# US 2020/0399987 A1 · System and Method for Predicting Fluid Behavior in an Unconventional Shale Play

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- Number: US 2020/0399987 A1
- Document ID: US20200399987A1
- Status: Published application
- Kind: A1
- Application number: 16/447,038
- Filed: 2019-06-20
- Published: 2020-12-24
- Earliest claimed: 2019-06-20
- Assignees: Shale Value, LLC
- Applicants: Shale Value, LLC
- Inventors: Ashish Dabral
- Web page: https://patentgrep.com/patent/US20200399987A1

## CPC

- G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30)
- E21B 41/0035: {Apparatus or methods for multilateral well technology, e.g. for the completion of or workover on wells with one or more lateral branches}
- 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)
- G06N 20/00: Machine learning
- E21B 2200/20: Computer models or simulations, e.g. for reservoirs under production, drill bits
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A system and method for generating a PVT model capable of predicting well behavior across a play is described. The method can comprise the step of obtaining, for each well of a subset of wells, a measured API gravity, a measured gas-to-oil ratio (GOR), and one or more lab experiments. The lab experiments can measure one or more PVT characteristics. The method can also comprise the step of training a PVT model to match the measured API gravities and the measured GOR with the PVT characteristics. Further, the method can comprise the step of inputting a PVT input into the PVT model and receiving a PVT output from the PVT model. The PVT input can be related to an additional hydrocarbon sample. The PVT input can comprise an API gravity and a GOR. The PVT output can be based on the API gravity and the GOR.

## Claims

### Claim 1

A method for generating a PVT model capable of predicting well behavior across a play comprising
obtaining, relating to each well of a subset of wells,
a measured API gravity; and
a measured gas-to-oil ratio (GOR); and
one or more lab experiments, said lab experiments measuring one or more PVT characteristics;
training a PVT model to match said measured API gravities and said measured GOR with said PVT characteristics;
inputting a PVT input into said PVT model, said PVT input related to an additional hydrocarbon sample, said PVT input comprising an API gravity and a GOR; and
receiving a PVT output from said PVT model, said PVT output based on said API gravity and said GOR.

### Claim 2 (depends on 1)

The method of claim 1 wherein said PVT output comprises a composition table, said composition table calculated based on said API gravity and said GOR.

### Claim 3 (depends on 1)

The method of claim 1 wherein said PVT output comprises a black oil table, said black oil table calculated based on said API gravity and said GOR.

### Claim 4 (depends on 1)

The method of claim 1 wherein said PVT output comprises a molecular weight of oil, said molecular weight of oil calculated based on said API gravity and said GOR.

### Claim 5 (depends on 1)

The method of claim 1 wherein said PVT output comprises a molecular weight of oil condensate, said molecular weight of oil condensate calculated based on said API gravity and said GOR.

### Claim 6 (depends on 1)

The method of claim 1 wherein said PVT output comprises a saturation pressure, said saturation pressure calculated based on said API gravity and said GOR.

### Claim 7 (depends on 1)

The method of claim 1 further comprising the step of determining a location of a well based at least in part by said PVT output.

### Claim 8

A system for generating a PVT model capable of predicting well behavior across a play comprising
a memory comprising
an application; and
a data store; and
a processor that according to said application in said memory
obtains, relating to each well of a subset of wells,
a measured API gravity; and
a measured gas-to-oil ratio (GOR); and
one or more lab experiments, said lab experiments measuring one or more PVT characteristics;
trains a PVT model to match said measured API gravities and said measured GOR with said PVT characteristics;
inputs a PVT input into said PVT model, said PVT input related to an additional hydrocarbon sample, said PVT input comprising an API gravity and a GOR; and
receives a PVT output from said PVT model, said PVT output based on said API gravity and said GOR.

### Claim 9 (depends on 8)

The system of claim 8 wherein obtaining said measured API gravities, said measured GORs, and said one or more lab experiments comprises obtaining sets of lab measurements from hydrocarbon samples from said subset of wells, each said set of lab measurements comprising said measured API gravity, said measured GOR, and said one or more lab experiments.

### Claim 10 (depends on 8)

The system of claim 8 further wherein said processor, according to said application in said memory obtains, relating to each well of said subset of wells, a measured composition, further wherein said PVT model comprises a composition model, further wherein said PVT output comprises a composition table.

### Claim 11

A method for generating a PVT model capable of predicting well behavior across a play comprising
obtaining sets of lab measurements from hydrocarbon samples of a subset of a plurality of wells, each set of said sets associated with a subset well of said subset of said plurality of wells, said lab measurements comprising
a measured API gravity;
a measured gas-to-oil (GOR) ratio;
a measured composition; and
one or more lab experiments;
training a model using said lab measurements by
tuning an equation of state by
dividing each of said measured compositions into component groupings, one or more groups of said component groupings comprising variable attributes; and
adjusting said variable attributes to match said one or more lab experiments;
adjusting Peneloux correction factors such that said measured API gravities and said measured GORs of said lab measurements match calculated API gravities and calculated GORs; and
creating a composition model, said composition model a function of a variable API gravity and a variable GOR, further said composition model a composition model constituent of a PVT model such that when said PVT model receive a PVT input comprising an API gravity and a GOR, said PVT model generates PVT output, said PVT output comprising a composition table generated using said composition model.

### Claim 12 (depends on 11)

The method of claim 11 further comprising the step of feeding initial PVT inputs from remaining wells of said plurality of wells into said model that has been trained to produce an initial PVT output for each of said initial PVT inputs, each said initial PVT input comprising an initial API gravity and an initial GOR, each of said initial PVT outputs calculated using said initial API gravity and said initial GOR.

### Claim 13 (depends on 12)

The method of claim 12 wherein each of said initial PVT outputs comprises an initial saturation pressure (PSAT) calculated using said initial API gravity and said initial GOR.

### Claim 14 (depends on 13)

The method of claim 13 further comprising the step of curve-fitting said initial PSATs to produce a PSAT equation that calculates a subsequent PSAT as a function of said variable API gravity and said variable GOR, said PSAT equation a PSAT constituent of said PVT model.

### Claim 15 (depends on 12)

The method of claim 12 wherein each of said initial PVT outputs comprises an initial molecular weight of oil (MWO) calculated using said initial API gravity and said initial GOR.

### Claim 16 (depends on 15)

The method of claim 15 further comprising the step of curve-fitting said initial MWOs to produce an MWO equation that calculates a subsequent MWO as a function of said variable API gravity and said variable GOR, said MWO equation an MWO constituent of said PVT model.

### Claim 17 (depends on 12)

The method of claim 12 wherein each of said initial PVT outputs comprises an initial molecular weight of oil condensate (MWC) calculated using said initial API gravity and said initial GOR.

### Claim 18 (depends on 17)

The method of claim 17 further comprising the step of curve-fitting said initial MWCs to produce an MWC equation that calculates a subsequent MWC as a function of said variable API gravity and said variable GOR, said MWC equation an MWC constituent of said PVT model.

### Claim 19 (depends on 12)

The method of claim 12 wherein each of said initial PVT outputs comprises an initial black oil table calculated using said initial API gravity and said initial GOR.

### Claim 20 (depends on 17)

The method of claim 17 further comprising the step of curve-fitting said initial black oil tables to produce a black oil table model that calculates a subsequent black oil table as a function of said variable API gravity and said variable GOR, said black oil table model a black oil table constituent of said PVT model.

### Claim 21 (depends on 18)

The method of claim 18 further comprising the steps of
determining remaining hydrocarbons for a site using said subsequent black oil table generated from a subsequent PVT input; and
choosing a new well location of a new well based at least in part on said determination.

### Claim 22 (depends on 12)

The method of claim 12 wherein each of said initial PVT outputs comprises a characteristic line calculated using said initial API gravity and said initial GOR.

### Claim 23 (depends on 22)

The method of claim 22 further comprising the step of determining a saturation limit line based on said characteristic lines.

### Claim 24 (depends on 23)

The method of claim 23 further comprising the step of plotting said saturation limit line on an API gravity-GOR graph, said saturation limit line forming at least a portion of a characteristic plot, said characteristic plot a characteristic plot constituent of said PVT model.

### Claim 25 (depends on 23)

The method of claim 23 further comprising the steps:
feeding a subsequent PVT input related to a new hydrocarbon sample into said PVT model;
generating a subsequent characteristic point related to said subsequent PVT input; and
determining if said hydrocarbon sample is saturated if said subsequent characteristic point is below said saturation limit line.

### Claim 26 (depends on 22)

The method of claim 22 further comprising the step of determining a sample validity limit line based on said characteristic lines.

### Claim 27 (depends on 26)

The method of claim 26 further comprising the step of plotting said sample validity limit line on said API gravity-GOR graph, said sample validity limit line forming at least a portion of a characteristic plot, said characteristic plot a characteristic plot constituent of said PVT model.

### Claim 28 (depends on 26)

The method of claim 26 further comprising the steps:
feeding a subsequent PVT input related to a new hydrocarbon sample into said PVT model;
generating a subsequent characteristic point related to said subsequent PVT input; and
screening out said hydrocarbon sample if said subsequent characteristic point is above said sample validity limit line.

### Claim 29

A system for generating a PVT model capable of predicting well behavior across a play comprising
a memory comprising
an application; and
a data store; and
a processor that according to said application in said memory
obtains sets of lab measurements from hydrocarbon samples of a subset of a plurality of wells, each set of said sets associated with a subset well of said subset of said plurality of well, said lab measurements comprising
a measured API gravity;
a measured gas-to-oil (GOR) ratio;
a measured composition; and
one or more lab experiments; and
trains a model using said lab measurements by
tuning an equation of state by
dividing each of said measured compositions into component groupings, one or more groups of said component groupings comprising variable attributes; and
adjusting said variable attributes to match said one or more lab experiments;
adjusting Peneloux correction factors such that said measured API gravities and said measured GORs of said lab measurements match calculated API gravities and calculated GORs; and
creating a composition model, said composition model a function of a variable API gravity and a variable GOR, further said composition model a composition model constituent of a PVT model such that when said PVT model receives a PVT input comprising an API gravity and a GOR, said PVT model generates a PVT output, said PVT output comprising a composition table generated using said composition model.

### Claim 30 (depends on 29)

The system of claim 29 wherein said hydrocarbon sample is an oil hydrocarbon sample.

### Claim 31 (depends on 29)

The system of claim 29 wherein said hydrocarbon sample is a gas condensate hydrocarbon sample.

### Claim 32 (depends on 29)

The system of claim 29 wherein said one or more lab experiments comprises a constant composition expansion test.

### Claim 33 (depends on 29)

The system of claim 29 wherein said one or more lab experiments comprises a constant volume depletion test.

### Claim 34 (depends on 29)

The system of claim 29 wherein said one or more lab experiments comprises a differential liberator test.

### Claim 35 (depends on 29)

The system of claim 29 wherein said one or more lab experiments comprises a separator test.

### Claim 36 (depends on 29)

The system of claim 29 further wherein said processor feeds sets of initial PVT inputs from remaining wells of said plurality of wells into said model that has been trained to produce initial PVT outputs.

### Claim 37 (depends on 36)

The system of claim 36 further wherein said processor curve-fits said initial PVT outputs to produce one or more functions of a variable API and a variable GOR, said one or more functions a constituent of said PVT model.

### Claim 38 (depends on 36)

The system of claim 36 further wherein the processor determines a location of a well at least in part by
feeding a subsequent PVT input into said PVT model, said subsequent PVT input comprising a subsequent API gravity and a subsequent GOR,
receiving a subsequent PVT output from said PVT model, and
basing said determination on said subsequent PVT output.

### Claim 39 (depends on 36)

The system of claim 36 wherein said one or more functions comprises a PSAT equation and said subsequent PVT output comprises a subsequent PSAT.

### Claim 40 (depends on 36)

The system of claim 36 wherein said one or more functions comprises an MWO equation and said subsequent PVT output comprises a subsequent MWO.

### Claim 41 (depends on 36)

The system of claim 36 wherein said one or more functions comprises an MWC equation and said subsequent PVT output comprises a subsequent MWC.

### Claim 42 (depends on 36)

The system of claim 36 wherein said one or more functions comprises a black oil table model and said subsequent PVT output comprises a subsequent black oil table.

### Claim 43 (depends on 36)

The system of claim 36 wherein said PVT input further comprises an N2.

### Claim 44 (depends on 29)

The system of claim 29 wherein said PVT input further comprises an H2S.

### Claim 45 (depends on 29)

The system of claim 29 wherein said PVT input further comprises a CO2.

### Claim 46 (depends on 1)

A computer readable storage medium having a computer readable program code embodied therein, wherein the computer readable program code is adapted to be executed to implement the method of claim 1.