Vikara, Derek
(2021)
IMPROVING PRODUCTION STRATEGIES IN UNCONVENTIONAL OIL AND GAS RESERVOIRS THROUGH MACHINE LEARNING.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
This research involves the application of supervised, unsupervised, and deep learning ML modeling approaches using empirically-derived well completion, production, and geologic datasets from prominent unconventional O&G plays in the U.S. The anticipated outcome of this work is to provide substantial contribution to the knowledgebase pertinent to O&G field development and reservoir management approaches (transferable to other subsurface applications) founded in data-driven strategies. ML-based models built through this work complete a multitude of tasks, including: 1) Evaluating potential well production response to various hydraulic fracturing completion designs using a gradient boosting ML algorithm; 2) hierarchical ranking of well design and geologic reservoir quality parameters and their associated interactions on production response by assessing parametric importance and partial dependence; 3) deriving well design strategies that maximize production given well placement through optimization; 4) development of time series-based predictive forecasting capability using long-short term memory neural networks that can generalize temporal or sequence-based tendencies in water and associated gas production trends; and, 5) to enable rapid identification of stratigraphic units within a basin using multiclass classification given total vertical depth and spatial positioning.
The findings from this work show that ML provides fast, accurate, and cost-effective analytical approaches to a variety of O&G-related functions. These strategies can be used to analyze disparate datasets in innovative ways, provide utility in generating new insights, and may be used in ways to identify improvements over industry benchmarks. They offer robust approaches that can supplement existing reservoir management best-practices and improve the return on investment from field data acquisition.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
16 January 2021 |
| Defense Date: |
24 March 2021 |
| Approval Date: |
13 June 2021 |
| Submission Date: |
10 March 2021 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
322 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Civil and Environmental Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Machine learning, gradient boosting, long-short term memory, random forest, oil and gas, neural networks |
| Additional Information: |
This is a draft version, submitted early for formatting insight. |
| Date Deposited: |
13 Jun 2021 18:25 |
| Last Modified: |
13 Jun 2022 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/40340 |
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