Transforming a time series so it is stationary I am not sure if I am undertaking the following steps correctly. I am trying to make this time series stationary: as you can see, it is decreasing:

In the beginning I tried taking log, but it is still decreasing:

Then I take differences, but still it doesn't look stationary to me:

Did I do anything wrong? What can I do to make it stationary? I need it to be stationary to use ACF and PACF.
 A: What matters if that your errors can be described by stationarity. In a statistical model the only distributional assumption is on the noise. That is the residuals from some model should be stationary and described by an appropriate probability distribution. Just plotting the data corresponds to a null model where the data themselves are in some sense residual. You could try to account for the declining mean by including some covariate(s) in a model, even if one of those covariates is simply time (e.g. a linear decline). 
A: Clearly, like Hey Lyla has pointed out, you have some seasonality in there. I would suggest you try to include $11$ monthly dummy variables for the first $11$ months. (Do not include $12$, since you will run into the dummy variable trap else.) I Then you can seasonally adjust the data by substracting the fitted model's monthly dummies from the actual data. This might do the job. 
Alternatively, you could consider applying a Fourier Transformation (essentially fitting sine/cosine curves) and substracting the corresponding coefficients instead. The number of expansion terms (i.e., sine/cosine functions) can be determined with information criteria like AIC or BIC.

Edit: As pointed out by Richard Hardy in the comments, if you suspect any kind of deterministic time trend to be present in the data, you should also include an appropriate regressor (in your case potentially one for a linear trend). Note that you will still only have to substract the monthly dummies.
A: It looks to me your series need a seasonal adjustment. You can check if it is true from the pacf; if the series shows significant spikes regularly (for example a spikes each 12 lags) then it has seasonality issues.
A: Log transform doesn't help with mean stationarity. It may help with certain kind of variance stationarity, namely when the variance is proportional to the levels.
Log-transform may help by linearizing the growth rates, e.g. $e^{\alpha x}$ will become $\alpha x$ after the log transform, which may help with modeling.
In any case you r transformation should have some purpose and correspond to the underlying process. Just randomly throwing things at the series to get the mean stabilize is not a good idea, you may mess up other characteristics.
Your series look like a candidate for threshold models with some kind of downward trend. Again, you can't simply de-trend it by using your favorite detrending function. Maybe the trend is what's most interesting in your series. There's got to be a purpose in these transformations.
