C&P: Development of the Brittle Shale Fracture Network Model

Speaker Dr. Amir Nejad
Dr. Amir Nejad received his doctorate degree in Petroleum Engineering from Pennsylvania State University in 2011. His thesis dissertation is focused on development of reservoir characterization workflow for unconventional reservoirs with the help of artificial intelligence expert systems. He worked in the center for smart oilfield technologies (CiSOFT) and learned ...

Dr. Amir Nejad received his doctorate degree in Petroleum Engineering from Pennsylvania State University in 2011. His thesis dissertation is focused on development of reservoir characterization workflow for unconventional reservoirs with the help of artificial intelligence expert systems. He worked in the center for smart oilfield technologies (CiSOFT) and learned the IT aspect of oil exploration and production while attending his Master’s degree in petroleum engineering in University of Southern California.


He joined StrataGen, the independent consulting arm of CARBO Ceramics, in 2012 and since then he is heavily involved in well completion and hydraulic fracture design evaluation using DANA® workflows (Data and Neural Analysis) for multiple operators in shale assets. He is an active member of SPE since 2004 and he is the author of more than 10 technical papers and the technical editor of a technical book (“Mechanics of Fluid Flow” published by Wiley-Scrivener).

Full Description

This paper discusses the workflow for the development of a brittle shale model using a data mining approach.  A database of more than 1,000 fracture stages and associated microseismic mapping results in the Barnett Shale was assembled. The fracture database is comprised of fracture design parameters including treatment volumes, rates, proppant mass and size, perforation length, fracture pressure, surface pressure trend and fracture dimensions on horizontal well bores. The goal of this analysis is to establish the relationship between frac design, pressure and frac network geometry. Data mining techniques are used on this complex database to find possible hidden relationships to explain the nature of the data. The outcome of this study is to develop a predictive model for fracture networks in shale. Also, using the predictive model, improvements in the current fracture design in Barnett shale are made.

Various aspects of this dataset are examined using data modeling and mining techniques including self-organizing maps (SOM). SOMs are unsupervised artificial neural networks that can cluster large amounts of data into two dimensional maps. Using SOM, frac design parameters are clustered and studied in depth. Then, a forward predictive neural network model is trained with fracture design parameters as inputs and fracture network length, width, height, and fracture volume as outputs. The network is trained with the help of genetic algorithm (GA). Sensitivity study on the trained network demonstrates the effect of different parameters on the fracture geometry. For example, an increase in slick-water volume will have a positive effect on fracture network width and length and negative effect on height. On the other hand, higher injection rates tend to accelerate height growth. Perforation length is also having a negative impact on the total stimulated or affected reservoir volume and tighter perforation designs are preferred. The results of this work potentially helps understanding of the development of fracture networks in shale reservoirs and the recommendations on improving stimulated reservoir volume. This will potentially help operators on more effective treatment designs and reducing the operational costs associated with fracturing in a brittle shale environment.

Organizer Jonathan Godwin

Contact:


jgodwin@carboceramics.com


281-921-6526

When?

Wed, Oct. 23, 2013
11:30 a.m. - 1 p.m. US/Central

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Greenspoint Club
16925 Northchase Drive
Houston, Texas 77060
USA

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