A method for extracting hydrocarbons from an oil and/or gas field in a subsurface region, wherein the method is computer-implemented to correlate predicted data describing a subsurface region with obtained data describing the subsurface region, the computer-implemented method comprising: obtaining data describing an initial state of the subsurface region; predicting data describing a subsequent state of the subsurface region based on the initial state; 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; outputting the predicted data; and the method further comprises 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 CHARACTERIZED IN THAT THE computer-implemented METHOD COMPRISES THE STEPS OF: 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; comparing the predicted data with the obtained data using at least a second likelihood measure if the sensitivity does not indicate an acceptable change in the comparison of the predicted data with the obtained data, and adjusting the obtained data describing the initial state of the subsurface region based on the likelihood measure that produces the largest change in sensitivity, such that at any point in the parameter space, only one likelihood measure will be active and drive the process until other measures catch up and the likelihood measure changes subsequently, before performing a subsequent iteration of predicting data describing the subsequent state of the subsurface region. obtaining data describing an initial state of the subsurface region; predicting data describing a subsequent state of the subsurface region based on the initial state; 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; outputting the predicted data; and the method further comprises 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 CHARACTERIZED IN THAT THE computer-implemented METHOD COMPRISES THE STEPS OF: 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; comparing the predicted data with the obtained data using at least a second likelihood measure if the sensitivity does not indicate an acceptable change in the comparison of the predicted data with the obtained data, and adjusting the obtained data describing the initial state of the subsurface region based on the likelihood measure that produces the largest change in sensitivity, such that at any point in the parameter space, only one likelihood measure will be active and drive the process until other measures catch up and the likelihood measure changes subsequently, before performing a subsequent iteration of predicting data describing the subsequent state of the subsurface region. 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; comparing the predicted data with the obtained data using at least a second likelihood measure if the sensitivity does not indicate an acceptable change in the comparison of the predicted data with the obtained data, and adjusting the obtained data describing the initial state of the subsurface region based on the likelihood measure that produces the largest change in sensitivity, such that at any point in the parameter space, only one likelihood measure will be active and drive the process until other measures catch up and the likelihood measure changes subsequently, before performing a subsequent iteration of predicting data describing the subsequent state of the subsurface region.