Data driven modeling (DDM) techniques implement machine learning (ML) to analyze data and discover connections without explicit knowledge of the physical behavior. Recent improvements in technology and computational power have increased interest in the application of DDM in petroleum industry. Recovery process evaluation using numerical reservoir simulators are always costly, time consuming, computational intensive with many assumptions and uncertainty involved. In this thesis, DDM have been adopted as an alternative tool to predict production performance under waterflooding which is one of the most important techniques for improving oil recovery.
A synthetic waterflooding dataset including production profile, operational parameters, reservoir properties and well locations is constructed using the numerical reservoir simulator. Exploratory data analysis provides several insights into the non-intuitive factors in building the reservoir model. K-means clustering analysis is performed to identify internal groupings among producers. Artificial neural network (ANN) and support vector machine (SVM), particularly support vector regression (SVR), are used to decipher the nonlinear relationships between input attributes and waterflooding production. The trained models are subsequently used to predict cumulative oil and watercut on the unseen samples.
Clustering analysis reveal that distance to the free water level has a dominant effect. The cluster that has the smallest average distance to FWL tends to have the highest watercut and lowest cumulative oil compared with the simulation results. Clustering results also indicates that the clustering assignment is controlled by the interplay among input attributes characterizing reservoir properties and relative well locations.
Good agreements between predicted outputs from models and simulation targets present the satisfactory generalization performance and predictive capabilities of ANN and SVR methods. ANN model with one output provides the most accurate prediction result on the test data. ANN model with two outputs reveals the robustness of this approach. SVR models provide similar but slightly worse forecast than ANN models. No previous work studied on the application of SVM on waterflooding performance prediction. Results in this study verify its acceptability and applicability. Proposed methodologies in this thesis study can be utilized as a surrogate or complementary model to analyze and predict recovery process in other reservoirs fast and efficiently.
| Identifer | oai:union.ndltd.org:LSU/oai:etd.lsu.edu:etd-07092017-164637 |
| Date | 25 July 2017 |
| Creators | Liao, Xuan |
| Contributors | Hughes, Richard, Williams, Wesley C., Tyagi, Mayank |
| Publisher | LSU |
| Source Sets | Louisiana State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lsu.edu/docs/available/etd-07092017-164637/ |
| Rights | restricted, I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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