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Deep Learning and Bayesian Inversion for Planning and Interpretation of Downhole Fluid Sampling


 Downhole fluid sampling is ubiquitous during exploration and appraisal because formation fluid properties have a strong impact on field development decisions. Efficient planning of sampling operations and interpretation of obtained data require a model-based approach. We present a framework for forward and inverse modeling of filtrate contamination cleanup during fluid sampling. The framework consists of a deep learning (DL) proxy forward model coupled with a Markov Chain Monte Carlo (MCMC) approach for the inverse model.
 The DL forward model is trained using precomputed numerical simulations of immiscible filtrate cleanup over a wide range of in situ conditions. The forward model consists of a multilayer neural network with both recurrent and linear layers, where inputs are defined by a combination of reservoir and fluid properties. A model training and selection process is presented, including network depth and layer size impact assessment. The inverse framework consists of an MCMC algorithm that stochastically explores the solution space using the likelihood of the observed data computed as the mismatch between the observations and the model predictions.
 The developed DL forward model achieved up to 50% increased accuracy compared with prior proxy models based on Gaussian process regression. Additionally, the new approach reduced the memory footprint by a factor of ten. The same model architecture and training process proved applicable to multiple sampling probe geometries without compromising performance. These attributes, combined with the speed of the model, enabled its use in real-time inversion applications. Furthermore, the DL forward model is amendable to incremental improvements if new training data becomes available.
 Flowline measurements acquired during cleanup and sampling hold valuable information about formation and fluid properties that may be uncovered through an inversion process. Using measurements of water cut and pressure, the MCMC inverse model achieved 93% less calls to the forward model compared to conventional gradient-based optimization along with comparable quality of history matches. Moreover, by obtaining estimates of the full posterior parameter distributions, the presented model enables more robust uncertainty quantification.