Automated Microseismic Event Detection and Location by Continuous Spatial Mapping

Abstract

Abstract Optimal control of oil-field hydraulic fracturing operations will benefit from reliable, automated, real-time microseismic monitoring. Existing microseismic processing techniques are often either unreliable, or impractical for real time use. In this paper we present the method of Coalescence Microseismic Mapping (CMMapping) for detection and localization of microseismic events. The technique is both automatic and robust, and explicitly allows for the inclusion of velocity model uncertainty into its formulation. We present the general basis for the method and illustrate its use on data acquired during a hydraulic fracture well stimulation. Introduction One of the clear benefits of being able to generate maps of the fracture network in real time as the stimulation progresses is that the operator can potentially adjust pumping and stimulation parameters, such as pump rate, fluid properties and volumes, to better keep the stimulation within its original design. Current techniques of microseismic event location rely on either fully-automated or interactive, semi-automated picking of discrete seismic arrivals at each of one or more seismic detectors. However, reliable automated arrival time picking remains a challenge and interactive picking of potentially many seismic arrivals is impractical for real-time monitoring. Tarantola and Valette[1] detail forward and inverse problems applied for seismic event location. In their example on hypocenter location, there is a reliance on the picking of discrete seismic arrivals. The inverse problem is then the simultaneous inversion for location and origin time for each discrete seismic event. In this paper we formulate the inverse problem in terms of both the probability of occurrence of a microseismic event and its spatio-temporal coordinates. We have implemented a method to detect and locate events, which does not require the identification and picking of discrete arrivals at each sensor.We do so by continuously updating and applying event detection to a spatial map of the probability of microseismicity occurrence. This map is generated by transforming and then forward mapping the signals from multiple seismic detectors, thereby allowing the joint detection and location of microseismic events, without requiring discrete arrival detection and time picking at each of the detectors individually. The method enhances the detectability of microseismic events and is a robust and fully automated method of microseismic event detection and location. By making use of today's faster computers and incorporating careful formulation and optimization, the method can be applied to generate reliable maps of the fracture network with near zero latency. In Parts 1–3, we discuss the theory, simplifications, and assumptions made in the formulation. In the process, we look to the general theory of Tarantola and Valette to understand how to account for forward model uncertainty in our formulation.[2] We then discuss implementation, and show the comparative result for one implementation, and for the result obtained using a traditional method of microseismic event detection and location, using arrival times picked on individual sensors.