, Republic of)

Publication Date:

Thu May 31 00:00:00 EDT 2018

Research Org.:

Univ. of Texas, Austin, TX (United States)

Sponsoring Org.:

USDOE Office of Fossil Energy (FE); Seoul National University; Army Research Laboratory; National Science Foundation (NSF); USDOE

OSTI Identifier:

1537925

Alternate Identifier(s):

OSTI ID: 1693797

Grant/Contract Number:  

FE0026515; W911NF-07-2-0027; OIA-1557349

Resource Type:

Accepted Manuscript

Journal Name:

Advances in Water Resources

Additional Journal Information:

Journal Volume: 118; Journal Issue: C; Journal ID: ISSN 0309-1708

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

42 ENGINEERING; machine learning; artificial neural network; support vector regression; data space inversion; future reservoir performance; data-driven forecast