

In [53]:

    
# load the package
include("../FractionalFlow/FractionalFlow.jl")
using PyPlot, SetPyPlot, NLopt, Dierckx, BlackBoxOptim, Statistics
import Calculus
import GR
FF = FractionalFlow









    



WARNING: replacing module FractionalFlow.






    Out[53]:





Main.FractionalFlow

Note

For the tertiary low salinity water flooding, the recovery factor is calculated by $$R_{tert}=\frac{S_{w,avg}-(1-S_{or,hs})}{S_{or,hs}}$$ However, the recovery factor is usually reported as the percentage of the initial oil that is in the reservoir before the secondary formation water water flooding, i.e., $$R=\frac{S_{w,avg}-S_{w,init}}{1-S_{w,init}}$$ To convert one to the other, we can write $$R=\frac{\left(R_{tert}S_{or,hs}+(1-S_{or,hs})\right)-S_{w,init}}{1-S_{w,init}}$$ We can also do it the other way around, i.e., $$R_{tert}=\frac{R\left(1-S_{w,init}\right)+S_{w,init}-\left(1-S_{or,hs}\right)}{S_{or,hs}}$$

Tertiary Water-flooding



In [54]:

    
# define the problem
sw0 = 0.15 # initial condition of the experiment
sor_hs = 0.25 # high salinity (formation water) residual oil saturation
sw_init = 1-sor_hs
fluids_hs = FF.oil_water_fluids(mu_water=1e-3, mu_oil=2e-3)
fluids_ls = FF.oil_water_fluids(mu_water=1e-3, mu_oil=2e-3)
rel_perms_hs = FF.oil_water_rel_perms(krw0=0.4, kro0=0.9, 
        swc=0.15, sor=sor_hs, nw=2.0, no = 2.0)
rel_perms_ls = FF.oil_water_rel_perms(krw0=0.3, kro0=0.95, 
        swc=0.15, sor=0.15, nw=2.0, no = 2.0)
core_flood = FF.core_flooding(u_inj=1.15e-5, pv_inject=4.0, p_back=1e5, sw_init=sw_init, 
    sw_inj=1.0, rel_perms=rel_perms_hs)
core_props = FF.core_properties()
wf_res = FF.low_sal_water_flood(core_props, fluids_ls, fluids_hs, rel_perms_hs, 
        rel_perms_ls, core_flood)
FF.visualize(wf_res)









    












    












    












    












    












    Out[54]:





PyObject <matplotlib.legend.Legend object at 0x0000000004D5C748>

synthetic experimental data

Here I scale the calculated recovery data to the total recovery data for a core initially saturated with oil at a saturation close to the connate water saturation, and secondary flooded with formation water.



In [55]:

    
R_exp = (wf_res.recovery_time[:,2].*sor_hs.+(1-sor_hs).-sw0)./(1.0-sw0)
t_exp_dp = wf_res.dp_time[:,1]
dp_exp = wf_res.dp_time[:,2]
t_exp_R = wf_res.recovery_time[:,1]
# R_exp = wf_res.recovery_time[:,2]
plotyy(t_exp_R, R_exp, t_exp_dp, dp_exp, fig_size = [8,5], x_label="time [s]", y1_label="R [-]", y2_label="dP [Pa]")









    












    Out[55]:





(Figure(PyObject <Figure size 800x500 with 2 Axes>), PyObject <matplotlib.axes._subplots.AxesSubplot object at 0x000000003BB508D0>, PyObject <matplotlib.axes._subplots.AxesSubplot object at 0x000000003B9F4DA0>)

Later, we need to convert the above data back to the tertiary recovery factor, as if the core flooding experiments starts with a core saturated with $S_{or,hs}$ oil saturation.

define the objective function



In [56]:

    
# struct
struct exp_data
    t_exp_dp
    dp_exp
    t_exp_R
    R_exp
end

# convert the recovery data
R_conv = (R_exp.*(1-sw0).+sw0.-(1-sor_hs))/sor_hs
exp_data1 = exp_data(t_exp_dp, dp_exp, t_exp_R, R_conv);



In [57]:

    
"""
rel_perm_param [krw0, kro0, nw, no, swc, sor]
core_props, fluids_ls, fluids_hs, rel_perms_hs, 
        rel_perms_ls, core_flood
"""
function error_calc(rel_perm_param, exp_data, core_props, fluids_ls, fluids_hs, 
        rel_perms_hs, core_flood; w_p=1.0, w_R=1.0)
    rel_perms_ls = FF.oil_water_rel_perms(krw0=rel_perm_param[1], kro0=rel_perm_param[2], 
    swc=rel_perm_param[5], sor=rel_perm_param[6], nw=rel_perm_param[3], no = rel_perm_param[4])
    wf_res = FF.low_sal_water_flood(core_props, fluids_ls, fluids_hs, rel_perms_hs, 
        rel_perms_ls, core_flood)
    dp_calc = Spline1D(wf_res.dp_time[:,1], wf_res.dp_time[:,2], k=1, bc="nearest")
    R_calc = Spline1D(wf_res.recovery_time[:,1], wf_res.recovery_time[:,2], k=1, bc="nearest")
    error_dp = abs.(dp_calc(exp_data.t_exp_dp) .- exp_data.dp_exp)
#     println(error_dp)
    error_R = abs.(R_calc(exp_data.t_exp_R) .- exp_data.R_exp)
#     println(error_R)
    error_dp_norm = w_p.*error_dp./exp_data.dp_exp
    error_R_norm = w_R.*error_R #./(exp_data.R_exp+eps()) # to avoid division by a small number
    return mean(error_R_norm)+mean(error_dp_norm)
end

function vis_error(rel_perm_param, exp_data, core_props, fluids_ls, fluids_hs, 
        rel_perms_hs, core_flood)
    rel_perms_ls = FF.oil_water_rel_perms(krw0=rel_perm_param[1], kro0=rel_perm_param[2], 
    swc=rel_perm_param[5], sor=rel_perm_param[6], nw=rel_perm_param[3], no = rel_perm_param[4])
    wf_res = FF.low_sal_water_flood(core_props, fluids_ls, fluids_hs, rel_perms_hs, 
        rel_perms_ls, core_flood)
    figure()
    plot(wf_res.dp_time[:,1], wf_res.dp_time[:,2],  exp_data.t_exp_dp, exp_data.dp_exp, "o")
    xlabel("t [s]")
    ylabel("dp [Pa]")
    legend(["Theoretical", "Experiment"])
    
    figure()
    plot(wf_res.recovery_time[:,1], wf_res.recovery_time[:,2], exp_data.t_exp_R, exp_data.R_exp, "v")
    xlabel("t [s]")
    ylabel("R [-]")
    legend(["Theoretical", "Experiment"])
    
end

# test
x_init = [0.109681, 0.201297, 3.96653, 3.0, 0.19, 0.1]

vis_error(x_init, exp_data1, core_props, fluids_ls, fluids_hs, rel_perms_hs, core_flood)
error_calc(x_init, exp_data1, core_props, fluids_ls, fluids_hs, rel_perms_hs, core_flood)









    












    












    Out[57]:





3.9618511090093786

define the objective function and gradients and weight factors



In [58]:

    
# weight factors:
w_p = ones(length(exp_data1.dp_exp))
temp_val, ind_max = findmax(exp_data1.dp_exp)
w_p[ind_max-1:ind_max+2] .= 10
w_p[end:end-5] .= 10
w_p[1] = 10
w_R = ones(length(exp_data1.R_exp))
w_R[20:25] .= 10
w_R[end:end-5] .= 10


function f(x)
    f_val = 0.0
    try
        f_val = error_calc(x, exp_data1, core_props, fluids_ls, fluids_hs, 
            rel_perms_hs, core_flood, w_p = w_p, w_R = w_R)
    catch
        f_val = 100.0
#         info("Objective function did not converge!")
    end
    return f_val
end

    
function g(x)
    eps1 = 1e-4
    f_val = f(x)
    g_val = ones(length(x))
    try
        # g_val = Calculus.gradient(x -> error_calc(x, exp_data1, core_props, fluids, core_flood), x)
        for j in eachindex(x)
            x2 = copy(x)
            x2[j]+=eps1
            f_val2 = f(x2)
            g_val[j] = (f_val2-f_val)/eps1
        end
    catch
        g_val = ones(length(x))
    end
    return g_val
end

function obj_fun(param, grad)
    if length(grad)>0
      grad[:] = g(param)
    end
    
    obj_fun_val = f(param)
    if isnan(obj_fun_val) || isinf(obj_fun_val)
        obj_fun_val = 100.0
    end
    return obj_fun_val
end

# test
grad_x = zeros(6)
obj_fun([1.0, 0.8, 3, 4, 0.1, 0.1], grad_x)

f([1.0, 0.8, 2, 2, 0.1, 0.1])









    Out[58]:





0.847348832234076



In [59]:

    
x_init = [0.9, 0.8, 2.5, 2.5, 0.1, 0.1]
x_lb = [0.1, 0.1, 1.5, 1.5, 0.05, 0.1]
x_ub = [1.0, 1.0, 4.0, 4.0, core_flood.initial_water_saturation, 0.25]

res = bboptimize(f, SearchRange = [(0.1, 1.0), (0.1, 1.0), (1.5, 4.0), (1.5, 4.0), 
        (0.05, core_flood.initial_water_saturation), (0.1, 0.25)])









    



Starting optimization with optimizer DiffEvoOpt{FitPopulation{Float64},RadiusLimitedSelector,BlackBoxOptim.AdaptiveDiffEvoRandBin{3},RandomBound{ContinuousRectSearchSpace}}
0.00 secs, 0 evals, 0 steps
0.51 secs, 20 evals, 10 steps, improv/step: 0.700 (last = 0.7000), fitness=0.130354101
1.06 secs, 49 evals, 27 steps, improv/step: 0.519 (last = 0.4118), fitness=0.130354101
1.61 secs, 77 evals, 44 steps, improv/step: 0.523 (last = 0.5294), fitness=0.130354101
2.11 secs, 109 evals, 64 steps, improv/step: 0.516 (last = 0.5000), fitness=0.123901856
2.62 secs, 136 evals, 82 steps, improv/step: 0.537 (last = 0.6111), fitness=0.123901856
3.16 secs, 164 evals, 102 steps, improv/step: 0.520 (last = 0.4500), fitness=0.123901856
3.71 secs, 189 evals, 119 steps, improv/step: 0.555 (last = 0.7647), fitness=0.123901856
4.23 secs, 215 evals, 140 steps, improv/step: 0.529 (last = 0.3810), fitness=0.123901856
4.76 secs, 242 evals, 163 steps, improv/step: 0.497 (last = 0.3043), fitness=0.084346110
5.28 secs, 264 evals, 180 steps, improv/step: 0.478 (last = 0.2941), fitness=0.084346110
5.80 secs, 300 evals, 212 steps, improv/step: 0.476 (last = 0.4688), fitness=0.084346110
6.31 secs, 334 evals, 241 steps, improv/step: 0.477 (last = 0.4828), fitness=0.084346110
6.81 secs, 356 evals, 260 steps, improv/step: 0.465 (last = 0.3158), fitness=0.084346110
7.32 secs, 387 evals, 289 steps, improv/step: 0.467 (last = 0.4828), fitness=0.084346110
7.82 secs, 422 evals, 322 steps, improv/step: 0.447 (last = 0.2727), fitness=0.059795116
8.33 secs, 458 evals, 356 steps, improv/step: 0.435 (last = 0.3235), fitness=0.059795116
8.85 secs, 495 evals, 391 steps, improv/step: 0.417 (last = 0.2286), fitness=0.059795116
9.36 secs, 516 evals, 409 steps, improv/step: 0.411 (last = 0.2778), fitness=0.059795116
9.90 secs, 540 evals, 431 steps, improv/step: 0.406 (last = 0.3182), fitness=0.059795116
10.46 secs, 556 evals, 446 steps, improv/step: 0.404 (last = 0.3333), fitness=0.059795116
10.96 secs, 575 evals, 464 steps, improv/step: 0.399 (last = 0.2778), fitness=0.059795116
11.47 secs, 600 evals, 488 steps, improv/step: 0.393 (last = 0.2917), fitness=0.059795116
11.98 secs, 629 evals, 517 steps, improv/step: 0.395 (last = 0.4138), fitness=0.059795116
12.49 secs, 666 evals, 552 steps, improv/step: 0.388 (last = 0.2857), fitness=0.059795116
13.00 secs, 702 evals, 587 steps, improv/step: 0.385 (last = 0.3429), fitness=0.059795116
13.51 secs, 739 evals, 623 steps, improv/step: 0.376 (last = 0.2222), fitness=0.059795116
14.03 secs, 770 evals, 653 steps, improv/step: 0.372 (last = 0.3000), fitness=0.059795116
14.55 secs, 793 evals, 675 steps, improv/step: 0.370 (last = 0.3182), fitness=0.059795116
15.06 secs, 829 evals, 710 steps, improv/step: 0.359 (last = 0.1429), fitness=0.059795116
15.57 secs, 859 evals, 737 steps, improv/step: 0.351 (last = 0.1481), fitness=0.059795116
16.08 secs, 889 evals, 766 steps, improv/step: 0.345 (last = 0.1724), fitness=0.059795116
16.58 secs, 927 evals, 803 steps, improv/step: 0.337 (last = 0.1892), fitness=0.059795116
17.08 secs, 964 evals, 839 steps, improv/step: 0.334 (last = 0.2500), fitness=0.059795116
17.60 secs, 1001 evals, 875 steps, improv/step: 0.331 (last = 0.2778), fitness=0.059795116
18.11 secs, 1038 evals, 910 steps, improv/step: 0.323 (last = 0.1143), fitness=0.059795116
18.61 secs, 1076 evals, 947 steps, improv/step: 0.320 (last = 0.2432), fitness=0.059795116
19.12 secs, 1113 evals, 984 steps, improv/step: 0.314 (last = 0.1622), fitness=0.055646045
19.63 secs, 1138 evals, 1009 steps, improv/step: 0.311 (last = 0.2000), fitness=0.055646045
20.14 secs, 1174 evals, 1043 steps, improv/step: 0.311 (last = 0.2941), fitness=0.055646045
20.66 secs, 1212 evals, 1080 steps, improv/step: 0.306 (last = 0.1622), fitness=0.055646045
21.17 secs, 1252 evals, 1119 steps, improv/step: 0.300 (last = 0.1538), fitness=0.055646045
21.67 secs, 1288 evals, 1154 steps, improv/step: 0.298 (last = 0.2286), fitness=0.055646045
22.18 secs, 1323 evals, 1188 steps, improv/step: 0.297 (last = 0.2647), fitness=0.055646045
22.70 secs, 1352 evals, 1217 steps, improv/step: 0.296 (last = 0.2414), fitness=0.055646045
23.23 secs, 1379 evals, 1244 steps, improv/step: 0.292 (last = 0.1111), fitness=0.055646045
23.74 secs, 1409 evals, 1271 steps, improv/step: 0.293 (last = 0.3704), fitness=0.055646045
24.25 secs, 1446 evals, 1306 steps, improv/step: 0.293 (last = 0.2857), fitness=0.055646045
24.77 secs, 1477 evals, 1335 steps, improv/step: 0.291 (last = 0.2069), fitness=0.055646045
25.28 secs, 1514 evals, 1369 steps, improv/step: 0.290 (last = 0.2353), fitness=0.055646045
25.80 secs, 1547 evals, 1402 steps, improv/step: 0.287 (last = 0.1818), fitness=0.055646045
26.30 secs, 1584 evals, 1437 steps, improv/step: 0.288 (last = 0.3143), fitness=0.055646045
26.80 secs, 1621 evals, 1474 steps, improv/step: 0.284 (last = 0.1081), fitness=0.055646045
27.31 secs, 1646 evals, 1499 steps, improv/step: 0.281 (last = 0.1200), fitness=0.055646045
27.82 secs, 1674 evals, 1527 steps, improv/step: 0.280 (last = 0.2143), fitness=0.031175328
28.32 secs, 1713 evals, 1566 steps, improv/step: 0.277 (last = 0.1795), fitness=0.031175328
28.84 secs, 1752 evals, 1605 steps, improv/step: 0.273 (last = 0.1026), fitness=0.031175328
29.34 secs, 1788 evals, 1641 steps, improv/step: 0.269 (last = 0.1111), fitness=0.031175328
29.87 secs, 1816 evals, 1669 steps, improv/step: 0.267 (last = 0.1429), fitness=0.031175328
30.37 secs, 1850 evals, 1702 steps, improv/step: 0.266 (last = 0.1818), fitness=0.027441003
30.88 secs, 1888 evals, 1740 steps, improv/step: 0.264 (last = 0.2105), fitness=0.027441003
31.38 secs, 1927 evals, 1779 steps, improv/step: 0.263 (last = 0.2051), fitness=0.027441003
31.90 secs, 1953 evals, 1803 steps, improv/step: 0.262 (last = 0.1667), fitness=0.027441003
32.41 secs, 1992 evals, 1842 steps, improv/step: 0.259 (last = 0.1282), fitness=0.027441003
32.91 secs, 2017 evals, 1866 steps, improv/step: 0.257 (last = 0.0833), fitness=0.027441003
33.43 secs, 2045 evals, 1894 steps, improv/step: 0.255 (last = 0.1429), fitness=0.027441003
33.94 secs, 2073 evals, 1921 steps, improv/step: 0.254 (last = 0.1852), fitness=0.027441003
34.46 secs, 2098 evals, 1946 steps, improv/step: 0.253 (last = 0.2000), fitness=0.027441003
34.97 secs, 2133 evals, 1980 steps, improv/step: 0.252 (last = 0.1765), fitness=0.027441003
35.47 secs, 2159 evals, 2006 steps, improv/step: 0.251 (last = 0.1538), fitness=0.027441003
35.99 secs, 2189 evals, 2036 steps, improv/step: 0.249 (last = 0.1333), fitness=0.027441003
36.49 secs, 2225 evals, 2069 steps, improv/step: 0.248 (last = 0.2121), fitness=0.023619697
37.01 secs, 2262 evals, 2104 steps, improv/step: 0.248 (last = 0.2286), fitness=0.023619697
37.51 secs, 2295 evals, 2137 steps, improv/step: 0.247 (last = 0.1818), fitness=0.023619697
38.01 secs, 2333 evals, 2175 steps, improv/step: 0.243 (last = 0.0000), fitness=0.023619697
38.52 secs, 2368 evals, 2210 steps, improv/step: 0.240 (last = 0.0571), fitness=0.023619697
39.04 secs, 2396 evals, 2238 steps, improv/step: 0.238 (last = 0.1071), fitness=0.023619697
39.57 secs, 2421 evals, 2262 steps, improv/step: 0.239 (last = 0.3333), fitness=0.023619697
40.08 secs, 2451 evals, 2291 steps, improv/step: 0.239 (last = 0.2414), fitness=0.023619697
40.58 secs, 2487 evals, 2326 steps, improv/step: 0.237 (last = 0.1143), fitness=0.023619697
41.08 secs, 2525 evals, 2362 steps, improv/step: 0.236 (last = 0.1389), fitness=0.023619697
41.59 secs, 2552 evals, 2387 steps, improv/step: 0.236 (last = 0.2400), fitness=0.023619697
42.09 secs, 2588 evals, 2422 steps, improv/step: 0.236 (last = 0.2571), fitness=0.023619697
42.60 secs, 2625 evals, 2458 steps, improv/step: 0.234 (last = 0.1111), fitness=0.023619697
43.10 secs, 2664 evals, 2496 steps, improv/step: 0.233 (last = 0.1316), fitness=0.023619697
43.61 secs, 2689 evals, 2521 steps, improv/step: 0.232 (last = 0.2000), fitness=0.023619697
44.12 secs, 2722 evals, 2553 steps, improv/step: 0.231 (last = 0.1250), fitness=0.023619697
44.62 secs, 2748 evals, 2579 steps, improv/step: 0.230 (last = 0.1154), fitness=0.023619697
45.14 secs, 2786 evals, 2616 steps, improv/step: 0.229 (last = 0.1351), fitness=0.023619697
45.64 secs, 2811 evals, 2641 steps, improv/step: 0.227 (last = 0.0800), fitness=0.023619697
46.15 secs, 2847 evals, 2677 steps, improv/step: 0.226 (last = 0.1667), fitness=0.023619697
46.66 secs, 2873 evals, 2702 steps, improv/step: 0.226 (last = 0.1600), fitness=0.023619697
47.16 secs, 2905 evals, 2733 steps, improv/step: 0.224 (last = 0.0968), fitness=0.022179115
47.67 secs, 2945 evals, 2773 steps, improv/step: 0.224 (last = 0.2000), fitness=0.022179115
48.19 secs, 2976 evals, 2804 steps, improv/step: 0.224 (last = 0.1935), fitness=0.020426512
48.76 secs, 3004 evals, 2832 steps, improv/step: 0.222 (last = 0.1071), fitness=0.019818156
49.27 secs, 3033 evals, 2861 steps, improv/step: 0.222 (last = 0.1379), fitness=0.017268805
49.78 secs, 3067 evals, 2895 steps, improv/step: 0.221 (last = 0.2059), fitness=0.017268805
50.28 secs, 3105 evals, 2932 steps, improv/step: 0.220 (last = 0.1351), fitness=0.017268805
50.79 secs, 3144 evals, 2969 steps, improv/step: 0.220 (last = 0.1622), fitness=0.017268805
51.30 secs, 3183 evals, 3008 steps, improv/step: 0.218 (last = 0.1282), fitness=0.017268805
51.82 secs, 3212 evals, 3037 steps, improv/step: 0.217 (last = 0.1034), fitness=0.017268805
52.33 secs, 3244 evals, 3068 steps, improv/step: 0.217 (last = 0.2258), fitness=0.017268805
52.84 secs, 3267 evals, 3090 steps, improv/step: 0.217 (last = 0.1364), fitness=0.017268805
53.34 secs, 3304 evals, 3127 steps, improv/step: 0.217 (last = 0.2162), fitness=0.017268805
53.86 secs, 3343 evals, 3164 steps, improv/step: 0.217 (last = 0.2432), fitness=0.010824736
54.37 secs, 3369 evals, 3190 steps, improv/step: 0.216 (last = 0.0385), fitness=0.010824736
54.90 secs, 3402 evals, 3223 steps, improv/step: 0.215 (last = 0.1515), fitness=0.006282297
55.41 secs, 3428 evals, 3249 steps, improv/step: 0.215 (last = 0.2308), fitness=0.006282297
55.92 secs, 3464 evals, 3282 steps, improv/step: 0.215 (last = 0.2121), fitness=0.006282297
56.43 secs, 3503 evals, 3321 steps, improv/step: 0.214 (last = 0.1026), fitness=0.006282297
56.94 secs, 3537 evals, 3353 steps, improv/step: 0.214 (last = 0.2188), fitness=0.006282297
57.45 secs, 3567 evals, 3382 steps, improv/step: 0.213 (last = 0.1379), fitness=0.006282297
57.95 secs, 3589 evals, 3404 steps, improv/step: 0.213 (last = 0.2273), fitness=0.006282297
58.46 secs, 3622 evals, 3437 steps, improv/step: 0.212 (last = 0.1212), fitness=0.006282297
58.96 secs, 3648 evals, 3463 steps, improv/step: 0.211 (last = 0.0769), fitness=0.006282297
59.46 secs, 3682 evals, 3493 steps, improv/step: 0.212 (last = 0.3000), fitness=0.006282297
59.97 secs, 3715 evals, 3524 steps, improv/step: 0.212 (last = 0.1613), fitness=0.006282297
60.48 secs, 3743 evals, 3551 steps, improv/step: 0.212 (last = 0.2593), fitness=0.006282297
60.99 secs, 3770 evals, 3578 steps, improv/step: 0.211 (last = 0.0741), fitness=0.006282297
61.51 secs, 3801 evals, 3608 steps, improv/step: 0.211 (last = 0.2000), fitness=0.006282297
62.02 secs, 3825 evals, 3632 steps, improv/step: 0.211 (last = 0.1667), fitness=0.006282297
62.54 secs, 3852 evals, 3658 steps, improv/step: 0.211 (last = 0.2308), fitness=0.006282297
63.05 secs, 3891 evals, 3697 steps, improv/step: 0.210 (last = 0.1795), fitness=0.006282297
63.55 secs, 3916 evals, 3721 steps, improv/step: 0.211 (last = 0.2917), fitness=0.006282297
64.08 secs, 3929 evals, 3734 steps, improv/step: 0.210 (last = 0.0769), fitness=0.006282297
64.58 secs, 3964 evals, 3768 steps, improv/step: 0.210 (last = 0.1765), fitness=0.006282297
65.09 secs, 4003 evals, 3807 steps, improv/step: 0.209 (last = 0.1282), fitness=0.006282297
65.61 secs, 4037 evals, 3841 steps, improv/step: 0.208 (last = 0.0882), fitness=0.006282297
66.12 secs, 4066 evals, 3870 steps, improv/step: 0.207 (last = 0.0690), fitness=0.006282297
66.63 secs, 4091 evals, 3895 steps, improv/step: 0.207 (last = 0.1600), fitness=0.006282297
67.15 secs, 4108 evals, 3912 steps, improv/step: 0.207 (last = 0.1176), fitness=0.006282297
67.69 secs, 4126 evals, 3930 steps, improv/step: 0.207 (last = 0.2222), fitness=0.006282297
68.20 secs, 4142 evals, 3946 steps, improv/step: 0.206 (last = 0.0625), fitness=0.006282297
68.72 secs, 4155 evals, 3959 steps, improv/step: 0.206 (last = 0.2308), fitness=0.006282297
69.25 secs, 4170 evals, 3974 steps, improv/step: 0.206 (last = 0.2667), fitness=0.006282297
69.79 secs, 4183 evals, 3987 steps, improv/step: 0.206 (last = 0.2308), fitness=0.006282297
70.32 secs, 4196 evals, 3999 steps, improv/step: 0.207 (last = 0.3333), fitness=0.006282297
70.83 secs, 4207 evals, 4009 steps, improv/step: 0.207 (last = 0.1000), fitness=0.006282297
71.34 secs, 4219 evals, 4021 steps, improv/step: 0.207 (last = 0.2500), fitness=0.006282297
71.85 secs, 4231 evals, 4033 steps, improv/step: 0.206 (last = 0.0833), fitness=0.006282297
72.36 secs, 4246 evals, 4048 steps, improv/step: 0.206 (last = 0.2000), fitness=0.006282297
72.88 secs, 4257 evals, 4059 steps, improv/step: 0.206 (last = 0.1818), fitness=0.006282297
73.41 secs, 4267 evals, 4069 steps, improv/step: 0.206 (last = 0.3000), fitness=0.006282297
73.95 secs, 4277 evals, 4079 steps, improv/step: 0.206 (last = 0.0000), fitness=0.006282297
74.45 secs, 4285 evals, 4087 steps, improv/step: 0.206 (last = 0.0000), fitness=0.006282297
74.95 secs, 4293 evals, 4094 steps, improv/step: 0.205 (last = 0.1429), fitness=0.006282297
75.55 secs, 4304 evals, 4103 steps, improv/step: 0.206 (last = 0.4444), fitness=0.006282297
76.09 secs, 4315 evals, 4113 steps, improv/step: 0.206 (last = 0.3000), fitness=0.006282297
76.66 secs, 4327 evals, 4125 steps, improv/step: 0.206 (last = 0.0833), fitness=0.006282297
77.17 secs, 4337 evals, 4135 steps, improv/step: 0.206 (last = 0.2000), fitness=0.006282297
77.69 secs, 4347 evals, 4144 steps, improv/step: 0.206 (last = 0.3333), fitness=0.006165264
78.25 secs, 4359 evals, 4155 steps, improv/step: 0.206 (last = 0.3636), fitness=0.006165264
78.78 secs, 4370 evals, 4166 steps, improv/step: 0.206 (last = 0.1818), fitness=0.006165264
79.30 secs, 4385 evals, 4181 steps, improv/step: 0.206 (last = 0.0667), fitness=0.006165264
79.81 secs, 4409 evals, 4205 steps, improv/step: 0.205 (last = 0.0833), fitness=0.006165264
80.32 secs, 4446 evals, 4242 steps, improv/step: 0.204 (last = 0.0541), fitness=0.006165264
80.84 secs, 4473 evals, 4269 steps, improv/step: 0.203 (last = 0.0741), fitness=0.006165264
81.35 secs, 4504 evals, 4300 steps, improv/step: 0.203 (last = 0.1935), fitness=0.006165264
81.86 secs, 4529 evals, 4325 steps, improv/step: 0.203 (last = 0.1200), fitness=0.006165264
82.37 secs, 4554 evals, 4350 steps, improv/step: 0.203 (last = 0.2000), fitness=0.006165264
82.88 secs, 4579 evals, 4375 steps, improv/step: 0.202 (last = 0.1200), fitness=0.006165264
83.39 secs, 4613 evals, 4409 steps, improv/step: 0.202 (last = 0.1765), fitness=0.006133581
83.91 secs, 4651 evals, 4446 steps, improv/step: 0.201 (last = 0.1351), fitness=0.006133581
84.41 secs, 4691 evals, 4486 steps, improv/step: 0.200 (last = 0.1000), fitness=0.006133581
84.92 secs, 4731 evals, 4525 steps, improv/step: 0.201 (last = 0.2564), fitness=0.006133581
85.42 secs, 4756 evals, 4550 steps, improv/step: 0.201 (last = 0.1600), fitness=0.005812309
85.94 secs, 4790 evals, 4583 steps, improv/step: 0.201 (last = 0.2121), fitness=0.005812309
86.45 secs, 4822 evals, 4615 steps, improv/step: 0.200 (last = 0.0313), fitness=0.005812309
86.96 secs, 4854 evals, 4645 steps, improv/step: 0.199 (last = 0.1667), fitness=0.005715130
87.47 secs, 4892 evals, 4683 steps, improv/step: 0.199 (last = 0.1579), fitness=0.005715130
87.99 secs, 4912 evals, 4703 steps, improv/step: 0.200 (last = 0.3500), fitness=0.005715130
88.49 secs, 4950 evals, 4741 steps, improv/step: 0.198 (last = 0.0526), fitness=0.005715130
88.99 secs, 4988 evals, 4778 steps, improv/step: 0.197 (last = 0.0541), fitness=0.005715130
89.52 secs, 5022 evals, 4812 steps, improv/step: 0.197 (last = 0.0882), fitness=0.003842075
90.02 secs, 5053 evals, 4843 steps, improv/step: 0.196 (last = 0.0323), fitness=0.003842075
90.53 secs, 5080 evals, 4870 steps, improv/step: 0.195 (last = 0.0741), fitness=0.003842075
91.03 secs, 5110 evals, 4900 steps, improv/step: 0.195 (last = 0.2000), fitness=0.003222240
91.54 secs, 5142 evals, 4932 steps, improv/step: 0.194 (last = 0.0938), fitness=0.003222240
92.05 secs, 5167 evals, 4957 steps, improv/step: 0.193 (last = 0.0400), fitness=0.003222240
92.55 secs, 5203 evals, 4993 steps, improv/step: 0.193 (last = 0.1944), fitness=0.003222240
93.06 secs, 5240 evals, 5029 steps, improv/step: 0.194 (last = 0.3056), fitness=0.003222240
93.57 secs, 5280 evals, 5069 steps, improv/step: 0.194 (last = 0.1000), fitness=0.003222240
94.08 secs, 5321 evals, 5110 steps, improv/step: 0.193 (last = 0.1220), fitness=0.003222240
94.59 secs, 5361 evals, 5150 steps, improv/step: 0.193 (last = 0.1500), fitness=0.003222240
95.09 secs, 5402 evals, 5191 steps, improv/step: 0.192 (last = 0.0976), fitness=0.003222240
95.60 secs, 5441 evals, 5230 steps, improv/step: 0.191 (last = 0.1282), fitness=0.003222240
96.10 secs, 5481 evals, 5270 steps, improv/step: 0.191 (last = 0.1750), fitness=0.002443829
96.60 secs, 5521 evals, 5308 steps, improv/step: 0.191 (last = 0.1842), fitness=0.002443829
97.11 secs, 5560 evals, 5347 steps, improv/step: 0.191 (last = 0.1795), fitness=0.002443829
97.62 secs, 5596 evals, 5382 steps, improv/step: 0.192 (last = 0.3429), fitness=0.002443829
98.13 secs, 5636 evals, 5419 steps, improv/step: 0.192 (last = 0.1622), fitness=0.002443829
98.64 secs, 5676 evals, 5459 steps, improv/step: 0.191 (last = 0.0750), fitness=0.002443829
99.14 secs, 5712 evals, 5494 steps, improv/step: 0.191 (last = 0.1714), fitness=0.002443829
99.64 secs, 5750 evals, 5532 steps, improv/step: 0.191 (last = 0.1842), fitness=0.001803434
100.15 secs, 5788 evals, 5569 steps, improv/step: 0.191 (last = 0.1351), fitness=0.001803434
100.65 secs, 5825 evals, 5605 steps, improv/step: 0.190 (last = 0.1389), fitness=0.001803434
101.16 secs, 5863 evals, 5641 steps, improv/step: 0.191 (last = 0.2500), fitness=0.001803434
101.66 secs, 5902 evals, 5680 steps, improv/step: 0.190 (last = 0.1026), fitness=0.001803434
102.17 secs, 5937 evals, 5714 steps, improv/step: 0.190 (last = 0.2059), fitness=0.001803434
102.68 secs, 5972 evals, 5748 steps, improv/step: 0.190 (last = 0.1765), fitness=0.001803434
103.18 secs, 6012 evals, 5788 steps, improv/step: 0.190 (last = 0.2000), fitness=0.001803434
103.69 secs, 6051 evals, 5826 steps, improv/step: 0.190 (last = 0.1316), fitness=0.001803434
104.19 secs, 6088 evals, 5862 steps, improv/step: 0.189 (last = 0.1111), fitness=0.001803434
104.70 secs, 6127 evals, 5901 steps, improv/step: 0.189 (last = 0.1795), fitness=0.001803434
105.21 secs, 6167 evals, 5940 steps, improv/step: 0.189 (last = 0.1538), fitness=0.001803434
105.71 secs, 6205 evals, 5977 steps, improv/step: 0.188 (last = 0.1081), fitness=0.001803434
106.22 secs, 6244 evals, 6015 steps, improv/step: 0.188 (last = 0.0789), fitness=0.001803434
106.72 secs, 6274 evals, 6045 steps, improv/step: 0.187 (last = 0.1000), fitness=0.001803434
107.23 secs, 6292 evals, 6063 steps, improv/step: 0.187 (last = 0.0000), fitness=0.001803434
107.74 secs, 6327 evals, 6098 steps, improv/step: 0.187 (last = 0.1714), fitness=0.001137763
108.27 secs, 6363 evals, 6134 steps, improv/step: 0.187 (last = 0.1667), fitness=0.001137763
108.77 secs, 6399 evals, 6170 steps, improv/step: 0.186 (last = 0.1667), fitness=0.001137763
109.28 secs, 6439 evals, 6210 steps, improv/step: 0.187 (last = 0.2500), fitness=0.001137763
109.79 secs, 6476 evals, 6247 steps, improv/step: 0.186 (last = 0.1081), fitness=0.001137763
110.29 secs, 6516 evals, 6287 steps, improv/step: 0.186 (last = 0.0750), fitness=0.001137763
110.80 secs, 6555 evals, 6326 steps, improv/step: 0.185 (last = 0.1026), fitness=0.001137763
111.32 secs, 6593 evals, 6364 steps, improv/step: 0.185 (last = 0.1842), fitness=0.001137763
111.82 secs, 6631 evals, 6402 steps, improv/step: 0.185 (last = 0.1316), fitness=0.001137763
112.32 secs, 6666 evals, 6437 steps, improv/step: 0.185 (last = 0.2286), fitness=0.001137763
112.84 secs, 6705 evals, 6476 steps, improv/step: 0.185 (last = 0.1282), fitness=0.001137763
113.35 secs, 6744 evals, 6514 steps, improv/step: 0.185 (last = 0.2368), fitness=0.001137763
113.85 secs, 6784 evals, 6553 steps, improv/step: 0.185 (last = 0.1795), fitness=0.001137763
114.37 secs, 6823 evals, 6592 steps, improv/step: 0.185 (last = 0.1282), fitness=0.001137763
114.88 secs, 6862 evals, 6631 steps, improv/step: 0.185 (last = 0.2308), fitness=0.001036962
115.38 secs, 6898 evals, 6667 steps, improv/step: 0.184 (last = 0.0556), fitness=0.001036962
115.88 secs, 6936 evals, 6705 steps, improv/step: 0.184 (last = 0.0789), fitness=0.001036962
116.39 secs, 6976 evals, 6745 steps, improv/step: 0.184 (last = 0.1750), fitness=0.001036962
116.89 secs, 7016 evals, 6785 steps, improv/step: 0.184 (last = 0.2000), fitness=0.001036962
117.40 secs, 7055 evals, 6824 steps, improv/step: 0.183 (last = 0.0513), fitness=0.001036962
117.91 secs, 7096 evals, 6865 steps, improv/step: 0.183 (last = 0.1220), fitness=0.001011167
118.42 secs, 7136 evals, 6905 steps, improv/step: 0.182 (last = 0.1500), fitness=0.001011167
118.93 secs, 7175 evals, 6944 steps, improv/step: 0.182 (last = 0.0769), fitness=0.001011167
119.43 secs, 7212 evals, 6981 steps, improv/step: 0.181 (last = 0.1081), fitness=0.000511087
119.93 secs, 7251 evals, 7020 steps, improv/step: 0.181 (last = 0.0513), fitness=0.000511087
120.43 secs, 7276 evals, 7045 steps, improv/step: 0.180 (last = 0.0800), fitness=0.000511087
120.96 secs, 7309 evals, 7078 steps, improv/step: 0.179 (last = 0.0000), fitness=0.000511087
121.46 secs, 7339 evals, 7108 steps, improv/step: 0.179 (last = 0.0333), fitness=0.000511087
121.96 secs, 7375 evals, 7144 steps, improv/step: 0.178 (last = 0.0833), fitness=0.000511087
122.47 secs, 7414 evals, 7183 steps, improv/step: 0.178 (last = 0.1795), fitness=0.000511087
122.98 secs, 7455 evals, 7224 steps, improv/step: 0.178 (last = 0.1463), fitness=0.000511087
123.49 secs, 7495 evals, 7264 steps, improv/step: 0.178 (last = 0.1750), fitness=0.000511087
123.99 secs, 7533 evals, 7302 steps, improv/step: 0.178 (last = 0.2105), fitness=0.000511087
124.50 secs, 7573 evals, 7342 steps, improv/step: 0.178 (last = 0.0750), fitness=0.000511087
125.01 secs, 7613 evals, 7382 steps, improv/step: 0.177 (last = 0.0750), fitness=0.000511087
125.51 secs, 7651 evals, 7420 steps, improv/step: 0.177 (last = 0.0789), fitness=0.000511087
126.02 secs, 7688 evals, 7457 steps, improv/step: 0.176 (last = 0.0811), fitness=0.000511087
126.53 secs, 7715 evals, 7484 steps, improv/step: 0.176 (last = 0.0370), fitness=0.000511087
127.04 secs, 7746 evals, 7515 steps, improv/step: 0.175 (last = 0.0968), fitness=0.000511087
127.55 secs, 7771 evals, 7540 steps, improv/step: 0.175 (last = 0.1200), fitness=0.000511087
128.05 secs, 7806 evals, 7575 steps, improv/step: 0.175 (last = 0.0571), fitness=0.000511087
128.55 secs, 7846 evals, 7615 steps, improv/step: 0.174 (last = 0.1000), fitness=0.000475263
129.07 secs, 7881 evals, 7650 steps, improv/step: 0.174 (last = 0.1143), fitness=0.000475263
129.57 secs, 7917 evals, 7686 steps, improv/step: 0.173 (last = 0.0556), fitness=0.000475263
130.08 secs, 7956 evals, 7725 steps, improv/step: 0.173 (last = 0.0769), fitness=0.000475263
130.58 secs, 7993 evals, 7762 steps, improv/step: 0.173 (last = 0.0811), fitness=0.000475263
131.09 secs, 8033 evals, 7802 steps, improv/step: 0.172 (last = 0.0750), fitness=0.000475263
131.59 secs, 8070 evals, 7839 steps, improv/step: 0.172 (last = 0.1081), fitness=0.000475263
132.10 secs, 8093 evals, 7862 steps, improv/step: 0.172 (last = 0.1739), fitness=0.000422055
132.60 secs, 8125 evals, 7894 steps, improv/step: 0.171 (last = 0.0938), fitness=0.000422055
133.10 secs, 8163 evals, 7932 steps, improv/step: 0.171 (last = 0.1316), fitness=0.000326960
133.62 secs, 8201 evals, 7970 steps, improv/step: 0.171 (last = 0.1053), fitness=0.000326960
134.12 secs, 8242 evals, 8011 steps, improv/step: 0.171 (last = 0.1463), fitness=0.000326960
134.63 secs, 8281 evals, 8050 steps, improv/step: 0.170 (last = 0.0256), fitness=0.000326960
135.14 secs, 8321 evals, 8090 steps, improv/step: 0.170 (last = 0.0750), fitness=0.000326960
135.64 secs, 8358 evals, 8127 steps, improv/step: 0.169 (last = 0.0270), fitness=0.000326960
136.15 secs, 8395 evals, 8165 steps, improv/step: 0.169 (last = 0.1579), fitness=0.000326960
136.66 secs, 8433 evals, 8203 steps, improv/step: 0.169 (last = 0.1053), fitness=0.000326960
137.17 secs, 8472 evals, 8242 steps, improv/step: 0.169 (last = 0.2051), fitness=0.000326960
137.67 secs, 8510 evals, 8280 steps, improv/step: 0.168 (last = 0.0789), fitness=0.000326960
138.18 secs, 8548 evals, 8318 steps, improv/step: 0.168 (last = 0.1053), fitness=0.000326960
138.69 secs, 8583 evals, 8353 steps, improv/step: 0.168 (last = 0.0571), fitness=0.000326960
139.21 secs, 8613 evals, 8383 steps, improv/step: 0.167 (last = 0.1000), fitness=0.000326960
139.72 secs, 8638 evals, 8408 steps, improv/step: 0.168 (last = 0.2400), fitness=0.000326960
140.23 secs, 8663 evals, 8433 steps, improv/step: 0.167 (last = 0.1200), fitness=0.000326960
140.73 secs, 8700 evals, 8470 steps, improv/step: 0.167 (last = 0.1081), fitness=0.000275498
141.24 secs, 8728 evals, 8498 steps, improv/step: 0.167 (last = 0.1071), fitness=0.000275498
141.74 secs, 8766 evals, 8536 steps, improv/step: 0.167 (last = 0.0789), fitness=0.000270849
142.24 secs, 8806 evals, 8576 steps, improv/step: 0.166 (last = 0.1000), fitness=0.000261780
142.75 secs, 8843 evals, 8613 steps, improv/step: 0.166 (last = 0.0541), fitness=0.000261780
143.26 secs, 8879 evals, 8649 steps, improv/step: 0.166 (last = 0.1111), fitness=0.000261780
143.76 secs, 8916 evals, 8686 steps, improv/step: 0.165 (last = 0.0811), fitness=0.000261780
144.27 secs, 8953 evals, 8723 steps, improv/step: 0.165 (last = 0.1622), fitness=0.000261780
144.78 secs, 8994 evals, 8764 steps, improv/step: 0.165 (last = 0.0976), fitness=0.000261780
145.28 secs, 9031 evals, 8801 steps, improv/step: 0.165 (last = 0.1081), fitness=0.000227520
145.79 secs, 9068 evals, 8838 steps, improv/step: 0.165 (last = 0.1892), fitness=0.000227520
146.30 secs, 9107 evals, 8877 steps, improv/step: 0.165 (last = 0.1795), fitness=0.000227520
146.81 secs, 9146 evals, 8916 steps, improv/step: 0.165 (last = 0.1282), fitness=0.000227520
147.33 secs, 9183 evals, 8953 steps, improv/step: 0.165 (last = 0.1351), fitness=0.000227520
147.83 secs, 9222 evals, 8992 steps, improv/step: 0.164 (last = 0.1026), fitness=0.000227520
148.34 secs, 9257 evals, 9027 steps, improv/step: 0.164 (last = 0.1429), fitness=0.000227520
148.85 secs, 9295 evals, 9065 steps, improv/step: 0.164 (last = 0.1842), fitness=0.000227520
149.35 secs, 9325 evals, 9095 steps, improv/step: 0.165 (last = 0.2667), fitness=0.000227520
149.87 secs, 9343 evals, 9113 steps, improv/step: 0.165 (last = 0.1667), fitness=0.000227520
150.38 secs, 9382 evals, 9152 steps, improv/step: 0.165 (last = 0.2051), fitness=0.000227520
150.88 secs, 9419 evals, 9189 steps, improv/step: 0.165 (last = 0.1351), fitness=0.000134806
151.40 secs, 9459 evals, 9229 steps, improv/step: 0.165 (last = 0.1500), fitness=0.000134806
151.90 secs, 9497 evals, 9267 steps, improv/step: 0.165 (last = 0.2105), fitness=0.000134806
152.41 secs, 9526 evals, 9296 steps, improv/step: 0.165 (last = 0.1034), fitness=0.000134806
152.92 secs, 9558 evals, 9328 steps, improv/step: 0.164 (last = 0.0938), fitness=0.000134806
153.44 secs, 9589 evals, 9359 steps, improv/step: 0.164 (last = 0.1613), fitness=0.000134806
153.95 secs, 9630 evals, 9400 steps, improv/step: 0.164 (last = 0.0976), fitness=0.000134806
154.46 secs, 9670 evals, 9440 steps, improv/step: 0.164 (last = 0.1750), fitness=0.000134806
154.96 secs, 9710 evals, 9480 steps, improv/step: 0.164 (last = 0.0750), fitness=0.000134806
155.47 secs, 9748 evals, 9518 steps, improv/step: 0.164 (last = 0.1842), fitness=0.000122375
155.97 secs, 9788 evals, 9558 steps, improv/step: 0.164 (last = 0.2000), fitness=0.000122375
156.48 secs, 9823 evals, 9593 steps, improv/step: 0.164 (last = 0.1429), fitness=0.000122375
156.98 secs, 9853 evals, 9623 steps, improv/step: 0.164 (last = 0.1000), fitness=0.000122375
157.49 secs, 9889 evals, 9659 steps, improv/step: 0.164 (last = 0.1944), fitness=0.000122375
157.99 secs, 9927 evals, 9697 steps, improv/step: 0.164 (last = 0.1053), fitness=0.000122375
158.50 secs, 9966 evals, 9736 steps, improv/step: 0.163 (last = 0.1282), fitness=0.000122375
159.01 secs, 9997 evals, 9767 steps, improv/step: 0.163 (last = 0.0000), fitness=0.000122375
159.52 secs, 10030 evals, 9800 steps, improv/step: 0.163 (last = 0.1212), fitness=0.000122375
160.02 secs, 10066 evals, 9836 steps, improv/step: 0.163 (last = 0.1111), fitness=0.000122375
160.54 secs, 10092 evals, 9862 steps, improv/step: 0.162 (last = 0.1154), fitness=0.000122375
161.05 secs, 10130 evals, 9900 steps, improv/step: 0.162 (last = 0.0526), fitness=0.000122375
161.55 secs, 10168 evals, 9938 steps, improv/step: 0.162 (last = 0.1842), fitness=0.000122375
162.06 secs, 10196 evals, 9967 steps, improv/step: 0.162 (last = 0.1034), fitness=0.000122375
162.57 secs, 10222 evals, 9994 steps, improv/step: 0.162 (last = 0.2222), fitness=0.000122375

Optimization stopped after 10001 steps and 162.68 seconds
Termination reason: Max number of steps (10000) reached
Steps per second = 61.48
Function evals per second = 62.88
Improvements/step = 0.16200
Total function evaluations = 10229


Best candidate found: [0.300088, 0.884932, 1.91793, 2.00462, 0.176313, 0.149841]

Fitness: 0.000122375







    Out[59]:





BlackBoxOptim.OptimizationResults("adaptive_de_rand_1_bin_radiuslimited", "Max number of steps (10000) reached", 10001, 1.575553392093e9, 162.67700004577637, DictChain{Symbol,Any}[DictChain{Symbol,Any}[Dict{Symbol,Any}(:RngSeed => 123340,:SearchRange => Tuple{Float64,Float64}[(0.1, 1.0), (0.1, 1.0), (1.5, 4.0), (1.5, 4.0), (0.05, 0.75), (0.1, 0.25)],:MaxSteps => 10000),Dict{Symbol,Any}()],Dict{Symbol,Any}(:FitnessScheme => ScalarFitnessScheme{true}(),:NumDimensions => :NotSpecified,:PopulationSize => 50,:MaxTime => 0.0,:SearchRange => (-1.0, 1.0),:Method => :adaptive_de_rand_1_bin_radiuslimited,:MaxNumStepsWithoutFuncEvals => 100,:RngSeed => 1234,:MaxFuncEvals => 0,:SaveTrace => false…)], 10229, ScalarFitnessScheme{true}(), BlackBoxOptim.TopListArchiveOutput{Float64,Array{Float64,1}}(0.00012237477101998878, [0.3000879389994999, 0.8849316302413965, 1.9179287750262926, 2.004615109299689, 0.17631265498980495, 0.1498405834595154]), BlackBoxOptim.PopulationOptimizerOutput{FitPopulation{Float64}}(FitPopulation{Float64}([0.3000339416835903 0.30002906647951005 … 0.2998911551028697 0.29993991763311506; 0.8922196662948053 0.892058510286837 … 0.8792749212423211 0.9067223651925492; … ; 0.17357198071564747 0.1735773935440087 … 0.17616051098854635 0.16677236943590248; 0.1498452100777135 0.1498487501217811 … 0.15007343547042828 0.14996824795976602], NaN, [0.00013668261211567258, 0.0001397860339672444, 0.00013978409750330055, 0.00013794714212117396, 0.00013837269489241295, 0.00013026523021544224, 0.00015024741189376663, 0.00014023719543146805, 0.00024001981648822394, 0.0001505572161084212  …  0.00015395474293318495, 0.0002611751051952438, 0.00028411895403892607, 0.00012237477101998878, 0.00019254334049529883, 0.0002435448823413269, 0.0001684680937268337, 0.00013977438931719004, 0.0001468655452705475, 0.00013480596161303247], 0, BlackBoxOptim.Candidate{Float64}[BlackBoxOptim.Candidate{Float64}([0.29880480596327286, 0.8445267976742463, 1.9085755976959176, 1.9660824530997343, 0.17792437613166018, 0.15151718954482588], 31, 0.0012551877485361925, BlackBoxOptim.AdaptiveDiffEvoRandBin{3}(BlackBoxOptim.AdaptiveDiffEvoParameters(BlackBoxOptim.BimodalCauchy(Distributions.Cauchy{Float64}(μ=0.65, σ=0.1), Distributions.Cauchy{Float64}(μ=1.0, σ=0.1), 0.5, false, true), BlackBoxOptim.BimodalCauchy(Distributions.Cauchy{Float64}(μ=0.1, σ=0.1), Distributions.Cauchy{Float64}(μ=0.95, σ=0.1), 0.5, false, true), [0.6261902343049117, 0.843430030324217, 0.7368513966736043, 0.8053251663742037, 0.6132221847594711, 0.6590810072492729, 0.43675730564325244, 1.0, 0.5743413379301606, 0.9435882694146726  …  0.6697714717834575, 0.624161774094745, 0.8398865119913571, 0.014342104655024612, 0.8968615241746991, 0.6035776095780596, 0.7411122520255475, 0.9443708040449552, 0.7921103003231158, 1.0], [0.9456811115754405, 0.16726356261367958, 0.9628749404102642, 0.43511585774242223, 1.0, 0.8057657192711437, 0.988182384005621, 0.17156821784655238, 0.03542870659005727, 0.2631305569807043  …  0.5215667253076903, 1.0, 0.9782741627907142, 0.9683382007890636, 1.0, 0.9524326061315721, 1.0, 0.9080279128169688, 1.0, 0.8845681560810993])), 0), BlackBoxOptim.Candidate{Float64}([0.3004005526728503, 0.8445267976742463, 1.8955480688572144, 2.0149419929072363, 0.1818384620967649, 0.14924960629525447], 31, 0.010686224053907168, BlackBoxOptim.AdaptiveDiffEvoRandBin{3}(BlackBoxOptim.AdaptiveDiffEvoParameters(BlackBoxOptim.BimodalCauchy(Distributions.Cauchy{Float64}(μ=0.65, σ=0.1), Distributions.Cauchy{Float64}(μ=1.0, σ=0.1), 0.5, false, true), BlackBoxOptim.BimodalCauchy(Distributions.Cauchy{Float64}(μ=0.1, σ=0.1), Distributions.Cauchy{Float64}(μ=0.95, σ=0.1), 0.5, false, true), [0.6261902343049117, 0.843430030324217, 0.7368513966736043, 0.8053251663742037, 0.6132221847594711, 0.6590810072492729, 0.43675730564325244, 1.0, 0.5743413379301606, 0.9435882694146726  …  0.6697714717834575, 0.624161774094745, 0.8398865119913571, 0.014342104655024612, 0.8968615241746991, 0.6035776095780596, 0.7411122520255475, 0.9443708040449552, 0.7921103003231158, 1.0], [0.9456811115754405, 0.16726356261367958, 0.9628749404102642, 0.43511585774242223, 1.0, 0.8057657192711437, 0.988182384005621, 0.17156821784655238, 0.03542870659005727, 0.2631305569807043  …  0.5215667253076903, 1.0, 0.9782741627907142, 0.9683382007890636, 1.0, 0.9524326061315721, 1.0, 0.9080279128169688, 1.0, 0.8845681560810993])), 0)])))



In [60]:

    
x_init = best_candidate(res)
vis_error(x_init, exp_data1, core_props, fluids_ls, fluids_hs, rel_perms_hs, core_flood)
error_calc(x_init, exp_data1, core_props, fluids_ls, fluids_hs, rel_perms_hs, core_flood)









    












    












    Out[60]:





8.03717989215052e-5