Richard G Houck, 67Avon, NC

6999879, Feb 14, 2006

Feb 13, 2004

10/778864

Richard T. Houck - Houston TX, US

ExxonMobil Upstream Research Company - Houston TX

G06F 19/00

702 14, 703 5

A method of devising infill strategy in planning a seismic survey, using Monte Carlo methods and value-of-information theory to assess the probable value in well pay-out of a given increase in survey costs to reduce artifact degradation of the quality of the seismic data, where the seismic data will be used to make well-drilling decisions.

7792766, Sep 7, 2010

Mar 20, 2006

11/886171

Richard T. Houck - Houston TX, US
Dmitriy Pavlov - The Woodlands TX, US

ExxonMobil Upstream Research Co. - Houston TX

G06F 19/00
G06F 17/00
G06G 7/48
H04H 9/00

706 12, 703 10, 702 1, 702 2, 702 6, 702 11, 367 73, 367 86

Method for determining an expected value for a proposed reconnaissance electromagnetic (or any other type of geophysical) survey using a user-controlled source. The method requires only available geologic and economic information about the survey region. A series of calibration surveys are simulated with an assortment of resistive targets consistent with the known information. The calibration surveys are used to train pattern recognition software to assess the economic potential from anomalous resistivity maps. The calibrated classifier is then used on further simulated surveys of the area to generate probabilities that can be used in Value of Information theory to predict an expected value of a survey of the same design as the simulated surveys. The calibrated classifier technique can also be used to interpret actual CSEM survey results for economic potential.

8185313, May 22, 2012

Dec 9, 2009

13/142560

Richard T. Houck - Houston TX, US

ExxonMobil Upstream Research Company - Houston TX

G01V 3/18

702 7

A probabilistic method for classifying observed CSEM response for a resistive anomaly to classify the response into multiple geologic categories indicative of hydrocarbon production potential. Each category is assigned a prior probability (). For each category, conditional joint probability distributions for observed CSEM data in the anomaly region are constructed () from rock property probability distributions () and a quantitative relationship between rock/fluid properties and the CSEM data (). Then, the joint probability distributions and prior probabilities for each category () are combined with observed data () and used in Bayes' Rule () to update the prior category probabilities (). Seismic data may be used to supplement CSEM data in the method.

2011019, Aug 18, 2011

Dec 4, 2009

13/124795

Richard T. Houck - Houston TX, US
Graham A. Winbow - Houston TX, US

G01V 1/28

367 72

Techniques are disclosed for performing time-lapse seismic monitor surveys with sparsely sampled monitor data sets. A more accurate 3D representation (e.g., image) of a target area (e.g., a hydrocarbon bearing subsurface reservoir) is constructed using the sparsely sampled monitor data set (). The sparsely sampled monitor data set may be so limited that it alone is insufficient to generate an accurate 3D representation of the target area, but accuracy is achieved through use of certain external information (). The external information may include information accurately identifying a shape of the seismic reflector(s) present in the target area. The shape may be predetermined from a fully sampled base survey, and used to enable an accurate 3D representation () of the target area to be later generated for a monitor survey using a sparsely sampled monitor data set.

2012001, Jan 19, 2012

Dec 4, 2009

13/127133

Richard T. Houck - Houston TX, US
Grant A. Gist - Houston TX, US
Dachang Li - Katy TX, US

G01V 1/00

367 72

Techniques are disclosed for performing time-lapse monitor surveys with sparsely sampled monitor data sets. An accurate 3D representation (e.g., image) of a target area (e.g., a hydrocarbon bearing subsurface reservoir) is constructed () using the sparsely sampled monitor data set (). The sparsely sampled monitor data set may be so limited that it alone is insufficient to generate an accurate 3D representation of the target area, but accuracy is enabled through use of certain external information (). The external information may be one or more alternative predicted models () that are representative of different predictions regarding how the target area may change over a lapse of time. The alternative models may, for example, reflect differences in permeability of at least a portion of the target area. The sparsely sampled monitor data set may then be processed to determine () which of the alternative models is representative of the target area.

2012001, Jan 19, 2012

Dec 4, 2009

13/127145

Richard T. Houck - Houston TX, US
Ramesh Neelamani - Houston TX, US

ExxonMobil Upstream Research Company - Houston TX

G01V 1/00

367 72

Techniques are disclosed for performing time-lapse monitor surveys with sparsely sampled monitor data sets (). An accurate 3D representation (e.g., image) of a target area (e.g., a hydrocarbon bearing subsurface reservoir) is constructed using the sparsely sampled monitor data set (e.g., seismic data set). The sparsely sampled monitor data set may be so limited that it alone is insufficient to generate an accurate 3D representation of the target area, but accuracy is achieved through use of certain external information (). The external information may include predetermined base survey data from a first time that is used () to interpolate data that is not recorded in the sparsely sampled monitor data set to derive a fully sampled monitor data set that can be processed (e.g., using conventional processing techniques) for determining an accurate representation of the target area at a second time.