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optimizer:
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----------
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For each Site constraint (usually one):
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call Site_Maskwa_steam_recommendation:
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params:
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site_data: for each trunk -> pad : {
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data (csv file / dropped unnamed column)
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max_constraints (well constraints)
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model
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pad_constraints: (pad constraints)
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}
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scenario_id
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step_size: 100
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steam_site_daily_inject: 1100
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steam_site_constraint_daily : 1100
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trunk_constraints: (trunk constraints)
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well_statuses: (well statuses)
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start_date
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end_date
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===========================================================================================================================
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Site_Maskwa_steam_recommendation:
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-----------------------------
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params:
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site_data: for each trunk -> pad : {
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data (csv file / dropped unnamed column)
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max_constraints (well constraints)
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model
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pad_constraints: (pad constraints)
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}
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scenario_id
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step_size: 100
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steam_site_daily_inject: 1100
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steam_site_constraint_daily : 1100
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trunk_constraints: (trunk constraints)
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well_statuses: (well statuses)
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start_date
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end_date
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variables / code:
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steam_site_daily_inject: 1100
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build the following:
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Pad_infill_well_list: wells per trunk
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pad_constraints : list of (pad, [constraints])
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well_constraints: list of (well, [constraints])
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date_ranges: for each date range add this:
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(
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end_date,
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start_date,
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min_trunk_stream ([0,0,0]),
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max_trunk_stream ([20000,20000]),
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min_pad_stream: { pad_name: 500, ... },
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max_pad_stream: { pad_name: 5000, ... }
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)
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for each date_range (usually one):
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trunk_steam_allocation: { for each trunk the bigger between two values: 1. min of each trunk, or 2. sum of min of pads}
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trunk_steam_max_allocation: { for each trunk the smaller between two: 1. the max for trunk, or 2. the sum of max of pads }
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Steam_site_daily_inject_local : is the min of two values: 1. the Steam_site_daily_inject or 2. sum of all max of trunk in trunk_steam_max_allocation
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trunk_steam_step = 1000
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max_volume_total = 0
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recomd_steam_distribution = {dict for each trunk an empty array}
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prediction_output_temp = None
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check and validate the `trunk_steam_allocation`
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results_cache = { for each trunk }
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initial = trunk_steam_allocation
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trunk_steam_allocation_sum = sum of all trunk steam allocation : trunk_steam_allocation
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| down remark : while sum of all current trunk stream < max steam site which is steam site or sum of all trunks |
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while trunk_steam_allocation_sum < Steam_site_daily_inject_local or initial:
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print ('.... Running 365 : ', )
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Do Something (while_loop)
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update trunk_steam_allocation_sum
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-------------------------------------------------
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Do Somthing (while_loop):
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max_volume_total = 0
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adjust the trunk step size (1000 or lower)
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for each trunk in trunk_steam_allocation:
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temp_sum_total = 0
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recomendation_temp = []
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df_Pad_data_name_date_temp = None
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trunk_steam_allocation_temp = trunk_steam_allocation.copy()
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adjust the trunk_steam_step
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for each trunk in site_data:
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recomendation_local_temp = None
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max_volume_temp, recomd_steam_distribution_temp, df_Pad_data_name_date_local, cache, recomendation_local_temp = One_Trunk_steam_recommendation()
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results_cache[trunk] = cache
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recomd_steam_distribution[trunk] = recomd_steam_distribution_temp.copy()
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temp_sum_total += max_volume_temp
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recomendation_temp += recomendation_local_temp
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df_Pad_data_name_date_temp = pd.concat([df_Pad_data_name_date_temp, df_Pad_data_name_date_local])
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===========================================================================================================================
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One_Trunk_steam_recommendation
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-----------------------------
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params:
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pad_data: as above but only for the trunk
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Steam_step_size: 100
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Steam_site_daily_inject: 3500 (min of trunk)
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Steam_site_constraint_daily: 3500
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Steam_trunk_constraint_daily: trunk_constraints(for this trunk)
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recomnd_steam_array: []
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start_date: start_date
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end_date: end_date
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results_cache {}
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Pad_infill_well_list: list of wells for the trunk
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print_result=1 # print_result = 1 will print recommendation results as well as corresponding oil production
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variables / code:
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recomd_steam_distribution = None
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tmp = time.time()
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output_prediction = None
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output_array = []
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volume = 0
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for row in Steam_trunk_constraint_daily (usually on constraint):
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Steam_pad_constraint_daily_max = 0
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Steam_well_constraint_daily_max = []
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infill_well_list = [] (list of all wells (volume columns))
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for pad in pad_data:
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add to the pad_data: {
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// previous
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data, max_constraints, model, pad_constraints
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// new
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maximum_pad_constraint: last constraint steam
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minimum_pad_constraint: last constraint min
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min_well_constraints: [ list of well min constraint ]
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max_well_constraints: [ list of well max constraint ]
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}
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Steam_well_constraint_daily_max = concat well max for all wells
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Steam_well_constraint_all_well_daily_max = sum of list of pad max where pad max = bigger of either 1. pad constraint max 2. sum of well max
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Steam_well_constraint_daily_min = [0] * number of wells
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max_rec = 0
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Total_steam_ava = Steam_site_daily_inject (start point)
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return volume, recomnd_steam_array, output_prediction, results_cache, output_array

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