Proppant distribution between perforation clusters

his blog post summarizes the model for calculating proppant distribution between perforation clusters. A very detailed description of the model and literature review are available in [1]. The purpose here is to outline the model and its main features, to demonstrate the comparison with some of the available data (more comparisons in [1]), as well as to discuss limiting cases and sensitivities to various parameters. This blog post is solely focused on presenting the mathematical model. In future work, the results will be applied to practical optimization decisions.

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Figure 5: Example simulation with ‘submesh fractal D’ set to 0.6.

Simulating ‘Fractal Fracture Swarms’ in a General-Purpose Reservoir Simulator

This blog post describes a new capability in ResFrac to capture the effect of ‘fracture swarms’ on production decline trends. Based on work from Acuna (2020), the idea is that variable spacing between fractures causes a gradual onset of production interference. Fractures in a swarm may be numerous and tightly spaced,  so rather than representing each individual crack in the model, we treat each swarm as a single crack and use a numerical technique to capture their effects. In ResFrac, this capability is useful because it provides another mechanism for explaining (and matching) production drawdown trends. For further details, refer to Section 19.10 from McClure et al. (2022).

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How to Diagnose False Radial Flow in a Diagnostic Fracture Injection Test (DFIT)

Genuine radial flow is rare in shale DFITs. If it does occur, it is typically observed in tests with very low injection volume (less than 10-20 bbl), unusually long shut-in (longer than one week), and relatively high permeability (greater than one microdarcy). Genuine radial flow should only be diagnosed if it occurs after an extended (at least one log cycle) period of after-closure linear flow. If ‘false radial’ flow is misdiagnosed and used to estimate permeability, it leads to a large overestimate (10-100x).

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Automated history matching to fracture geometries as measured by Volume to First Response (VFR): A Tutorial

In this post, I will walk through a simple example of using SWPM to calibrate the fracture geometries of a hypothetical data set leveraging the ResFrac Automated History Matching functionality to expedite the workflow. In a follow-on post, I use the model to demonstrate some intuitions on fracture geometry using the Sensitivity Analysis functionality as well as some nuances of VFR calibration.

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simulation with five wells in a hypothetical formation with two pay zones. In the base simulation, all the wells are landed in the upper pay zone. However, the algorithm is given the option to vary the landing depth of the second and fourth wells. The figures below show the ‘baseline’ simulation.

ResFrac’s Automated Economic Optimization Tool

ResFrac’s automated optimization tool allows you to quickly and easily identify the economically best well spacing and frac design. This blog post steps through a simple demo of our built-in economics engine that is similar to those used by commercial software in the industry. It accounts for details such as working interest, different types of taxes, time-varying operations cost, etc.

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Understanding fracture morphology

Field scale hydraulic fracture simulations reveal a variety of complex fracture geometries. Very often stress interaction between the fractures leads to very asymmetric fracture growth within a stage. At the same time, for some other cases, all the fractures are more regularly shaped and symmetric. This blog post presents results of numerical simulations and analysis demonstrating how fracture morphology changes versus problem parameters for some fundamental cases. The results can be used to better understand the observed fracture complexity in a field scale simulation or as a guideline to achieve the desired fracture morphology.

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