When Petrophysics Meets Big Data: What can Machine Do?
- Chicheng Xu (Aramco Services Company: Aramco Research Center) | Siddharth Misra (The University of Oklahoma) | Poorna Srinivasan (Aramco Services Company: Aramco Research Center) | Shouxiang Ma (Saudi Aramco)
- Document ID
- Society of Petroleum Engineers
- SPE Middle East Oil and Gas Show and Conference, 18-21 March, Manama, Bahrain
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- 5.1 Reservoir Characterisation, 3 Production and Well Operations, 7.6 Information Management and Systems, 7 Management and Information, 1.2.3 Rock properties, 1.12 Drilling Measurement, Data Acquisition and Automation, 7.6.6 Artificial Intelligence, 1.12.4 Sensor Technology, 0.2 Wellbore Design, 0.2.2 Geomechanics, 3.3 Well & Reservoir Surveillance and Monitoring, 5 Reservoir Desciption & Dynamics, 7.6.4 Data Mining
- Formation Evaluation, Geological Modeling, Reservoir Characterization, Machine Learning, Petrophysical Data-Driven Analytics
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Petrophysics is a pivotal discipline that bridges engineering and geosciences for reservoir characterization and development. New sensor technologies have enabled real-time streaming of large-volume, multi-scale, and high-dimensional petrophysical data into our databases. Petrophysical data types are extremely diverse, and include numeric curves, arrays, waveforms, images, maps, 3-D volumes, and texts. All data can be indexed with depth (continuous or discrete) or time. Petrophysical data exhibits all the "7V" characteristics of big data, i.e., volume, velocity, variety, variability, veracity, visualization, and value. This paper will give an overview of both theories and applications of machine learning methods as applicable to petrophysical big data analysis.
Recent publications indicate that petrophysical data-driven analytics (PDDA) has been emerging as an active sub-discipline of petrophysics. Field examples from the petrophysics literature will be used to illustrate the advantages of machine learning in the following technical areas: (1) Geological facies classification or petrophysical rock typing; (2) Seismic rock properties or rock physics modeling; (3) Petrophysical/geochemical/geomechanical properties prediction; (3) Fast physical modeling of logging tools; (4) Well and reservoir surveillance; (6) Automated data quality control; (7) Pseudo data generation; and (8) Logging or coring operation guidance.
The paper will also review the major challenges that need to be overcome before the potentially game-changing value of machine learning for petrophysics discipline can be realized. First, a robust theoretical foundation to support the application of machine leaning to petrophysical interpretation should be established; second, the utility of existing machine learning algorithms must be evaluated and tested in different petrophysical tasks with different data scenarios; third, procedures to control the quality of data used in machine leaning algorithms need to be implemented and the associated uncertainties need to be appropriately addressed. The paper will outlook the future opportunities of enabling advanced data analytics to solve challenging oilfield problems in the era of the 4th industrial revolution (IR4.0).
|File Size||1 MB||Number of Pages||25|
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