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Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/F074C258-28B0-4A56-B07B-016883288653 | ||
| Year | 2009 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
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| Bibliographic Citation | P. Hegeman, C. Dong, N. Varotsis and V. Gaganis, “Application of artificial neural networks to downhole fluid analysis”, SPE Reservoir Evaluation & Engineering, vol. 12, no. 1, pp. 8-13, 2009. doi: http://dx.doi.org/10.2118/123423-PA https://doi.org/http://dx.doi.org/10.2118/123423-PA | ||
| Appears in Collections |
Reservoir characterization and asset management require comprehensive information about formation fluids. Obtaining this information at all stages of the exploration and development cycle is essential for field planning and operation. Traditionally, fluid information has been obtained by capturing samples and then measuring the pressure/volume/temperature (PVT) properties in a laboratory. More recently, downhole fluid analysis (DFA) during formation testing has provided real-time fluid information. However, the extreme conditions of the downhole environment limit the DFA-tool measurements to only a small subset of the fluid properties provided by a laboratory. Nevertheless, these tools are valuable in predicting other PVT properties from the measured data. These predictions can be used in real time to optimize the sampling program, to help evaluate completion decisions, and to understand flow-assurance issues. The petroleum industry has devoted much effort to developing computational methods to model phase behavior. Two approaches are prevalent—simple correlations and equation-of-state (EOS) models. However, in recent years, artificial-neural-network (ANN) technology has been applied successfully to many petroleum-engineering problems, including the prediction of PVT behavior. ANN technology can recognize patterns in data, adjust dynamically to changes, infer general rules from specific cases, and accept a large number of input variables. An ANN architecture can allow for continuous improvement by expanding the training database with new data. In this paper, we present the application of ANN technology to DFA. We demonstrate this with an ANN model that uses the DFA-tool measurements of fluid composition as input and produces predictions of gas/oil ratio (GOR), a key PVT property used in real time to monitor a formation-tester sampling job. The ANN also provides an uncertainty estimation of its outputs as a quality-assurance indicator. We compare ANN results with those from the algorithms used by DFA tools.
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