With efficiency being crucial when every dollar counts, operators in unconventional plays could add microseismic technology to fracture modeling methods to gain insight into permeability advances and better forecast production.
That’s according to Sudhendu Kashikar, vice president of completions evaluation for MicroSeismic Inc.
Understanding drainage volume and improved permeability of stimulated rock are essential to forecasting production, he said. Typically, several models are used to accomplish this, but the approach has its drawbacks.
A single frack model per stage ignores geological variations along the wellbore. Plus, a discrete fracture network (DFN) model is needed to determine how fracturing actually improves the permeability of stimulated rock, Kashikar said.
Microseismic techniques can simplify the workflow and help with production forecasting, Kashikar said during a webcast June 16.
“Technology and procedures were developed to discriminate the microseismic events and fractures described by these events, capturing propped versus unpropped fractures,” Kashikar said while describing Productive-stimulated rock volume (Productive-SRV) technology. “A rock volume capturing the proppant-filled refractures showed much better correlation to the cumulative production than the total stimulated rock volume.”
Productive-SRV technology estimates how much stimulated fracture remains open through proppant placement by using estimated target zone productivity, a DFN, propped fracture estimate and the Fat Fracture drainage estimate, according to MicroSeismic’s website.
Focus is usually on the location of the proppant, but focus should also be on the amount of improved permeability achieved within the SRV or the Productive-SRV, he said.
Understanding and measuring such improvements will lead to the next step in reservoir stimulation and production forecasting, he said.
Using microseismic data has proven beneficial in establishing a deterministic DFN, which shows fractures detected through seismic.
“For every microseismic event we describe a fracture plane. The size is guided by the magnitude, and the orientation comes from the focal mechanism,” he said. “This is much easier to do with surface microseismic.”
The model is calibrated to actual fluid volumes pumped for a well. A mass balance approach is used to fill the fractures with proppant starting from the wellbore moving outward until the proppant is consumed for that stage, Kashikar explained. Once the fracture network and the propped network have been established, a geocellular grid can be superimposed to obtain the SRV and productive SRV to capture the proppant-filled rock volume, he said.
“One advantage of this workflow is the ability to capture fracture intensity—the number of fractures, the orientation of these fractures—to quantify the permeability enhancement achieved,” Kashikar added.
Key steps for the production forecasting workflow are describing three reservoir volumes—the productive SRV (the propped fractures), total SRV (includes propped and unpropped fractures) and the permeability scalar for individual cells within each region to determine how permeability improved for neighboring cells.
This workflow, he said, captures not only the size and shape of the drainage volume but also permeability within the drainage volume.
The process is a big step forward, he said, in understanding and determining the effectiveness of hydraulic fracturing.
“Rather than relying on a single representative fracture model, we can fully and accurately capture the variable fracture geometry and fracture intensity for the entire length of the wellbore, providing a much better production forecast,” Kashikar said. “We can now use the productive stimulated rock volume and the stimulated rock volume with permeability scalars to directly and explicitly describe the reservoir volume in the reservoir simulator.”
Contact the author, Velda Addison, at firstname.lastname@example.org.