Geomechanics and Data Analytics: Bridging between ISIP and Geology

February 6, 2019 admin No comments exist

ISIP values are favoured by many in the industry because they are accessible and relatively easy to measure. Nevertheless, there are still questions on the nature of ISIP and its relation with geological and operational parameters. What is briefly shared here includes some short excerpts on ISIP characterization from some comprehensive regional studies on unconventional plays, These Canadian Discovery Ltd. studies implement Hybrid Data-Physics Analytics techniques for field characterization and fracturing and production optimization.

What is ISIP?

During hydraulic fracturing operations, as soon as the pumps are off, a sudden drop happens in the pressure curve (E to F in the graph below) and pressure falls to a value called Instantaneous Shut-in Pressure (ISIP). This drop occurs because the pressure caused by flow turbulence and friction during injection almost instantly disappears (see this article to learn more).

What is the geomechanical meaning of ISIP?

With no influence from the dynamic flow, the mechanical characters of the rock and fracture are probably less masked in ISIP compared to the the recorded pressures during pumping. This is one of the reasons that ISIP has gained so much popularity in the industry. Some may argue that ISIP is the ‘real’ fracture propagation pressure (FPP) as it does not include the dynamic effects of the fluid flow. There are also some who assume that ISIP is same as closure pressure or minimum stress which hardly makes any sense. There are also others who try to find relations between ISIP and minimum stress and/or pore pressure which can make sense. For instance, the figure below shows the variation of ISIP and pore pressure gradients for numerous wells in a play.

Can we implement such relations and develop models that use ISIP for finding stress or pressure? The answer could be yes if we can find out what other factors can also influence ISIP and find the proper algorithm to correlate these parameters.

Does ISIP have a significant geological character?

Maybe one way to answer this question is finding out if there is a pattern that connects ISIP to geographical parameters of a play (e.g., well location) or even more specific geological parameters. As an example, the map below shows the variation of mean ISIP gradient of numerous wells throughout a play and demonstrates an apparent geological pattern.

What are the parameters that influence ISIP the most?

Despite its significant influence, geology is not the only parameter affecting ISIP and there are plenty of other operational factors such as well orientation, completion method, fracturing technology, fracturing fluid, rate of injection, proppant volume, fracture spacing, etc. that may also influence ISIP.

Can we define models to relate ISIP to geology and operations?

In other words, can we implement machine learning algorithms to find meaningful models that can predict ISIP? Let’s take a look at the results of an advanced algorithm that predicts ISIP from a number of geological and operational parameters for a random test sample of the data for numerous wells:

As the graph tells, despite all the possible variations and errors that we can imagine, ISIP can be predicted with a relatively good accuracy if we use the right geological and completion/fracturing parameters and proper modeling algorithms.

In our studies, geological character and predictability property of ISIP are used for field characterization, fracturing/production optimization and seismicity risk mitigation.

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