Conoco Inc.

Conocophillips Company

Gregory Joseph Jorgensen

Jerry Lee Kisabeth

Alan Royce Huffman

Partha Sarathi Routh

David W Bell

John B Sinton

Juan Andres Chavarria

The present invention is a method for determining a parameter of interest of a region of interest of the earth. At least one component of potential fields data is measured at a plurality of locations over a region of interest including a subterranean formation of interest. The potential fields data are selected from magnetic data and gravity data. An initial geophysical model is determined for the region including the subterranean formation of interest. For the model, geophysical tensor data is updated using a forward model at a plurality of locations using a High Order Compact Finite Difference method. A difference between the estimated model value and the measured value of the potential field measurements are determined, and the geophysical model is updated. The model is iteratively updated and compared to the measured data until the differences reach an acceptable level and the parameter of interest has been determined.

Modeling gravity and tensor gravity data using poisson's equation for airborne, surface and borehole applications

The present invention is a method for determining a parameter of interest of a region of interest of the earth using tensor data. At least one component of potential fields data is measured at a plurality of locations over a region of interest including a subterranean formation of interest. The potential fields data are selected from magnetic data and gravity data. An initial geophysical model is determined for the region including the subterranean formation of interest. For the model, geophysical tensor data is updated using a forward model at a plurality of locations using an interior method for constrained optimization. A difference between the estimated model value and the measured value of the potential field measurements are determined at the plurality of locations. Based on the this difference the geophysical model is updated. The model is iteratively updated and compared to the measured data until the differences reach an acceptable level and the parameter of interest has been determined.

Nonlinear constrained inversion method to determine base of salt interface from gravity and gravity tensor data

A method for modeling geological structures includes obtaining seismic data and using these data to derive an initial density model. Potential fields data is used to update the initial geophysical model by an inversion process using vector or tensor components of gravity and/or magnetic data. In regions having an anomalous density zone, the initial model includes a topographic or bathymetric surface and a 2D or 3D density model including the top of any zones of anomalous density. Potential fields data is then used to derive the lower boundary of the anomalous density zones by using an inversion process. The final density model from the inversion may be further refined using the seismic data in conjunction with the model obtained by inversion of the potential fields data. The model data are integrated two or three dimensions to determine an overburden stress for the subsurface that includes a proper treatment of the anomalous density zone. In an optional process, the seismic data can be processed in depth or time using the density model from the inversion to improve the quality of the velocity information adjacent to and beneath the anomalous bodies.

Method for gravity and magnetic data inversion using vector and tensor data with seismic imaging and geopressure prediction for oil, gas and mineral exploration and production

A method for determining formation pore pressure uses seismic data to derive an initial density model. Potential fields data are then used to derive the lower boundary of anomalous density zones (e.g., salt, shale diapirs, igneous or magmatic formations) by an inversion process. The model data are integrated vertically in two or three dimensions to determine an overburden stress for the subsurface that includes a proper treatment of the anomalous density zone. The velocity measurements determined from the seismic data are used to determine the effective stress based on appropriate relationships between effective stress and velocity. The fluid pressure is then obtained as the difference between the overburden stress and the effective stress.

Method for integrating gravity and magnetic inversion with geopressure prediction for oil, gas and mineral exploration and production

A method for modeling geological structures beneath anomalous density zones includes receiving seismic data and using this data to derive the top of a geologic model. Non-seismic data, such as gravity or magnetic data are used to derive the lower boundary of the geologic model in an inversion process. In one embodiment, the predicted parameters are combined with seismic data to obtain a depth image and to derive a velocity model in delineating formations of interest. In another embodiment, the processed seismic data is used to further constrain the inversion of the non-seismic data. Another novel aspect of the invention is the filtering of the non-seismic data during the inversion process in a manner that is consistent with Laplace's equation.

Method for integrating gravity and magnetic inversion data with model based seismic data for oil, gas and mineral exploration and production

A method for modeling geological zones having anomalous density includes determining the top of the anomalous zone from non-potential fields data. Potential fields data is then used to derive the lower boundary of the geologic anomalous zone. A lower boundary to a anomalous zone is formulated by predicting parameters representing the lower boundary within predetermined limits. This is done by using an inversion process on the potential fields data, such as measurements of gravity data, magnetic data. These may be in both vector and tensor form. The potential fields data is compared to the predicted fields from the results of the inversion process to obtain a difference between the two. If the difference exceeds a predetermined value, the parameters representing the anomalous zone are adjusted to improve the fit. When the lower boundary limits are reached or the difference between the model and the data is less than the predetermined value or convergence is attained, the anomalous zone has been determined.

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Ponca City, OK, United States of America

Gregory Joseph Jorgensen