Hydraulic fracturing (HF) is a very complex engineering process. It involves rock and fluid mechanics, mixed mode rock failure and transportation of individual proppant particles. This process is multiscale both in time and spatial domains that is why it is almost impossible to create a fully coupled 3D model with a detailed description of physical and chemical processes even with significant assumptions.

Recent data indicates well completion becomes more expensive than the drilling itself for several unconventional reservoirs. The reason is an increase of fluid and proppant pumped at high rates. Thus, the critical importance of engineering optimization of fracture spacing and individual pumping schedule for maximization of Net Present Value (NPV), Estimated Ultimate Recovery (EUR), and other metrics in the "lower for longer" price environment.

Unfortunately, unconventional operators see little value in fracture modeling because of its complexity and amount of data required for any reasonable predictive power. That is why many companies consider geometric design as the cheapest and hence the most efficient option thus resulting in trial and error to optimize HF design.

In this paper we use publicly available well logs and completion data covering the Midland Basin from the University Lands website. We also demonstrate that HF model calibration with microseismic data and stochastically generated Discrete Fracture Network (DFN), although is a challenging task, may improve our understanding of fracture design pitfalls and to become an essential step for optimum engineering design. Insights from our work can be useful to increase the predictive power of in-house models and reduce total cost and effort involved in this complex modeling.

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