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Reservoir characterization

Machine-Learning Approach Optimizes  Formation-Pressure Testing in Complex Reservoirs

This paper introduces a machine-learning approach that integrates well-logging data to enhance depth selection, thereby increasing the likelihood of obtaining accurate and valuable formation-pressure results.

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August 1, 2025
Journal of Petroleum Technology
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Fig. 1—Confusion matrix illustrates the ANN model’s performance in predicting valid and invalid test outcomes where darker shades indicate higher classification frequency.
Fig. 1—Confusion matrix illustrates the ANN model’s performance in predicting valid and invalid test outcomes where darker shades indicate higher classification frequency.
Source: SPE 224365.

Traditionally, formation-pressure-test (FPT) depth selection relies heavily on the expertise of engineers and geoscientists in analyzing well-logging data to determine test locations. A manual approach can be inconsistent, time-consuming, and prone to human bias. To address these challenges, this study introduces a machine-learning (ML) framework to enhance FPT depth selection, systematically improving decision-making based on well-log data. The proposed framework aims for consistent and reliable test placement, minimizing invalid tests, enhancing safety, and reducing operational costs.

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