Estimating contamination during focused sampling

Patent metadata

Field	Value
Publication number	US-10975693-B2
Application number	US-201916677040-A
Country	US
Kind code	B2
Filing date	Nov 7, 2019
Priority date	Dec 30, 2014
Publication date	Apr 13, 2021
Grant date	Apr 13, 2021

Abstract

Disclosed are methods and apparatus pertaining to processing in-situ, real-time data associated with fluid obtained by a downhole sampling tool. The processing includes generating a population of values for Ĉ, where each value of Ĉ is an estimated value of a fluid property for native formation fluid within the obtained fluid. The obtained data is iteratively fit to a predetermined model in linear space. The model relates the fluid property to pumpout volume or time. Each iterative fitting utilizes a different one of the values for Ĉ. A value Ĉ* is identified as the one of the values Ĉ that minimizes model fit error in linear space based on the iterative fitting. Selected values Ĉ that are near Ĉ* are then assessed to determine which one has a minimum integral error of nonlinearity in logarithmic space.

First claim

What is claimed is:

1. A method comprising:
obtaining in-situ, real-time data associated with fluid obtained by a downhole sampling tool disposed in a borehole that extends into a subterranean formation, wherein the obtained fluid comprises native formation fluid and filtrate contamination resulting from formation of the borehole, wherein the downhole sampling tool is in communication with surface equipment disposed at a wellsite surface from which the borehole extends, and wherein the obtained data includes a plurality of values of a fluid property of the obtained fluid relative to:
a pumpout volume of the fluid pumped from the subterranean formation by the downhole sampling tool; or
a pumpout time during which the fluid is pumped from the subterranean formation by the downhole sampling tool; and

via operation of at least one of the downhole sampling tool and the surface equipment:
generating a population of values for Ĉ, wherein each value of Ĉ is an estimated value of the fluid property for the native formation fluid;
iteratively fitting the obtained data to a predetermined model in linear space, wherein the model relates the fluid property to the pumpout volume or time, and wherein each iterative fitting utilizes a different one of the values for Ĉ;
identifying as Ĉ* which one of the values for e minimizes model fit error in linear space based on the iterative fitting of the obtained data;
selecting ones of the values for Ĉ that are near Ĉ*; and
determining which one of the selected ones of the values for Ĉ near Ĉ* has a minimum integral error of nonlinearity (IEN) in logarithmic space.

2. The method of claim 1 further comprising, via operation of at least one of the downhole sampling tool and the surface equipment, determining a fit start to be utilized for the iterative fitting of the obtained data, wherein determining the fit start is based on a derivative of the obtained fluid property values with respect to the pumpout volume or time.

3. The method of claim 2 wherein the fit start is determined to be no earlier than the pumpout volume or time at which the derivative of the obtained fluid property values reaches a maximum value.

4. The method of claim 1 wherein the fluid property is optical density (OD), and wherein the method further comprises, via operation of at least one of the downhole sampling tool and the surface equipment, determining the IEN for each of the selected ones of the values for Ĉ near Ĉ* utilizing:
IEN=∫ e dV*, where: e =log {{circumflex over ( C )}−OD(V)}−( a V*+ b ); and V*=log V

where OD(V) is the obtained optical density data with respect to pumpout volume (V), and a and b are constants of a straight line determined by first and last points of the function Ĉ OD(V).

5. The method of claim 4 further comprising, via operation of at least one of the downhole sampling tool and the surface equipment, truncating the obtained OD(V) data based on the derivative of the obtained OD(V) data with respect to V, wherein determining the IEN utilizes the truncated OD(V) data.

6. The method of claim 5 wherein truncating the obtained OD(V) data comprises excluding the obtained OD(V) data obtained prior to the derivative of the obtained OD(V) data reaching a maximum value.

7. The method of claim 1 further comprising, via operation of at least one of the downhole sampling tool and the surface equipment, obtaining a range and size of the population of values for Ĉ.

8. The method of claim 7 wherein obtaining the range and size comprises obtaining user inputs.

9. The method of claim 7 wherein obtaining the range and size comprises obtaining a predetermined range and size.

10. The method of claim 1 wherein iteratively fitting the obtained data to the predetermined model in linear space comprises performing linear regression to determine one or more adjustable parameters of the predetermined model using linear least squares fitting.

11. The method of claim 1 further comprising, via operation of at least one of the downhole sampling tool and the surface equipment, filtering the obtained data utilizing a robust moving percentile (RMP) filter prior to iteratively fitting the obtained data.

12. The method of claim 11 wherein filtering the obtained data utilizing the RMP filter comprises:
obtaining parameters for a data window to be moved through a plurality of window locations individually utilized to collectively filter the obtained data, wherein the parameters include a window size and a window target percentile range between upper and lower percentiles; and
at each of the plurality of window locations:
determining which of the obtained data values correspond to the upper and lower percentiles of the obtained data within the window at the current window location;
replacing the obtained data within the window at the current window location with random data having values ranging between the obtained data values determined to correspond to the upper and lower percentiles;
smoothing the random data; and
determining a filtered data point for the current window location based on the smoothed random data.

13. The method of claim 12 wherein smoothing the random data utilizes a weighted linear regression of the random data within the window at the current window location.

14. The method of claim 13 wherein the weighted linear regression weights the random data based on position within the window at the current window location, such that the random data located centrally within the window is weighted more heavily than the random data located near ends of the window.

15. A method comprising:
obtaining in-situ, real-time data associated with fluid obtained by a downhole sampling tool disposed in a borehole that extends into a subterranean formation, wherein the obtained fluid comprises native formation fluid and filtrate contamination resulting from formation of the borehole, wherein the downhole sampling tool is in communication with surface equipment disposed at a wellsite surface from which the borehole extends, and wherein the obtained data includes a plurality of values of a fluid property of the obtained fluid relative to:
a pumpout volume of the fluid pumped from the subterranean formation by the downhole sampling tool; or
a pumpout time during which the fluid is pumped from the subterranean formation by the downhole sampling tool; and

via operation of at least one of the downhole sampling tool and the surface equipment:
generating a population of values for Ĉ, wherein each value of Ĉ is an estimated value of the fluid property for the native formation fluid; and
determining which one of the values for Ĉ has a minimum integral error of nonlinearity (IEN) in logarithmic space.

16. The method of claim 15 wherein the fluid property is optical density (OD), and wherein the method further comprises, via operation of at least one of the downhole sampling tool and the surface equipment, determining the IEN for each of the values for Ĉ utilizing:
IEN=∫ e dV*, where: e =log {{circumflex over ( C )}−OD(V)}−( a V*+ b ); and V*=log V

where OD(V) is the obtained optical density data with respect to pumpout volume (V), and a and b are constants of a straight line determined by first and last points of the function Ĉ−OD(V).

17. The method of claim 16 further comprising, via operation of at least one of the downhole sampling tool and the surface equipment, truncating the obtained OD(V) data based on a maximum value of the derivative of the obtained OD(V) data with respect to V, wherein determining the IEN utilizes the truncated

Assignees

• Schlumberger Technology Corp

Inventors

• Lee Ryan Sangjun
• Gisolf Adriaan
• Zuo Youxiang

Classifications

• E21B49/10 Primary

  using side-wall fluid samplers or testers · CPC title

• E21B49/0875

  determining specific fluid parameters · CPC title

• G06Q50/02

  Agriculture; Fishing; Forestry; Mining · CPC title

• E21B2200/20

  Computer models or simulations, e.g. for reservoirs under production, drill bits · CPC title

• E21B49/081 Primary

  with down-hole means for trapping a fluid sample (E21B49/10 takes precedence) · CPC title