Petrophysical Uncertainties Do They Really Matter? · Objectives of Petrophysics •To assess the presence of hydrocarbon accumulation in exploration areas •To provide input parameters

Petrophysical UncertaintiesDo They Really Matter?

A brief discussion on uncertainties

in petrophysical evaluations

Ko Ko Kyi

Retired Principal Petrophysicist

28 April 2019

Uncertainties in Resource Assessment

• Geophysical uncertainties

• Geological uncertainties

• Petrophysical uncertainties• Uncertainties in measurements

• Uncertainties in interpretation

Ref: Schlumberger Oilfield Review

Objectives of Petrophysics

• To assess the presence of hydrocarbon accumulation in exploration areas

• To provide input parameters for hydrocarbon resource assessment

• To confirm the presence of hydrocarbons and quantify their volumes in development wells

Hydrocarbon Resource Assessment

• Calculation of Hydrocarbon Initially In Place

HIIP = GBV x N/G x F x (1-Sw) x 1/FVF

Where, HIIP = hydrocarbon initially in place

GBV = gross bulk volume of rock

N/G = net to gross ratio

F = porosity

Sw = water saturation

FVF = formation volume factor

Hydrocarbon Volume Calculation

In the following equation:

HIIP = GBV x N/G x F x (1-Sw) x 1/FVF

Three main parameters, namely N/G, F and Sw are provided by petrophysicists

Other important parameters provided by petrophysicists are fluid type, fluid contacts and permeability estimates

Effects of Uncertainties in Parameters on Resource Assessment

Definitions of Petrophysical Parameters

• Net to Gross = Net Thickness/Gross Thickness

• Porosity = (Total Pore Volume)/(Total Rock Volume)

• Sw = (Water filled porosity)/(Total porosity)

Petrophysical Definitions

• Gross Interval Thickness

Base of Interval minus Top of Interval

• Gross Sand Thickness

The sum of all thicknesses in the Sand Unit , meeting a certain clay volume Vcl cutoff criterion

• Net Sand Thickness

The sum of all thicknesses in the Gross Sand,

meeting a certain porosity F cutoff criterion

• Net Pay Thickness

The sum of all thicknesses in the Net Sand, meeting

a certain water saturation Sw cutoff criterion

Petrophysical Dependencies

• Gross Sand thickness is dependent on clay volume Vcl cutoff

• Net Sand thickness is dependent on clay volume Vcl and porosity F cutoffs

• Net Pay thickness is dependent on clay volume Vcl , porosity F and water saturation Sw cutoffs

Petrophysical Interdependencies

• Net to gross ratio N/G, net sand, net pay, average porosity and average water saturation are all inter-related

• A change in one parameter, e.g. net sand, results in changes in other related parameters, e.g. average porosity and average water saturation of the net sand

Measurement Uncertainties

Well Logs

• Errors in depth measurements due to cable wear, stretching, reduction in cable diameter, sticky hole, etc.

• Errors in depth due to pipe stretching, pipe tally for MWD/LWD

• Errors in True Vertical Depth conversion using survey data and different methods of conversion

• Errors in tool calibrations

• Intrinsic tool errors, due to tool limitations

• Algorithms used to convert measured raw data into output data

• Hostile borehole environment for logging tools



Depth Measurement Accuracy

• Both drill pipe and wireline cable suffer from stretch and inaccuracies in measuring well depth

• This uncertainty becomes greater with increasing depth

and well deviation

• For a vertical well of 3500 m depth, measured depth at TD

has an accuracy of about 2 meters

• For a deviated well of 3500 m depth and 50 degrees deviation at bottom, the inaccuracy may increase to about

5 meters

• Well surveys measure borehole deviation and azimuth and produce X and Y lateral displacements only, they do not provide absolute vertical depth measurements

Well Deviation Survey Accuracy

• Magnetic Survey Tools - typical lateral borehole uncertainties;

14 m per 1000 m in a vertical well and 20 m per 1000 m in a

70 degrees deviated well

• Gyro Survey Tools – typical lateral borehole uncertainties:

1.5 m per 1000 m in a vertical well and 8 m per 1000 m in a

70 degrees deviated well

• FINDS (Schlumberger) – utilizes highly accurate accelerometers and double integrate the accelerations to determine absolute distance moved by tool during survey:

accuracy of 0.5 m per 1000 m regardless of deviation

• There may be more accurate systems which have become commercial since this presentation was made.

Borehole Position Uncertainty

Effects of Borehole Position Uncertainty

• Well Safety – drilling of relief well to intercept another well;

well collision avoidance in densely drilled areas

• Mapping – uncertainties in true vertical depths of reservoir horizons can lead to serious errors in maps; errors in fluid contacts may lead to wrong judgments on faults

• Geosteering – accurate measurement of TVD is required

• Pressure/gradient determination – requires accurate TVD

• Legal implications – accurate TVD to drill within national boundaries; unitization and equity determination issues

Mitigating Factors

Depth Measurements

• Logging contractors use best industrial practices to correct for depth measurement errors due to cable and pipe stretch

• Although absolute depth measurements have intrinsic errors, relative depths are reasonably accurate

• It is a prudent practice to incorporate uncertainties in depth measurements when computing hydrocarbon volumes

Petrophysical Parameters

Shale Volume Vsh

• Shale volume cutoff is used to determine gross sand

• Sensitive to method of calculation used

• Several methods of computing shale volume

• Simplest and easiest method to compute Vsh is from GR log

Vsh = (GR – GRmin)/(GRmax – GRmin)

Where, GR = Gamma Ray log reading

GRmax= Maximum GR log reading

GRmin = Minimum GR log reading



Parameters for Shale Volume Simulation

Vsh Simulation Summary

Vsh Frequency Distribution

Vsh Cumulative Distribution


Porosity F

• Porosity cutoff is used to determine Net Sand

• Several methods of computing porosity from logs

• Preferred method is to compute porosity using density log

F = (rb – rma)/(rf – rma)

Where, rb = bulk density of rock

rma = matrix density

rf = fluid density

Parameters for Porosity Simulation

Porosity Simulation Summary

Porosity Frequency Distribution

Porosity Cumulative Distribution

Porosity Sensitivity Analysis Results


Water saturation Sw

• Sw cutoff is used to determine Net Pay

• Several models can be used to compute Sw

• Simplest model is Archie’s equation for clean sand

Swn = (aRw)/(Fm x Rt)

Where, n = saturation exponent

a = Archie’s constant

m = cementation exponent

Rw= resistivity of formation water

Rt= true resistivity of formation

F = formation porosity

Parameters for Sw Simulation

Sw Simulation Summary

Sw Frequency Distribution

Sw Cumulative Distribution

Sw Sensitivity Analysis Results

Input Parameters for Sw Modelling

Tornado Chart for Sw Computation

Frequency Distribution of Modelled Sw



Measurement Uncertainties

Core Data

• Alteration of formation properties due to dissimilar saturantproperties

• Measurements done under laboratory conditions may not truly represent those at reservoir conditions

• Basic assumptions used in core measurements may not truly represent the actual formation properties

• Errors in best guess estimation of confining pressures used in measurements simulating overburden condition

• Errors in actual measurements

Uncertainties in Core Analysis

Results of study by the Society of Core Analysts

• Only four out of 17 core analysis laboratories mixed the correct brine solution with required salinity of 100,000 ppm NaCl

• Even when provided with premixed brine, laboratories reported different resistivity values

• The saturation exponent n and cementation exponent m, obtained using different methods, show a fair amount of scatter, between 1.6 to 2.1

Ref: Pierre Berger, Schlumberger



Sensitivity Analysis for m and n

To study the effects of errors in m and n values on water saturation determination, Archie’s equation can be expressed as follows for changes in m and n:

(DSw/Sw)m = -(Dm/n) * (ln F) keeping n constant

(DSw/Sw)n = -(Dn/n) * (ln Sw) keeping m constant

The effects of changes of m and n values on Sw are shown on the following plots


The effect of rock porosity as relative error in water saturation determination

(DSw/Sw) caused by relative deviations in cementation exponent (Dm/n)


The effect of water saturation as relative error in water saturation determination

(DSw/Sw) caused by relative deviations in saturation exponent (Dn/n)


Effects of m and n values

Effects of the changes in m and n values

• An exact knowledge of m and n values are crucial for the determination of water saturation Sw

• The biggest error in saturation determination is likely to come from inaccurate estimate of m and n values

• The effect becomes worse in shaly formations as there is an apparent decrease in the slope of the Resistivity Index (I) to Sw response

• There can be several inaccuracies in the m and n values measured in laboratories


residual oil

Handling Uncertainties

• Recognize the existence of uncertainties• There are no unique solutions in petrophysics

• Identify major uncertainties and their effects• Determine which uncertainties have biggest effects

• Do sensitivity analysis on the impact of uncertainties

• Mitigate uncertainty effects by using statistics• Provide range of values for petrophysical parameters to

account for various uncertainties

Recommended Practice

For single well average parameter

• Find average value (e.g. FAVG ) and standard deviation ( s ) of the parameter of interest for the well

• Uncertainty of parameter = 2 x s

• Minimum value FMIN = FAVG - (2 x s)

• Maximum value FMAX = FAVG + (2 x s)

Recommended Practice

For multi well average parameter in a field

• Find average value (e.g. POR) and standard deviation (PORSD) of the parameter for the wells (n) in the field

• Uncertainty (UNC) in POR = SQRT((PORSD)2 / (n + s2))

• Minimum value PORMIN = POR - (2 x UNC)

• Maximum value PORMAX = POR + (2 x UNC)

Some Common Practices

Most companies handle uncertainties in petrophysical parameters in the following ways:

For probabilistic method of resource assessment, a range of input parameters are used:

• Minimum, Most Likely and Maximum Values

• P15, P50 and P85 values

• P10, P50 and P90 values

In geostatic models, several porosity, water saturation curves etc. for different scenarios are used in Monte Carlo simulation to compute a range of hydrocarbon volumes.


The 50-50-90 Rule

Anytime you have a 50-50 chance of getting something right, there's a 90% probability that you'll get it wrong

Thank You!!

Documents

Petrophysical Uncertainties Do They Really Matter? · Objectives of Petrophysics •To assess the presence of hydrocarbon accumulation in exploration areas •To provide input parameters