There are probably a lot of factors. I worked on CUDA code for around a year, and used to understand the landscape pretty well, but if I were to start a high-performance computing project today I'd probably take my lumps and go with OpenCL. There would be a lot of lumps.

Firstly, CUDA is just more mature; there is a very large and well-established set of libraries for a lot of common operations, there is a decent sized community, and Nvidia even produces specialized hardware (Tesla cards) designed just for CUDA.

Second, all that generic-ness of OpenCL doesn't come for free. With Nvidia, you're just working with one architecture; CUDA cards. Optimizing your kernels is much easier. OpenCL is just generically parallel, so you could have any sort of crazy heterogeneous high-performance computing environment you have to fiddle with (any number of CPU's with different chipsets and any number of GPU's with different chipsets).

I haven't used OpenCL myself, but almost purely anecdotally I have heard many people say that CUDA is often slightly faster[1] and the code is easier to write.

TL;DR: CUDA sacrifices flexibility for ease of development and performance gains. OpenCL wants to be everything for everyone, and comes with the typical burdens.

[1]: Maybe this is a result of OpenCL being more generic and so harder to optimize.