Gradient-based workflows for conditioning of process-based geologic models
First Claim
1. A computer implemented method for correlating predicted data describing a subsurface region with known data describing the subsurface region, the method comprising:
- obtaining data describing an initial state of the subsurface region;
predicting data describing a subsequent state of the subsurface region;
updating a likelihood measure that determines whether the predicted data is within an acceptable range of the obtained data, the updating being performed at least one of dynamically and interactively;
using a computer to compare the predicted data with the obtained data using the likelihood measure;
using a computer to determine a sensitivity of the predicted data if the predicted data is not within an acceptable range of the obtained data as measured by the likelihood measure;
adjusting the data describing the initial state of the subsurface region based on the sensitivity before performing a subsequent iteration of predicting data describing the subsequent state of the subsurface region, wherein the adjusting is performed based on the likelihood measure having the largest change in sensitivity, such that at any point in parameter space only the likelihood measure that produces the largest change in sensitivity is chosen and drive the process until other measures catch up and the likelihood measure changes subsequently; and
outputting the predicted data based on the adjusting.
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Abstract
A method for correlating predicted data describing a subsurface region with obtained data describing the subsurface region is provided. Data is obtained describing an initial state of the subsurface region. Data describing a subsequent state of the subsurface region is predicted. A likelihood measure that determines whether the predicted data is within an acceptable range of the obtained data is dynamically and/or interactively updated. The predicted data is compared with the obtained data using the likelihood measure and determining a sensitivity of the predicted data if the predicted data is not within an acceptable range of the obtained data as measured by the likelihood measure. Data describing the initial state of the subsurface region is adjusted based on the sensitivity before performing a subsequent iteration of predicting data describing the subsequent state of the subsurface region. The predicted data is outputted.
81 Citations
16 Claims
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1. A computer implemented method for correlating predicted data describing a subsurface region with known data describing the subsurface region, the method comprising:
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obtaining data describing an initial state of the subsurface region;
predicting data describing a subsequent state of the subsurface region;
updating a likelihood measure that determines whether the predicted data is within an acceptable range of the obtained data, the updating being performed at least one of dynamically and interactively;using a computer to compare the predicted data with the obtained data using the likelihood measure; using a computer to determine a sensitivity of the predicted data if the predicted data is not within an acceptable range of the obtained data as measured by the likelihood measure; adjusting the data describing the initial state of the subsurface region based on the sensitivity before performing a subsequent iteration of predicting data describing the subsequent state of the subsurface region, wherein the adjusting is performed based on the likelihood measure having the largest change in sensitivity, such that at any point in parameter space only the likelihood measure that produces the largest change in sensitivity is chosen and drive the process until other measures catch up and the likelihood measure changes subsequently; and outputting the predicted data based on the adjusting. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method for producing hydrocarbons from an oil and/or gas field, the method comprising:
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obtaining data describing an initial state of a subsurface region containing at least a part of the oil and/or gas field; predicting data describing a subsequent state of the subsurface region;
updating a likelihood measure that determines whether the predicted data is within an acceptable range of the obtained data, the updating being performed at least one of dynamically and interactively;comparing the predicted data with the obtained data using the likelihood measure; determining a sensitivity of the predicted data if the predicted data is not within an acceptable range of the obtained data as measured by the likelihood measure; adjusting the data describing the initial state of the subsurface region based on the sensitivity before performing a subsequent iteration of predicting data describing the subsequent state of the subsurface region, wherein the adjusting is performed based the likelihood measure having the largest change in sensitivity, such that at any point in parameter space only the likelihood measure that produces the largest change in sensitivity is chosen and drive the process until other measures catch up and the likelihood measure changes subsequently; and extracting hydrocarbons from the oil and/or gas field using the predicted data if the predicted data is within an acceptable range of the obtained data. - View Dependent Claims (11, 12, 13, 14, 15)
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16. A computer system that is configured to correlate predicted data describing a subsurface region with obtained data describing the subsurface region, the computer system comprising:
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processor; and
a non-transitory machine-readable storage medium that stores tangible, machine-readable instructions for execution by the processor, the tangible, machine-readable instructions comprising;code that is configured to obtain data describing an initial state of the subsurface region; code that is configured to predict data describing a subsequent state of the subsurface region; code that is configured to update a likelihood measure that determines whether the predicted data is within an acceptable range of the obtained data, the updating being performed at least one of dynamically and interactively; code that is configured to compare the predicted data with the obtained data using the likelihood measure; code that is configured to determine a sensitivity of the predicted data if the predicted data is not within an acceptable range of the obtained data as measured by the likelihood measure; and code that is configured to adjust the data describing the initial state of the subsurface region based on the sensitivity before performing a subsequent iteration of predicting data describing the subsequent state of the subsurface region, wherein the adjusting is performed based on the likelihood measure having the largest change in sensitivity, such that at any point in parameter space only the likelihood measure that produces the largest change in sensitivity is chosen and drive the process until other measures catch up and the likelihood measure changes subsequently.
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Specification