Deep Neural Network Trains To Pinpoint Microseismic Events in Real Time

Abstract

Real-time analysis of microseismic events using data gathered during hydraulic fracturing can give engineers critical feedback on whether a particular fracturing job has achieved its goal of increasing porosity and permeability and boosting stimulated reservoir volume (SRV). Currently, no perfect way exists to understand clearly if a fracturing operation has had the intended effect. Engineers collect data, but the methods used to gather it, manually sort it, and analyze it provide an inconclusive picture of what really is happening underground. Daniel Stephen Wamriew, a PhD candidate at the Skolkovo Institute of Science and Technology (Skoltech) in Moscow, said he believes this can change with advances in artificial intelligence and machine learning that can enhance accuracy in determining the location of a microseismic event while obtaining stable source mechanism solutions, all in real time. Wamriew presented his research at the 2020 SPE Russian Petroleum Technology Conference in Moscow in October in paper SPE 201925, “Deep Neural Network for Real-Time Location and Moment Tensor Inversion of Borehole Microseismic Events Induced by Hydraulic Fracturing.” The paper’s coauthors included Marwan Charara, Aramco Research Center, and Evgenii Maltsev, Skolkovo Institute of Science and Technology. Skoltech is a private institute established in 2011 as part of a multiyear partnership with the Massachusetts Institute of Technology. “People in the field mainly want to know if they created more fractures and if the fractures are connected,” Wamriew explained in a recent interview with JPT. “So, we need to know where exactly the fractures are, and we need to know the orientation (the source mechanism).” It Starts With Data “Usually, when you do hydraulic fracturing, a lot of data comes in,” Wamriew said. “It is not easy to analyze this data manually because you have to choose what part of the data you deal with, and, in doing that, you might leave out some necessary data that the human eye has missed.” To solve this problem, Wamriew proposes feeding microseismic data gathered during a fracturing job into a convolutional neural network (CNN) that he is constructing (Fig. 1). Humans discard nothing. Wave signals from actual events along with noise of all kinds goes into a machine, and the CNN delivers valuable information to reservoir engineers who want to understand the likely SRV. Companies today can identify the location of microseismic events, even without the help of artificial intelligence—though the techniques are always open to refinement—but analyzing the orientation (and hence their understanding of whether and how the fractures are connected) is a difficult and often expensive task that is usually left undone. “Current source mechanism solutions are largely inconsistent,” Wamriew said. “One scientist collects data and performs the moment tensor inversion, and another does the same and gets different results, even if they both use the same algorithm. When we handle data manually, we choose the process, and, in doing so, we introduce errors at every step because we are truncating, rounding up, and rounding down. We end up with something far from reality.”