METHOD AND SYSTEM FOR GENERATING FORMATION PROPERTY VOLUME USING MACHINE LEARNING

1 . A method, comprising:

obtaining, by a computer processor, well log data for a plurality of wells regarding a geological region of interest, the well log data corresponding to a plurality of well logs with different logging types;

assigning, by the computer processor and using a grouping algorithm, subsets of the well log data to a plurality of groups based on one or more geological attributes;

determining, by the computer processor and using the plurality of groups and a machine-learning algorithm, a plurality of well zones for different portions of a respective well among the plurality of wells;

determining, by the computer processor, interpolated log data using the well log data, the plurality of well zones, and an intrawell interpolation process; and

generating, by the computer processor, a formation property volume based on the interpolated log data and the well log data.

2 . The method of claim 1 , further comprising:

determining predicted log data for a predetermined well among the plurality of wells using a machine-learning model,

wherein the predicted log data corresponds to a predetermined well log type that is missing for the predetermined well among the plurality of well logs, and

wherein the formation property volume is based on the predicted log data.

3 . The method of claim 2 ,

wherein the machine-learning model is a deep neural network with a plurality of hidden layers, an input layer, and an output layer,

wherein the machine-learning model obtains gamma-ray (GR) log data, true vertical depth (TVD) data, northing data, and easting data as inputs to the input layer, and

wherein the machine-learning model provides sonic log data, Poisson's ratio data, and Young's modulus data.

4 . The method of claim 1 , further comprising:

determining extrapolated log data using the well log data, the plurality of well zones, and an interwell extrapolation process,

wherein the extrapolated log data corresponds to one or more interwell regions between a first well and a second well among the plurality of wells, and

wherein the formation property volume is based on the extrapolated log data.

5 . The method of claim 4 , further comprising:

obtaining seismic data regarding the geological region of interest,

wherein the interwell extrapolation process uses the seismic data to determine the extrapolated log data.

6 . The method of claim 1 ,

wherein the grouping algorithm is a k-means clustering algorithm,

wherein at least one group among the plurality of groups comprises a first subset of well log data and a second subset of well log data, and

wherein the first subset of well log data corresponds to data at a different depth and in a different well as the second subset of well log data.

7 . The method of claim 1 ,

wherein the one or more geological attributes are selected from a group consisting of a facies classification, a stratigraphy horizon, and a geological time period.

8 . The method of claim 1 ,

wherein at least one well zone among the plurality of well zones corresponds to a predetermined depth range within a respective well among the plurality of wells,

wherein the machine-learning algorithm is based on a vertical smoothing and a horizontal smoothing, and

wherein the at least one well zone comprises smoothed well log data that is generated using the vertical smoothing and horizontal smoothing.

9 . The method of claim 1 ,

wherein the formation property volume corresponds to a three-dimensional region that describes a plurality of geological properties,

wherein the plurality of geological properties are selected from a group consisting of porosity, permittivity, resistivity, density, and water saturation, and

wherein the formation property volume is used to perform hydrocarbon exploration within the geological region of interest.

10 . A system, comprising:

a logging system coupled to a logging tool;

a well system coupled to the logging system and a wellbore; and

a reservoir simulator comprising a computer processor, wherein the reservoir simulator is coupled to the logging system and the well system, the reservoir simulator comprising functionality for:

obtaining, using the logging tool, first well log data, wherein the first well log data is a portion of a second well log data for a plurality of wells regarding a geological region of interest, wherein the second well log data correspond to a plurality of well logs with different logging types;

assigning, using a grouping algorithm, subsets of the second well log data to a plurality of groups based on one or more geological attributes;

determining, using the plurality of groups and a machine-learning algorithm, a plurality of well zones for different portions of a respective well among the plurality of wells;

determining interpolated log data using the well log data, the plurality of well zones, and an intrawell interpolation process; and

generating a formation property volume based on the interpolated log data and the second well log data.

11 . The system of claim 10 , wherein the reservoir simulator further comprises functionality for:

determining predicted log data for a predetermined well among the plurality of wells using a machine-learning model,

wherein the predicted log data corresponds to a predetermined well log type that is missing for the predetermined well among the plurality of well logs, and

wherein the formation property volume is based on the predicted log data.

12 . The system of claim 10 , wherein the reservoir simulator further comprises functionality for:

determining extrapolated log data using the well log data, the plurality of well zones, and an interwell extrapolation process,

wherein the extrapolated log data corresponds to one or more interwell regions between a first well and a second well among the plurality of wells, and

wherein the formation property volume is based on the extrapolated log data.

13 . The system of claim 12 , wherein the reservoir simulator further comprises functionality for:

obtaining seismic data regarding the geological region of interest,

wherein the interwell extrapolation process uses the seismic data to determine the extrapolated log data.

14 . The system of claim 10 ,

wherein the grouping algorithm is a k-means clustering algorithm,

wherein at least one group among the plurality of groups comprises a first subset of well log data and a second subset of well log data, and

wherein the first subset of well log data corresponds to data at a different depth and in a different well as the second subset of well log data.

15 . The system of claim 10 ,

wherein the machine-learning model is a deep neural network with a plurality of hidden layers, an input layer, and an output layer,

wherein the machine-learning model obtains gamma ray (GR) log data, true vertical depth (TVD) data, northing data, and easting data as inputs to the input layer, and

wherein the machine-learning model provides sonic log data, Poisson's ratio data, and Young's modulus data.

16 . The system of claim 10 ,

wherein at least one well zone among the plurality of well zones corresponds to a predetermined depth range within a respective well among the plurality of wells,

wherein the machine-learning algorithm is based on a vertical smoothing and a horizontal smoothing, and

wherein the at least one well zone comprises smoothed well log data that is generated using the vertical smoothing and horizontal smoothing.

17 . The system of claim 10 ,

wherein the formation property volume corresponds to a three-dimensional region that describes a plurality of geological properties,

wherein the plurality of geological properties are selected from a group consisting of porosity, permittivity, resistivity, density, and water saturation, and

wherein the formation property volume is used to perform hydrocarbon exploration within the geological region of interest.

18 . A non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality for:

obtaining well log data for a plurality of wells regarding a geological region of interest, wherein the well log data correspond to a plurality of well logs with different logging types;

assigning, using a grouping algorithm, subsets of the well log data to a plurality of groups based on one or more geological attributes;

determining, using the plurality of groups and a machine-learning algorithm, a plurality of well zones for different portions of a respective well among the plurality of wells;

determining interpolated log data using the well log data, the plurality of well zones, and an intrawell interpolation process; and

generating a formation property volume based on the interpolated log data and the well log data.

19 . The non-transitory computer readable medium of claim 18 , the instructions further comprising functionality for:

determining predicted log data for a predetermined well among the plurality of wells using a machine-learning model,

wherein the predicted log data corresponds to a predetermined well log type that is missing for the predetermined well among the plurality of well logs, and

wherein the formation property volume is based on the predicted log data.

20 . The non-transitory computer readable medium of claim 18 , the instructions further comprising functionality for:

determining extrapolated log data using the well log data, the plurality of well zones, and an interwell extrapolation process,

wherein the extrapolated log data corresponds to one or more interwell regions between a first well and a second well among the plurality of wells, and

wherein the formation property volume is based on the extrapolated log data.