Our services

Simulation services to maximize return on investment

Turnkey fracture design and reservoir simulation services

Close collaboration

We work closely with clients to align on priorities and support business objectives. We've refined the process over dozens of projects across North and South America.

Understand the why

We don’t just hand you a solution. We help you understand the approach, think critically about simulation inputs, and explain what is happening in the reservoir and why.

Build your capabilities

We strive to transfer our knowledge so you can apply it to future well designs and projects.

Our hydraulic fracturing and reservoir simulation services apply to a broad range of applications

Mitigate parent-child problems

Reduce production loss when drilling infill wells.

Enhanced oil recovery in shale

Increase recovery factor by 2-3x.

Well spacing optimization

Optimize the distance between wells to maximize capital efficiency.

Landing depth optimization

Maximize reservoir contact and optimize well spacing in 3D.

Fracturing design optimization

Customize frac design to your formation and economic drivers.

Geothermal systems

Simulate hydraulic stimulation and long-term circulation in a single integrated model.

The process

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.

Model calibration

Determine ‘key observations’ to be matched from field data. Plan the calibration process in advance. Then, vary parameters to achieve a match.

Design optimization

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.

Field implementation

Evaluate results

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?

ResFrac simulations helped Liberty design its 'Dune and Cap' high strength proppant strategy, leading to a 20% production uplift.
A ResFrac modeling study helped Recoil Resources modify proppant loading and well spacing, improving ROI.

Recent content from the ResFrac blog

ResFrac Retreat in Colorado

For the first time in more than two years, the entire (almost!) ResFrac team gathered in Denver. We mixed education (thank you to our friends at Liberty Services for showing us the nuts and bolts of frac’ing)…… with fun.

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Commentary on Four New DFIT Papers: (a) Direct In-Situ Measurements of Fracture Opening/Closing from the EGS Collab Project; (b) Comparison of Stress Measurement Techniques from the Bedretto Project; (c) a Statistical Summary of 62 DFITs Interpretations Across Nine Shale Plays; and (d) A Different Perspective: An Article Advocating the Use of the Tangent Method

This post provides commentary on recent four papers on diagnostic fracture injection testing (DFIT). The first paper uses in-situ deformation measurements to directly observe fractures opening and closing during fracture injection-falloff tests (Guglielmi et al., 2022). The second compares various stress measurement techniques in a series of fracture/injection tests from the Bedretto project (Bröker and Ma, 2022). The third statistically reviews results from applying the interpretation procedure from McClure et al. (2019) to 62 DFITs across nine different shale plays (McClure et al., 2022). The fourth is an op-ed written in JPT (Journal of Petroleum Technology) by an advocate of the tangent method for estimating DFIT closure stress (Buijs, 2021; 2022). This article presupposes that the reader already has familiarity with these topics. If you would like more background, please refer to McClure et al. (2019).

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