Statoil Petroleum As

Equinor Energy As

Torgeir Wiik

Janniche Iren Nordskag

Anh Kiet Nguyen

Ketil Hokstad

Kenneth Duffaut

Emmanuel Causse

Lars Ole Løseth

Christine Fichler

Rune Kyrkjebø

Anders Dræge

Fredrik Andersson

Frank Agnar Maao

Per Atle Olsen

Eirik Øverland Dischler

Adriana Citlali Ramírez-Pérez

Lars Ole Loseth

Eirik Overland Dischler

Anders Draege

Rune Kyrkjebo

Eirik ØVerland Dischler

A method of estimating a set of physical parameters, the method comprising iteratively inverting an equation to minimise an error between simulated data and measured data and to provide an estimated set of physical parameters, wherein said iteratively inverting comprises at least a first inversion step and a second inversion step and wherein the simulated data depend on a model vector representing the set of physical parameters, applying a compression operator to the model vector representing the set of physical parameters to reduce the number of free variables and to produce a compressed model vector and varying the compression operator between the first inversion step and the second inversion step.

Model compression

The present invention relates to a method of processing seismic data. The method may include calculating a number of calculated structure tensors for each of a number of seismic data lines, the seismic data lines being spatially distributed about an area of the surface of the Earth. The method also may include interpolating the calculated structure tensors to find interpolated structure tensors in a region of the area between the lines of the seismic data lines, and calculating calculated seismic data from the interpolated structure tensors.

Method and apparatus for measuring seismic data

A method of estimating electromagnetic parameters of a geological structure, comprising: providing controlled source electromagnetic, CSEM, data of the structure, calculating a numerical model representing electromagnetic parameters of the structure and generating simulated CSEM data, discretising the numerical model based on prior knowledge of the structure, defining a functional for minimising the distance between said simulated CSEM data and said CSEM data, wherein the functional comprises a regularisation term which depends on prior knowledge of said structure.

Inversion method

A method is disclosed for the estimation of subsurface temperature distributions from a 3-dimensional heat conductivity model for a geological formation. The method may be characterized by the following steps: (a) obtaining measured data corresponding to a geological subsurface formation of interest including seismic survey data, in-well temperature, seafloor or surface heat flux measurements and laboratory-based measurements of core porosity, (b) estimating a relationship between seismic velocity and heat conductivity, wherein seismic velocity is linearly dependent on porosity and heat conductivity is exponentially or linearly dependent on porosity, and (c) calibrating the model to the measured in-well data and laboratory-based measurements of core porosity.

Temperature modeling constrained on geophysical data and kinematic restoration

A method for estimating saturation using mCSEM data and stochastic petrophysical models by quantifying the average water saturation in a reservoir given the transverse resistance (TR) obtained from mCSEM data, including the following steps: a) obtaining mCSEM survey data from a subsurface region of interest, b) performing an inversion of the obtained mCSEM data, c) subtracting a background resistivity trend from the mCSEM inversion data from the resistivity trend of the mCSEM inversion data from inside a hydrocarbon reservoir, d) estimating the location of an anomaly in the mCSEM inversion data, e) estimating the magnitude of the transverse resistance associated with an anomaly from the mCSEM inversion data, f) estimating an initial average reservoir saturation corresponding to transverse resistance using a stochastic petrophysical model and Monte Carlo simulation connecting reservoir parameters to transverse resistance, and g) integrating the obtained saturation distribution as a function of transverse resistances over the assumed distribution of transverse resistances to obtain a final estimation of the fluid saturation probability.

Saturation estimation using mCSEM data and stochastic petrophysical modeling

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Trondheim, Norway

Torgeir Wiik