Loggigng Reservoir parameter prediction dataset

Citation Author(s):
Huazhong
Yang
Submitted by:
Zhong Yang
Last updated:
Mon, 10/14/2024 - 03:19
DOI:
10.21227/drsd-4z63
License:
Creative Commons Attribution
77 Views
Categories:
Artificial Intelligence
Digital signal processing
Machine Learning
Sensors
Energy
Keywords:
Fourier KAN network, Multi-task prediction, Reservoir parameters prediction, SHAP analysis, transformer
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Abstract 

Well logs are interpreted/processed to estimate the in-situ reservoir properties (petrophysical, geomechanical, and geochemical), which is essential for reservoir modeling, reserve estimation, and production forecasting. The modeling is often based on multi-mineral physics or empirical formulae. When sufficient amount of training data is available, machine learning solution provides an alternative approach to estimate those reservoir properties based on well log data and is usually with less turn-around time and human involvements.

Instructions: 

The goal of this contest is to develop data-driven models to estimate reservoir properties including shale volume, porosity, and fluid saturation, based on a common set of well logs including gamma ray, bulk density, neutron porosity, resistivity, and sonic.

You will be provided with log data from about 10 wells from the same field together with the corresponding reservoir properties estimated by petrophysicists. You need to build a data-driven model using the provided training dataset. Following that, you will deploy the newly developed data-driven models on the test dataset to predict the reservoir properties based on the well log data.

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