Method For Predicting Differences In Subsurface Conditions

A method for producing hydrocarbons from an oil and/or gas field, the method comprising:

• obtaining a data set representative of a first subsurface condition in the oil or gas field, the data set comprising a plurality of data points;
• defining at least one layer comprising a plurality of data points in the data set, the at least one layer representing a subsurface region in the oil or gas field that is predicted to contain hydrocarbons;
• performing an exhaustive search methodology using a set of parameters corresponding to the second subsurface condition for each of the plurality of data points in the data set that relate to the at least one layer or to a lesser number of such data points that are predicted to have a significant impact on data fit below, wherein synthetic geophysical data, generated using a model having the first and second subsurface conditions as a model parameters, is compared to measured geophysical survey data, and a best data-fit set of second subsurface condition values is selected;
• performing a statistical analysis to determine whether error in the data fit of the synthetic data relative to the measured data is significantly reduced if one of the at least one layer is subdivided into at least two layers;
• dividing the at least one layer into at least two layers if the error is significantly reduced;
• predicting a model showing variation of the second subsurface condition at a time when the measured geophysical survey data were obtained;
• and
• extracting or managing production of hydrocarbons from the oil or gas field using the predicted model.

A tangible, machine-readable medium, comprising:

• code adapted to represent at least one layer comprising a plurality of data points in a data set representing a first subsurface condition, the at least one layer representing a subsurface region that is predicted to contain hydrocarbons;
• code adapted to perform an exhaustive search methodology using a set of parameters corresponding to a second subsurface condition for each of the plurality of data points in the first data set that relate to the at least one layer or to a lesser number of such data points that are predicted to have a significant impact on data fit below, wherein synthetic geophysical data, generated using a model having the first and second subsurface conditions as a model parameters, is compared to measured geophysical survey data, and a best data-fit set of second subsurface condition values is selected;
• code adapted to perform a statistical analysis to determine whether error in the data fit of the synthetic data relative to the measured data is significantly reduced if one of the at least one layer is subdivided into at least two layers;
• and
• code adapted to divide the at least one layer into at least two layers if the error is significantly reduced.