By Peter M. Duncan
Founder & Co-Chairman
That all sounds pretty neat, but how do we judge “better”? In the past we have used such things as number of events or the areal or volumetric extent of the microseismic cloud as a measure of success, but of course the real measure of success is “more hydrocarbons for less dollars”. So I believe that we need to be able to predict production from the microseismic data, and we need the ability to predict how changing the treatment parameters will change that production so that we can prescribe the best set of parameters or at least demonstrate the sensitivity of the production to various treatment options.
At MSI we have been working on just such a workflow and have actually gotten to the point where we can estimate production from a model solely derived from the microseismic data. This workflow involves building a discrete fracture network (DFN) model from the microseismic event set, estimating what portion of the DFN gets propp’ed, estimating the permeability of frac’ed system by calibrating the model with historical production. Recently we built such a model from one well of a 4 well set and then successfully predicted the production on the 3 other wells on the same pad. Such a “blind test” is exactly the validation we need to establish the credibility of microseismic data in general and this workflow in particular.
The next step is to be able to predict how changing treatment parameters will change how the rocks respond. A simple way to do this is to calibrate the rock properties of a 2-D frac model with the microseismic data. That is, we adjust the rock properties until the modeled treatment overlays the microseismic cloud. Then we can run the calibrated model with new treatment options and catalogue the results. However, we, and most other people, recognize the shortcomings of the 2-D models for unconventional reservoir rocks. New, more geomechanically sophisticated models of fracturing are starting to be available. These too require some sort of local rock and stress property calibration. We are approaching such a calibration by searching for the range of parameters that causes the more complex model to match what the microseismic monitoring data observed at the treatment well. We can test the validity of the predicted DFN by doing a blind test like the one that validated the production prediction, but this time we will test the predicted DFN with the one actually observed at other wells nearby the calibration well. Once we are satisfied with the calibration, we permute the treatment parameters and catalogue how the stochastic model responds. The DFN model that results is submitted to a reservoir simulator and the production differences for different treatment options can be estimated. It’s early days for this workflow but the initial results are encouraging.
In the meantime we have made a huge step forward on getting a more useful and believable analysis product out of microseismic monitoring, namely an estimate of production, and we have moved a little closer to that vision of a world where every frac is monitored.