Peter S Kaufman

US Patent:

D634661, Mar 22, 2011

Filed:

Mar 22, 2010

Appl. No.:

29/349306

Inventors:

Garrett Y. Kaufman - Evergreen CO, US
Peter Kaufman - Marquette MI, US

Assignee:

Essex Electronics, Inc. - Carpinteria CA

International Classification:

1005

US Classification:

D10106

US Patent:

D634662, Mar 22, 2011

Filed:

Mar 22, 2010

Appl. No.:

29/349308

Inventors:

Garrett Y. Kaufman - Evergreen CO, US
Peter Kaufman - Marquette MI, US

Assignee:

Essex Electronics, Inc. - Carpinteria CA

International Classification:

1005

US Classification:

D10106

US Patent:

D639194, Jun 7, 2011

Filed:

Mar 22, 2010

Appl. No.:

29/349307

Inventors:

Garrett Y. Kaufman - Evergreen CO, US
Peter Kaufman - Marquette MI, US

Assignee:

Essex Electronics, Inc. - Carpinteria CA

International Classification:

1005

US Classification:

D101068

US Patent:

D639195, Jun 7, 2011

Filed:

Mar 22, 2010

Appl. No.:

29/349309

Inventors:

Garrett Y. Kaufman - Evergreen CO, US
Peter Kaufman - Marquette MI, US

Assignee:

Essex Electronics, Inc. - Carpinteria CA

International Classification:

1005

US Classification:

D101068

US Patent:

D686931, Jul 30, 2013

Filed:

Mar 22, 2012

Appl. No.:

29/416503

Inventors:

Garrett Kaufman - Evergreen CO, US
Peter Kaufman - Marquette MI, US

Assignee:

Essex Electronics, Inc. - Carpinteria CA

International Classification:

1005

US Classification:

D101068

US Patent:

D686932, Jul 30, 2013

Filed:

Mar 22, 2012

Appl. No.:

29/416506

Inventors:

Garrett Kaufman - Evergreen CO, US
Peter Kaufman - Marquette MI, US

Assignee:

Essex Electronics, Inc. - Carpinteria CA

International Classification:

1005

US Classification:

D101068

US Patent:

20080040086, Feb 14, 2008

Filed:

Aug 1, 2007

Appl. No.:

11/832290

Inventors:

Soraya Betancourt - Cambridge MA, US
Oliver Mullins - Ridgefield CT, US
Rimas Gaizutis - Kuala Lumpur, MY
ChengGang Xian - Tripoli, LY
Peter Kaufman - Highlands Ranch CO, US
Francois Dubost - Orgeval, FR
Lalitha Venkataramanan - Lexington MA, US

Assignee:

SCHLUMBERGER TECHNOLOGY CORPORATION - Cambridge MA

International Classification:

G01V 9/00
G06F 19/00
G06G 7/48

US Classification:

703010000, 702011000, 073152280

Abstract:

Formation fluid data based on measurements taken downhole under natural conditions is utilized to help identify reservoir compartments. A geological model of the reservoir including expected pressure and temperature conditions is integrated with a predicted fluid model fitted to measured composition and PVT data on reservoir fluid samples or representative analog. Synthetic downhole fluid analysis (DFA) logs created from the predictive fluid model can be displayed along the proposed borehole trajectory by geological modeling software prior to data acquisition. During a downhole fluid sampling operation, actual measurements can be displayed next to the predicted logs. If agreement exists between the predicted and measured fluid samples, the geologic and fluid models are validated. However, if there is a discrepancy between the predicted and measured fluid samples, the geological model and the fluid model need to be re-analyzed, e.g., to identify reservoir fluid compartments. A quantitative comparative analysis of the sampled fluids can be performed against other samples in the same borehole or in different boreholes in the field or region to calculate the statistical similarity of the fluids, and thus the possible connectivity between two or more reservoir regions.

US Patent:

20150363520, Dec 17, 2015

Filed:

Jan 13, 2014

Appl. No.:

14/762828

Inventors:

- Sugar Land TX, US
Benoit Couet - Belmont MA, US
Peter Kaufman - Highlands Ranch CO, US
Oliver C. Mullins - Houston TX, US

International Classification:

G06F 17/50
E21B 49/08

Abstract:

A method for deriving VOI for a hydrocarbon-bearing reservoir fluid model based on DFA data (“true fluid model”) versus an “incorrect fluid model” includes calculating first, second and third objective functions that are based on NPV(s) of simulated production by a reservoir simulator with different configurations. For the first objective function, the simulator is configured with the incorrect fluid model and control variables that are optimized to derive a first group of control variable values. For the second objective function, the simulator is configured with the true fluid model and the first group of control variable values. For the third objective function, the simulator is configured with the true fluid model and control variables that are optimized to identify a second group of control variable values. The objective functions can be deterministic, or can include statistics that account for uncertainty. A visualization of such results with uncertainty is also described.