A comparison of machine learning algorithms as surrogate model for net present value prediction from wells arrangement data
| Autor: | João Roberto Bertini Junior, Mei Abe Funcia, Denis José Schiozer, Antonio Alberto de Souza dos Santos |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Boosting (machine learning) business.industry Computer science 02 engineering and technology Machine learning computer.software_genre Perceptron Computational resource Net present value Support vector machine Tree (data structure) 020901 industrial engineering & automation Surrogate model 020401 chemical engineering Linear regression Artificial intelligence 0204 chemical engineering business Algorithm computer |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn.2019.8851708 |
| Popis: | Net Present Value (NPV) measures whether an investment will be profitable within a given period of time. In oil production planning, it consists of an important indicator to evaluate different production strategies. The NPV estimate is calculated on the basis of the production data which are generally obtained by means of numerical simulations, which consider the strategy details and the physical reservoir model. However, the simulator demands high computational resource, which may take hours or days of processing time to evaluate a single strategy, depending on the size of the reservoir model. To speed up this process a simpler model, referred to as a surrogate model, can be used to approximate the simulator output. In this work, we hypothesize that it is possible to predict the NPV using only wells arrangement data as predictors. Moreover, we present a comparison among six machine learning algorithms used as a surrogate model: Linear Regression, K-Nearest Neighbor, Multi-Layer Perceptron, Kernel Ridge Regression, Support Vector Regression, and Gradient Tree Boosting. Results confirm it is viable to predict NPV from wells arrangement data, in special with kernel-based methods. |
| Databáze: | OpenAIRE |
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