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Our hydraulic fracturing and reservoir simulation solutions apply to a broad range of applications
Following the right modeling workflow is critical for a successful project. Every ResFrac project includes a series of ‘checkpoint’ meetings at key junctures to keep stakeholders aligned and ensure strong engineering design principles.
Model construction and setup
Ingest data, set up an initial model, and present back to confirm everything has been communicated successfully.
Determine ‘key observations’ to be matched from field data. Plan the calibration process in advance. Then, vary parameters to achieve a match.
Align on the design variables to optimize. Perform a quantitative optimization for NPV, investment efficiency, or any other objective.
Design field implementation
Establish baseline performance expectations. Establish performance metrics, and try to minimize uncontrolled variables.
Compare actual production with predicted. What are the ‘key observations’ from the field data? How do they align with expectation? If there is variance, what are potential causes? Are there additional design changes to consider next?
Recent content from the ResFrac blog
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 . 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 ), 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.
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).
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).