# US 10,767,476 B2 · Microbiome based systems, apparatus and methods for the exploration and production of hydrocarbons

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- Number: US 10,767,476 B2
- Document ID: US10767476B2
- Status: Granted
- Kind: B2
- Application number: 15/641,965
- Filed: 2017-07-05
- Published: 2017-12-28
- Granted: 2020-09-08
- Earliest claimed: 2013-12-31
- Assignees: Biota Technology, Inc.
- Applicants: Biota Technology, Inc.
- Inventors: Rob Knight; Ajay Kshatriya; John Ely; Paul Henshaw; J. Gregory Caporaso; Dan Knights; Ryan Gill
- Web page: https://patentgrep.com/patent/US10767476B2

## CPC

- E21B 49/08: Obtaining fluid samples or testing fluids, in boreholes or wells
- C09K 8/582: characterised by the use of bacteria
- C09K 8/62: Compositions for forming crevices or fractures
- C12Q 1/6874: involving nucleic acid arrays, e.g. sequencing by hybridisation
- C12Q 1/6888: for detection or identification of organisms
- C12Q 1/689: for bacteria
- E21B 43/00: Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells (applicable only to water E03B)
- E21B 43/2607: {Surface equipment specially adapted for fracturing operations}
- E21B 47/11: using tracers; using radioactivity
- E21B 49/00: Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
- E21B 49/086: {Withdrawing samples at the surface}
- G01V 9/00: Prospecting or detecting by methods not provided for in groups G01V1/00 - G01V8/00
- 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
- G16B 20/20: Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
- G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16B 45/00: ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
- C12Q 2600/156: Polymorphic or mutational markers
- E21B 21/065: {Separating solids from drilling fluids}
- E21B 43/267: reinforcing fractures by propping
- E21B 49/003: {by analysing drilling variables or conditions (E21B49/005 takes precedence; systems specially adapted for monitoring a plurality of drilling variables or conditions E21B44/00)}
- E21B 49/0875: {determining specific fluid parameters}
- E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- C09K: MATERIALS FOR MISCELLANEOUS APPLICATIONS, NOT PROVIDED FOR ELSEWHERE
- 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
- G01V: GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS (means for indicating the location of accidentally buried, e.g. snow-buried, persons A63B29/02)
- 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 the exploration, determination, production and recovery of natural resources, including energy sources, and the monitoring, control and analysis of processes and activities.

## Claims

### Claim 1

A system comprising:
a database storing extraction data corresponding to one or more sample materials obtained at a resource production field, the extraction data including microbiome data corresponding to the one or more sample materials; and
a computer system including one or more memories and one or more processors, one or more computer instructions incorporated into the one or more memories to configure the one or more processors to perform operations for improving a predictive accuracy of predictive microbiome data with respect to a target environmental parameter at the resource production field, the operations comprising:
accessing the database to obtain the microbiome data;
selecting a predictive model from a plurality of predictive models based on an expected prediction error of the predictive model being lower than an expected prediction error of other models of the plurality of predictive models, the expected prediction error of the predictive model being based on repeated fittings of the predictive model to different subsets of the microbiome data or other related microbiome data;
generating the predictive microbiome data from the first subset of the microbiome data, the generating of the predictive microbiome data including applying the predictive model using a machine-learned combination of feature inputs and model parameters; and
causing an interactive display of a visualization of the predictive microbiome data in a graphical user interface to assist an operator in creating a plan for directing an activity at the resource production field.

### Claim 2 (depends on 1)

The system of claim 1, further comprising selecting an abundance of genetic material identified in the microbiome data as one of the feature inputs based on the machine-learned combination indicating that the abundance discriminates between classifications of data points within the microbiome data, the classifications relating to the target environmental parameter.

### Claim 3 (depends on 1)

The system of claim 1, wherein the plurality of models is selected from a class of models based on structures inherent in the first subset of the microbiome data.

### Claim 4 (depends on 1)

The system of claim 1, wherein a constraint inherent in the microbiome data includes a diversity of data and the class of models includes generative models appropriate for the diversity.

### Claim 5 (depends on 1)

The system of claim 1, wherein the target environmental parameter relates to hydrocarbon exploration or production at the resource production field.

### Claim 6 (depends on 1)

The system of claim 1, wherein the target environmental parameter relates to at least one of subsurface flow communication or reservoir connectivity.

### Claim 7 (depends on 1)

The system of claim 1, wherein the target environmental parameter relates to at least one of oil saturation or permeability of a well zone.

### Claim 8 (depends on 1)

The system of claim 1, wherein the target environmental parameter relates to wettability of a reservoir.

### Claim 9 (depends on 1)

The system of claim 1, wherein the target environmental parameter relates to at least one of viscosity, temperature, pressure, porosity, or compressibility of oil or water in a reservoir.

### Claim 10

A method comprising:
performing, using one or more processors, operations for improving a predictive accuracy of predictive microbiome data with respect to a target environmental parameter at a resource production field, the operations comprising:
accessing a database to obtain the microbiome data, the database storing extraction data corresponding to one or more sample materials obtained at the resource production field, the extraction data including the microbiome data;
selecting a predictive model from a plurality of predictive models based on an expected prediction error of the predictive model being lower than an expected prediction error of other models of the plurality of predictive models, the expected prediction error of the predictive model being based on repeated fittings of the predictive model to different subsets of the microbiome data or other related microbiome data;
generating the predictive microbiome data from the microbiome data, the generating of the predictive microbiome data including applying the predictive model using a machine-learned combination of feature inputs and model parameters; and
causing an interactive display of a visualization of the predictive microbiome data in a graphical user interface to assist an operator in creating a plan for directing an activity at the resource production field.

### Claim 11 (depends on 10)

The method of claim 10, further comprising selecting an abundance of genetic material identified in the microbiome data as one of the feature inputs based on the machine-learned combination indicating that the abundance discriminates between classifications of data points within the microbiome data, the classifications relating to the target environmental parameter.

### Claim 12 (depends on 10)

The system of claim 10, wherein the plurality of models is selected from a class of models based on structures inherent in the first subset of the microbiome data.

### Claim 13 (depends on 10)

The system of claim 10, wherein a constraint inherent in the microbiome data includes a diversity of data and the class of models includes generative models appropriate for the diversity.

### Claim 14

A non-transitory machine-readable storage medium embodying instructions that, when executed by one or more processors, cause the one or more processors to perform operations for improving a predictive accuracy of predictive microbiome data with respect to a target environmental parameter at a resource production field, the operations comprising:
accessing a database to obtain the microbiome data, the database storing extraction data corresponding to one or more sample materials obtained at the resource production field, the extraction data including the microbiome data;
selecting a predictive model from a plurality of predictive models based on an expected prediction error of the predictive model being lower than an expected prediction error of other models of the plurality of predictive models, the expected prediction error of the predictive model being based on repeated fittings of the predictive model to different subsets of the microbiome data or other related microbiome data;
generating the predictive microbiome data from the microbiome data, the generating of the predictive microbiome data including applying the predictive model using a machine-learned combination of feature inputs and model parameters; and
causing an interactive display of a visualization of the predictive microbiome data in a graphical user interface to assist an operator in creating a plan for directing an activity at the resource production field.

### Claim 15 (depends on 14)

The non-transitory machine-readable storage medium of claim 14, further comprising selecting an abundance of genetic material identified in the microbiome data as one of the feature inputs based on the machine-learned combination indicating that the abundance discriminates between classifications of data points within the microbiome data, the classifications relating to the target environmental parameter.

### Claim 16 (depends on 15)

The non-transitory machine-readable storage medium of claim 15, wherein the plurality of models is selected from a class of models based on structures inherent in the first subset of the microbiome data.

### Claim 17 (depends on 14)

The non-transitory machine-readable storage medium of claim 14, wherein a constraint inherent in the microbiome data includes a diversity of data and the class of models includes generative models appropriate for the diversity.