We have the ECLIPSE executable that we can call, there is an ECLIPSE input text file called input.DATA which includes well location in x and y directions. optimizer should change the x and y locations of the wells each time and then simulations need to be automatically submitted by PYTHON for the specified number of times and gives the field production values each time. we want to maximize the output: Cumulative Oil Production which exists in the output file.
then we will find the best configuration of wells in the field that maximizes the cumulative oil production.
I appreciate your help.
thanks.
First create a numpy array of x y location you want to try out, secondly the well location description in the eclipse data file, cut that section and save in a file , call it change.dat. In the main eclipse data file change that portion and write “INCLUDE” then under it put the “change.dat”. This will make eclipse read that well property as an external file via the include statement. Now write a simple python code using freas and fwrite commands that will take the n by 2 numpy array you created earlier. In a for loop, take each row of the n by. 2 array, use open/fwrite commands on the change .dat file so you can be over writing the X and y location in that file. Run close by invoking “ecl XX.dat” where XX.dat is the main of your eclipse data file. Make sure you use the EXCEL and SERPARATE key words in the data file to output the oil/water/gas production rate to compute your NPV. You can use reinforcement learning to solve for the steepest ascent value of this NPV taking into account the discount factor and oil price per barrel and also cost for disposal and water treatment. You can let RL guide this well locations automatically instead of using brute force.at the end the algorithm will converge on the optimum x and y location with the maximised NPV value. Hope this helps.