Looking up the AI maturity curve in E&P opportunities, challenges and the impact on geoscience work

Eirik Larsen, Stephen Purves, Dimitris Economou, Behzad Alaei
2019 Zenodo  
Machine Learning (ML) has been capable for three decades, to infer lithology, sedimentary facies, porosity, and fluid saturation as functions of wireline logs. Now, ML is moving from R&D projects and into the tool box of the generalist, transforming the subsurface workflow. In addition to being fueled by algorithmic development, data, and high-performance compute; this transformation is enabled by the emergence of data analytics platforms, that facilitate; i) practical use of ML methods by the
more » ... ML methods by the generalist geoscientist, ii) integration of data analytics with structured data in databases, iii) semi-automated data management, quality-control and -improvement, and iv) tracking of data provenance, enabling reproducible scientific workflows. On a regional scale we can now train supervised ML models with well-log data (as features) and data from core, and/or from physics models (as target labels). We can efficiently condition large well data sets in order to enable ML prediction of rock and fluid properties at scale. We can measure prediction accuracies using a cross-validation approach with blind testing against all wells in the dataset. The data-types we can predict includes porosity, permeability, lithology, sedimentary facies, source rock properties, and fluid saturation among others. On a local scale we can train supervised ML models with partial-stack seismic data (features) and rock- and fluid-property data from wells (labels). We can use deep convolutional neural networks to predict rock- and fluid property cubes based on upscaled version of the inferred property logs. Wells within the bounds of 3D surveys can be used for blind cross validation allowing network hyperparameters to be tuned and model performance to be assessed. In order to provide stratigraphic and structural context to the predicted rock and fluid property data we can use automated seismic interpretation techniques to interpret stratigraphic units and faults from seismic [...]
doi:10.5281/zenodo.2578736 fatcat:ubnjy7t27ffmfkcpdumrx5na2e