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Year 2024
 
Vol 65, Issue 5, [10 - 2024]

A review of research on reservoir porosity prediction by machine learning based on real-time drilling data

DOI:10.46326/JMES.2024.65(5).02

  • Affiliations:

    Hanoi University of Mining and Geology, Hanoi, Vietnam

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  • Received: 21st-May-2024
  • Revised: 18th-Aug-2024
  • Accepted: 23rd-Aug-2024
  • Online: 1st-Oct-2024
Pages: 10 - 20
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Abstract:

To exploit oil effectively, it is necessary to determine the reservoir parameters of the formation. One of the important parameters that needs to be determined is porosity. Porosity prediction helps evaluate the reservoir's production performance, select the location of the production well, design enhanced oil recovery and evaluate the reservoir's economic feasibility. Normally, the value of porosity is determined directly by various laboratory core sample tests or indirectly based on the results of interpreting well geophysical measurement documents, and well logs. These traditional identification methods are often time-consuming and expensive. Laboratory testing methods are highly accurate but often require available core samples, require a lot of auxiliary measuring equipment, and sometimes require additional core sample measurement results, which consumes time and sampling costs. Moreover, well logging measurements are not always performed in all production wells. By applying different machine learning techniques such as Artificial Neural Network, Decision Tree, Random Forest, etc., the porosity value is also predicted. These techniques often use input parameters as data from well log curves or drilling data. However, using well geophysical measurement curve data as input parameters for machine learning models often faces limitations from the availability of data sources. Meanwhile, drilling parameters such as rate of penetration, weight on bit, drillstring or drillpipe rotation speed measured in revolutions/minute, torque, pumping rate of the circulation of the drilling fluid measured in gallons/minute, and the resulting standpipe pressure are continuously collected from measured while drilling sensors. The article focuses on evaluating and analyzing scientific works that have been researched on the application of machine learning techniques to predict formation porosity values, based on real-time drilling data.

How to Cite
Le, D.Quang 2024. A review of research on reservoir porosity prediction by machine learning based on real-time drilling data (in Vietnamese). Journal of Mining and Earth Sciences. 65, 5 (Oct, 2024), 10-20. DOI:https://doi.org/10.46326/JMES.2024.65(5).02.
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