Enhancing the resolution of sparse rock property measurements using machine learning and random field theory

Abstract

The travel time of rock compressional waves is an essential parameter used for estimating important rock properties, such as porosity, permeability, and lithology. Current methods, like wireline logging tests, provide broad measurements but lack finer resolution. Laboratory-based rock core measurements offer higher resolution but are resource-intensive. Conventionally, wireline logging and rock core measurements have been used independently. This study introduces a novel approach that integrates both data sources. The method leverages the detailed features from limited core data to enhance the resolution of wireline logging data. By combining machine learning with random field theory, the method allows for probabilistic predictions in regions with sparse data sampling. In this framework, 12 parameters from wireline tests are used to predict trends in rock core data. The residuals are modeled using random field theory. The outcomes are high-resolution predictions that combine both the predicted trend and the probabilistic realizations of the residual. By utilizing unconditional and conditional random field theories, this method enables unconditional and conditional simulations of the underlying high-resolution rock compressional wave travel time profile and provides uncertainty estimates. This integrated approach optimizes the use of existing core and logging data. Its applicability is confirmed in an oil project in West China.

Keywords

• Wireline logs
• Core characterization
• Compressional wave travel time
• Machine learning
• Random field theory