Joe Morris

Towards Realtime Hydraulic Fracture Monitoring using Machine Learning and Distributed Fiber Sensing

Joseph Morris, Christopher Sherman, Robert Mellors, Frederick Ryerson, Charles Yu, Michael Messerly

Abstract: Hydraulic fracturing operations (“pumping jobs”) are typically planned well in advance and do not allow for on-the-fly modification of control parameters, such as pumping rate and viscosity enhancement, that can be used to optimize the efficacy of the operation. Monitoring technologies, such as microseismic, have enabled an iterative cycle where observations of one pumping job may influence the selection of parameters of subsequent jobs. However, the significant time lag introduced by data processing and interpretation means that the iterative cycle may take weeks. We seek to enable a future where data collected during a job enables actionable, realtime decision making. Recent advances in distributed acoustic sensor (DAS) technology have produced a source of abundant new data for monitoring processes in the subsurface. Because of the massive dataset size (TB per day), developing a machine learning approach for interpreting DAS data is essential for effective use, such as in operational situations, which require near-realtime results. In our work, we use the massively parallel multi-physics code GEOS to generate a catalog of synthetic DAS measurements that are typical of those recorded during the stimulation of a hydraulic fracture. We then relate physical observables in the model such as the extents of the generated fractures, fluid flow, and interactions with pre-existing rock fractures to the DAS. These data quantify the potential of DAS measurements for revealing subsurface processes in realtime. Determining how best to construct and train a neural network is challenging. We will present our specific approach to building a deep neural network, including the nature of the training data and subsequent success of the network in identifying features. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344.