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

  • Affiliations:

    Hanoi University of Mining and Geology, Hanoi, Vietnam

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  • Received: 25th-May-2024
  • Revised: 26th-June-2024
  • Accepted: 28th-June-2024
  • Online: 1st-Aug-2024
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Porosity is one of the important parameters of the reservoir formation properties. Porosity prediction helps to evaluate the reservoir's production performance, select location of production well, design enhanced oil recovery and evaluate reservoir 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, well log. These traditional identification methods are often time-consuming and expensive. Laboratory testing methods are highly accurate but often require available core samples and 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... 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, the resulting standpipe pressure are continuously collected from measurement- while-drilling tools. 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 on reservoir porosity prediction by machine learning based on real-time drilling data (in Vietnamese). Journal of Mining and Earth Sciences. 65, 4 (Aug, 2024).

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