# US 11,028,449 B2 · Microbiome based systems, apparatus and methods for monitoring and controlling industrial processes and systems

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- Number: US 11,028,449 B2
- Document ID: US11028449B2
- Status: Granted
- Kind: B2
- Application number: 14/585,078
- Filed: 2014-12-29
- Published: 2015-10-08
- Granted: 2021-06-08
- Earliest claimed: 2013-12-31
- Assignees: Biota Technology, Inc.
- Applicants: Biota Technology, Inc.
- Inventors: Rob Knight; Ajay Kshatriya; Chris Lauber; J. Gregory Caporaso; Dan Knights; Ryan Gill; Joel Moxley
- Web page: https://patentgrep.com/patent/US11028449B2

## CPC

- C12Q 1/6888: for detection or identification of organisms
- C09K 8/582: characterised by the use of bacteria
- C12Q 1/689: for bacteria
- E21B 43/16: Enhanced recovery methods for obtaining hydrocarbons
- E21B 47/11: using tracers; using radioactivity
- G16B 10/00: ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
- G16B 20/00: ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- C12Q 2600/156: Polymorphic or mutational markers
- C12Q: MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS (immunoassay G01N33/53); COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C09K: MATERIALS FOR MISCELLANEOUS APPLICATIONS, NOT PROVIDED FOR ELSEWHERE
- E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY

## Abstract

There are provided methods, systems and processes for the utilization of microbial and related genetic information for use in industrial settings, such as the exploration, determination, and recovery of natural resources, minerals, and energy sources, the monitoring and analysis of processes, activities, and materials transmission.

## Claims

### Claim 1

A computerized method comprising:
generating, using one or more computer processors, microbiome information from genetic material extracted from a first sample of a plurality samples sourced from a plurality of wells at a location associated with an industrial operation;
generating, using the one or more computer processors, microbiome information from genetic material extracted from a second sample of the plurality of samples;
selecting, using the one or more computer processors, a machine-learned model from a plurality of machine-learned models based on a testing of a predictive accuracy of the machine-learned model when applied to a plurality of subsets of the microbiome information from the genetic material extracted from the first sample, the selecting based on a class of the machine-learned model being a specialized generative model that was generated using an embedded approach to feature selection having knowledge of a parameter-selection process;
generating, using the one or more computer processors, predictive microbiome information corresponding to the microbiome information from the genetic material extracted from the second sample, the generating of the predictive microbiome information including applying the selected machine-learned model to the genetic material from the second sample, the predictive microbiome information relating to a source production zone corresponding to a hydrocarbon formation or a characteristic of a reservoir corresponding to the hydrocarbon formation, the characteristic including at least one of pressure, temperature, or viscosity; and
communicating, using the one or more computer processors, the predictive microbiome information relating to the source production zone or the characteristic of the reservoir for display in a user interface.

### Claim 2 (depends on 1)

The method of claim 1, wherein at least a portion of the predictive microbiome information is displayed in a two dimensional plot.

### Claim 3 (depends on 1)

The method of claim 1, wherein at least a portion of the predictive microbiome information is displayed in a three dimensional plot.

### Claim 4 (depends on 1)

The method of claim 1, wherein non-genetic information corresponding to the predictive microbiome information is selected from a group consisting of temperature, GPS, pressure, depth, borehole true vertical depth, casing collar location, and viscosity, and the method further comprises communicating the non-genetic information for display in the user interface.

### Claim 5

A method comprising:
selecting, using one or more computer processors, a machine-learned model from a plurality of machine-learned models based on a testing of a predictive accuracy of the machine-learned model when applied to a plurality of subsets of microbiome information from a genetic material extracted from a first sample, the selecting based on a class of the machine-learned model being a specialized generative model that was generated using an embedded approach to feature selection having knowledge of a parameter-selection process;
generating, using one or more computer processors, predictive microbiome information based on a nucleic acid sequencing data, the generating of the predictive microbiome information including applying a machine-learned model to the nucleic acid sequencing data, the predictive microbiome information predicting a productivity of each of a plurality of wells at a location based on intra-well temporal dynamics of microbial communities at the plurality of wells;
communicating, using the one or more computer processors, the predictive microbiome information for display in a user interface.

### Claim 6 (depends on 5)

The method of claim 5, further comprising determining the intra-well and temporal dynamics based on real time microbiome information corresponding to the genetic material or an additional material.

### Claim 7 (depends on 5)

The method of claim 5, further comprising determining the intra-well and temporal dynamics based on historic microbiome information corresponding to the genetic the material or an additional material.

### Claim 8 (depends on 5)

The method of claim 5, wherein the plurality of wells is associated with an industrial operation selected from a group consisting of hydraulic fracturing and drilling a borehole.

### Claim 9 (depends on 5)

The method of claim 5, further comprising determining the intra-well and temporal dynamics based on historic information and real time information corresponding to the genetic material or an additional material.

### Claim 10 (depends on 5)

The method of claim 5, further comprising determining the intra-well and temporal dynamics is based on derived microbiome information and real time information corresponding to the genetic material or an additional material.

### Claim 11

A method comprising:
performing operations at a computing system having one or more computer processors for communicating predictive microbiome information to a memory storage device for display in a human machine interface (HMI) of the memory storage device, the display having an interactive configuration for identifying patterns in the predictive microbiome information that are relevant to optimizing recovery of hydrocarbons, the operations comprising:
selecting, using the one or more computer processors, a machine-learned model from a plurality of machine-learned models based on a testing of a predictive accuracy of the machine-learned model when applied to a plurality of subsets of the microbiome information from a genetic material extracted from the first sample, the selecting based on a class of the machine-learned model being a specialized generative model that was generated using an embedded approach to feature selection having knowledge of a parameter-selection process;
generating, using the one or more computer processors, the predictive microbiome information, the predictive microbiome information corresponding to sequencing data extracted from genetic material from a second sample sourced at a well or an additional well, the generating of the predictive microbiome information including applying the selected machine-learned model to the sequencing data extracted from the genetic material from the second sample, the predictive microbiome information relating to a source production zone corresponding to a hydrocarbon formation or a characteristic of a reservoir corresponding to the hydrocarbon formation, the characteristic including at least one of pressure, temperature, or viscosity; and
performing, using the one or more computer processors, the communicating of the predictive microbiome information for the display in a user interface.

### Claim 12 (depends on 11)

The method of claim 11, further comprising using computational analysis to subtract possible sources of contamination during collection of the first sample or the second sample.

### Claim 13 (depends on 11)

The method of claim 11, further comprising communicating the predictive microbiome information for presentation in the user interface as n-dimensional space information.

### Claim 14 (depends on 11)

The method of claim 11, wherein the predictive microbiome information further predicts a productivity based on information about temporal dynamics of microbial communities within the well or the additional well.

### Claim 15 (depends on 11)

The method of claim 11, wherein the predictive microbiome information predicts inter-well and inter-formation differences between the well and the additional well.

### Claim 16 (depends on 11)

The method of claim 11, wherein the predictive microbiome information pertains to a perforation and hydraulic fracturing plan to improve production.

### Claim 17 (depends on 11)

The method of claim 11, further comprising storing the predictive microbiome information in auxiliary data structures for ready and efficient access.

### Claim 18 (depends on 17)

The method of claim 17, wherein the auxiliary data structures include indexes or hashes.

### Claim 19 (depends on 1)

The computerized method of claim 1, the user interface having an interactive configuration for visualizing patterns in the predictive microbiome information that are relevant to directing a recovery operation at the location, the interactive configuration including a plot using different colors for the predictive microbiome information and non-genetic information corresponding to the predictive microbiome information.

### Claim 20 (depends on 5)

The method of claim 5, further comprising determining the intra-well spatial dynamics based on real time microbiome information corresponding to the genetic material or an additional material.