This package is being designed among others to provide Petroleum Engineering tools in a modern programming language. This package is part of the project 7G which proposes to make basic but powerful engineering software packages that cover the main topics of the Oil and Gas development phases which could be applied to any case study by suitable engineers.
There are five topics in which the project is going to be focused on:
-Geoscience* (Current Package)
-Reservoir
-Production
-Economics
-Integration
The package will always be in permanent development and open to suggestions to enhance the program. As the code has been written so far by a code enthusiastic Petroleum Engineer I hope to learn as much as possible to get better and usefull programs.
WellLogs.jl is a package to visualize Oil And Gas Well logs in order to make interpretetion and petrophysics analysis via PyPlot plots.
The visualization of logs is based on Tracks functions. Each log track is implemented through a single function that plot the desired data. For example, the Lithology Track is composed generally by GammaRay and Sp logs, so LithoTrack function can plot the GammaRay and SP logs with optional features like add Well tops (Formations or Units), Gr clean and Gr shale to Vshale estimations, etc...
The visualization of logs is customizable through the use of Subplots to show any track as well as the use Interact.jl to add interactivity, mainly depth limits.
Load Packages required:
using Interact
using PyPlot
using CSV
using Colors
using DataFrames
using Statistics
using Query
using Interpolations
using Distributions
using WellLogsThe logs, perforations and Formation and Tops units, in this case, are stored in CSV files which are loaded:
Logs=CSV.read("~\\Logs.csv")
Fms=CSV.read("~\\FormationTops.csv")
Perfs=CSV.read("~\\Perforations.csv")
Units=CSV.read("~\\UnitsTops.csv")Logs Example
| WellId | Md | GammaRay | Sp |
|---|---|---|---|
| 1 | 0 | 123 | 345 |
| ... | ... | 678 | 987 |
| 2 | 6839 | 843 | 548 |
@manipulate for from=9000, to=10180
fig, axes=subplots(1,3,figsize=(10,5))
withfig(fig,clear=false) do
for ax in axes
ax.cla()
end
subplot(131)
LithoTrack(Logs.Md,Logs.GammaRay,SponPot=Logs.SP,DepthFrom=from,DepthTo=to,WellUnit=Units[:,[:MdTop,:MdBottom]],UnitAlpha=0.5)
subplot(132)
PoroTrack(Logs.Md,Logs.Density,Logs.NeutronSand,DepthFrom=from,DepthTo=to)
subplot(133)
ResTrack(Logs.Md,Res=[Logs.ShallowRes Logs.MediumRes Logs.DeepRes],DepthFrom=from,DepthTo=to)
end
end
To estimate basic Petrophysics calculations on the current DataFrame log, AddColPetro function add either all or specific columns of zeros to log DataFrame.
Logs=AddColPetro(Logs,All=true)
names(Logs)
25-element Array{Symbol,1}:
:WellId
:Md
:Tvd
:Tvdss
:GammaRay
:SP
:NeutronSand
:Density
:MicroRes
:ShallowRes
:MediumRes
:DeepRes
:Sonic
:Caliper
:Pe
:Bit
:Dcor
:NeutronLime
:Vsh #(Added)
:Phie #(Added)
:Sw #(Added)
:Perm #(Added)
:PayFlag #(Added)
:Kh #(Added)
:DenPhi #(Added)Once the columns required are created, it is estimated the petrophysics calculation by PetroPhysics function
Logs=PetroPhysics(Logs,9100,9300,Vsh=[26,178],DenPhi=[2.65,1],Phie=true,Sw=[0.62,2.15,2,0.5, Logs.Phie, Logs.DeepRes],Perm=["Oil","Timur"], PayFlag=[0.5,0.1,0.5,10], Kh=true);| Parameter | Description |
|---|---|
| DepthFrom,DepthTo; | #range of depth to calculate Petrophysics |
| Vsh | #If caculate Vshale [GrSand, GrShale] |
| DenPhi | #If caculate Porosity from Density Log [RhoMatrix, RhoFluid] |
| Phie | #If caculate Efective Porosity. It is requiered Vsh, DenPhi, and Neutron |
| Sw | #If caculate Water Saturation. It is requiered Phie and DeepRes and Archie Parameters[a,m,n,Rw] |
| Perm | #If caculate Permeability). Phie, Sw, Fluid, Author [Fluid, Author] |
| PayFlag | #If calculate PayFlag. It is requiered Vsh,Phie,Sw,Perm [VshCutoff,PhieCutOff,SwCutoff,KCutOff] |
| Kh | #If requiered Flow Capacity percentage |
Then you can plot the results within an interactive plot:
@manipulate for from=9100, to=9300, grsand=16, grshale=178
fig, axes=subplots(1,7,figsize=(15,5))
withfig(fig,clear=false) do
for ax in axes
ax.cla()
end
subplot(171)
LithoTrack(Logs.Md,Logs.GammaRay,SponPot=Logs.SP,DepthFrom=from,DepthTo=to,
Pay=Logs.PayFlag,GRSand=[grsand,from,to],GRShale=[grshale,from,to], GRMax=200)
subplot(172)
VshTrack(Logs.Md,Logs.Vsh,DepthFrom=from,DepthTo=to,PhiePlot=Logs.Phie)
subplot(173)
PoroTrack(Logs.Md,Logs.Density,Logs.NeutronSand,DepthFrom=from,DepthTo=to)
subplot(174)
ResTrack(Logs.Md,Res=[Logs.R20O Logs.R30O Logs.R40O Logs.R85O Logs.RTAO],DepthFrom=from,DepthTo=to)
subplot(175)
SwTrack(Logs.Md,Logs.Sw,DepthFrom=from,DepthTo=to)
subplot(176)
PermTrack(Logs.Md,Logs.Perm,DepthFrom=from,DepthTo=to)
subplot(177)
KhTrack(Logs.Md,Logs.KhCum,DepthFrom=from,DepthTo=to)
end
end
You can make both structural and stratigraphic correlations.
from=2010
to=2780
c=2223
fig, axes=subplots(1,10,figsize=(16,12))
subplot(1,10,1)
LithoTrack(wLogs.Tvdss,wLogs.GammaRay,DepthFrom=from,DepthTo=to,GRMax=200,
WellUnit=wUnits[:,[:TvdssTop,:TvdssBottom]],
LineCorrelate=c,Dtick=false)
subplot(1,10,2)
LithoTrack(C2Logs.Tvdss,C2Logs.GammaRay,DepthFrom=from,DepthTo=to,GRMax=200,
WellUnit=C2Units[:,[:TvdssTop,:TvdssBottom]],
LineCorrelate=c, Dtick=false)
subplot(1,10,3)
LithoTrack(C1Logs.Tvdss,C1Logs.GammaRay,DepthFrom=from,DepthTo=to,GRMax=200,
WellUnit=C1Units[:,[:TvdssTop,:TvdssBottom]],
LineCorrelate=c, Dtick=false)
subplot(1,10,4)
LithoTrack(C6Logs.Tvdss,C6Logs.GammaRay,DepthFrom=from,DepthTo=to,GRMax=200,
WellUnit=C6Units[:,[:TvdssTop,:TvdssBottom]],
LineCorrelate=c, Dtick=false)
subplot(1,10,5)
LithoTrack(C5Logs.Tvdss,C5Logs.GammaRay,DepthFrom=from,DepthTo=to,GRMax=200,
WellUnit=C5Units[:,[:TvdssTop,:TvdssBottom]],
LineCorrelate=c, Dtick=false)
subplot(1,10,6)
LithoTrack(C7Logs.Tvdss,C7Logs.GammaRay,DepthFrom=from,DepthTo=to,GRMax=200,
WellUnit=C7Units[:,[:TvdssTop,:TvdssBottom]],
LineCorrelate=c, Dtick=false)
subplot(1,10,7)
LithoTrack(C4Logs.Tvdss,C4Logs.GammaRay,DepthFrom=from,DepthTo=to,GRMax=200,
WellUnit=C4Units[:,[:TvdssTop,:TvdssBottom]],
LineCorrelate=c, Dtick=false)
subplot(1,10,8)
LithoTrack(C3Logs.Tvdss,C3Logs.GammaRay,DepthFrom=from,DepthTo=to,GRMax=200,
WellUnit=C3Units[:,[:TvdssTop,:TvdssBottom]],
LineCorrelate=c, Dtick=false)
subplot(1,10,9)
LithoTrack(C8Logs.Tvdss,C8Logs.GammaRay,DepthFrom=from,DepthTo=to,GRMax=200,
WellUnit=C8Units[:,[:TvdssTop,:TvdssBottom]],
LineCorrelate=c, Dtick=false)
subplot(1,10,10)
LithoTrack(C9Logs.Tvdss,C9Logs.GammaRay,DepthFrom=from,DepthTo=to,GRMax=200,
WellUnit=C9Units[:,[:TvdssTop,:TvdssBottom]],
LineCorrelate=c, Dtick=false)
A picket plot can be added by calling PickettPlot function
function PickettPlot(Rt,Phie,Rw; #Must provide Rt, Phie and Rw
a=1,m=2,n=2, #Default values for Archie equation
Sw=range(0.2,stop=1,length=5), #Default Sw range to plot
WellName="-")
.....................
PickettPlot(LC1.DeepRes,LC1.Phie,0.8)
You can plot the distancew between points giving x, y and optionally z coordinates.
xs=[20.,35.,3.]
ys=[45.,25.,25.]
n=["Point A","Point B","Point C"]
topdistance(xs,ys, Names=n, title="All Distances Between Three Points", xlim=(0,50), ylim=(15, 60))
You can choose only one point to see all distances with respect of it.
For example, to plot distances with respect the second point ("Point B") just add Show=2
topdistance(xs,ys, Names=n, Show=2, title="All Distances Between Three Points", xlim=(0,50), ylim=(15, 60))
You can make spatial interpolations and plot the results in a contour map using idwi (Inverse Distance Weight Interpolation)
x=rand(collect(1.:50.),10)
y=rand(collect(1.:50.),10)
z=rand(collect(100.:500.),10)
idwi(x,y,z, title="Contour map by Inverse Weight Interpolation")
You can modify the grids to interpolate as well as the exponent of the idwi method. Besides, you can combine the distance plot with a contour plot.
x=rand(collect(1.:30.),10)
y=rand(collect(1.:30.),10)
z=rand(collect(100.:500.),10)
xs=[5.,15.,20.]
ys=[10.,20.,27.]
n=["Point A","Point B","Point C"]
idwi(x,y,z, n=100, m=100, p=2,title="All Distances Between Three Points and Contour Map", seriescolor = :heat)
topdistance!(xs,ys, Names=n, Show=2)
You can perform Volumetrics Calulations both Deterministic and Probabilistic.
From the Well Logs, you can estimate the probability distribution of certain property whose parameters can be used as input for volumetrics calculations.
For example
The Probability distribution of any variable can be estimate using fit function of Distrubition.jl Package.
PhieDistribution=fit(Normal,Logs.Phie.*1)
SwDistribution=fit(LogNormal,Logs.Sw.*1)It also can be plotted
p1=prophist(Logs.Phie,Normal,seriescolor= :darkred, seriesalpha=:0.2, title="Porosity")
p2=prophist(Logs.Sw,LogNormal,seriescolor= :darkblue, seriesalpha=:0.2,title="Water Saturation")
p=Plots.plot(p1,p2, layout=(2,1))
Once you have the some distribution for your properties, you can estimate probabilistically volumetrics calculations with P10, P50 and P90 Percentiles.
OGIP(Area=458,Height=26.3, Pres=3940, Temp=180,z=0.99,PhiDist=PhieDistribution,SwDist=SwDistribution,DisHist=true)
OOIP(Area=458,Height=26.3, Bo=1.2 ,PhiDist=Normal(0.1,0.02),Sw=0.55, Perc=[0.2 0.6 0.8], DisHist=true)
Deterministic calculations can also be performed as well
OOIP(Area=458,Height=26.3, Bo=1.2 ,Phi=0.07,Sw=0.55)
OGIP(Area=500,Height=20, Bg=0.005812 ,Phi=0.18,Sw=0.2 )
-Crain's Petrophysical Handbook - Site Summary / Home Page, www.spec2000.net/index.htm.
(n.d.). Retrieved from https://www.spec2000.net/index.htm
-Ahmed, T. H. (2019). Reservoir engineering handbook. Gulf Professional Publishing, an imprint of Elsevier.
-Rider, M. (n.d.). The geological interpretation of well logs.
-Djebbar Tiab and Erle C. Donaldson. (2016). Petrophysics (Fourth Edition).
-Armstrong, M. (1998). Basic linear geostatistics. Springer
-Pyrcz, M. J., & Deutsch, C. V. (2014). Geostatistical reservoir modeling. Oxford University Press.