Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Abstract

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability to support reserve-volume estimation, production forecasts, and decision-making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and non-uniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multi-well estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multi-well rock property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multi-well rock property estimation from well logs, we develop discriminative adversarial (DA) and linear-constraint models for well-log normalization and rock-property estimation.The DA neural network model developed for well-log normalization and interpretation can perform both linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. On the other hand, the linear constraint model uses an ensemble of predictions from linear models to constrain both well-log normalization and rock property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the inter-well distance.We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared to the permeability predicted with the classical machine-learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20-50%.

Assessing heterogeneous groundwater systems: Geostatistical interpretation of well logging data for estimating essential hydrogeological parameters

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Stacked ensemble machine learning for porosity and absolute permeability prediction of carbonate rock plugs

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