Landmark Graphics Corporation

Baker Hughes Corporation

Honeywell G.m.b.h.

Hatem N Nasr

Alvin Stanley Cullick

Gustavo Carvajal

Amit Kumar

Priyesh Ranjan

Sheldon Burt Gorell

Luigi Saputelli

Hasnain A Khan

Karelis Alejandra Urrutia

Gustavo A Carvajal

Ranjan Priyesh

Burt Gorell Sheldon

Volker Krueger

John W Harrell

Firooz A Sadjadi

Roger W Fincher

Macmillan M Wisler

Michael E Bazakos

Ronald Gene Bland

John Barton Weirich

Robert Paul MacDonald

Joachim F Oppelt

Mike E Bazakos

Valeri Papanyan

Douglas W Johnson

William W Smith, Jr

Hossien Amehdi

Systems and methods for subsurface secondary and/or tertiary oil recovery optimization based on either a short term, medium term or long term optimization analysis of selected zones, wells, patterns/clusters and/or fields.

Systems and methods for subsurface oil recovery optimization

Predicting future hydrocarbon production. At least some of the illustrative embodiments are methods including: reading data regarding hydrocarbon production from a hydrocarbon producing field; producing at least one value indicative of future hydrocarbon production based on a data model and the data regarding hydrocarbon production; displaying, on a display device of a computer system, an indication of historic data regarding hydrocarbon production; and displaying, on the display device, an indication of the at least one value indicative of future hydrocarbon production.

Method and system of predicting future hydrocarbon production

The present invention provides a drilling system that utilizes an integrated bottom hole assembly. The bottom hole assembly contains sensors for determining the health of the bottom hole assembly, borehole condition, formation evaluation characteristics, drilling fluid physical and chemical properties, bed boundary conditions around and in front of the drill bit, seismic maps and the desired drilling parameters that include the weight on bit, drill bit speed and the fluid flow rate. A downhole processor controls the operation of the various devices in the bottom hole assembly to effect changes to the drilling parameters and the drilling direction to optimize the drilling effectiveness.

Drilling system with integrated bottom hole assembly

The present invention provides a drilling assembly with a stabilization system that stabilizes at least a section of the drilling assembly as a function of one or more downhole measured parameters or parameters of interest. One or more sensors carried by the drilling assembly measure the desired parameters of interest and a control system controls the stabilizer system in response to one or more determined parameters of interest to provide desired stabilization of a section of the drilling assembly. Measurement-while-drilling sensors whose measurements are affected by the parameters of interest are preferably disposed in the stabilized section. In one embodiment, the stabilization system includes at least two axially spaced apart stabilizers. Each such stabilizer includes a plurality of independently adjustable members that radially extend from the drilling assembly to exert force on the wellbore inside. A control circuit processes the signals from the sensors and in response thereto causes a power unit to independently adjust the force applied by the adjustable members so as to maintain a parameter of interest below a predetermined value. The measurement-while-drilling sensors take measurements while the stabilization system is activated, which may be during the drilling of the wellbore or during retrieval of the drilling assembly from the wellbore. In an alternative embodiment, the stabilization system includes only one stabilizer that may contain a plurality of independently adjustable members.

Stabilization system for measurement-while-drilling sensors

The present invention provides a drilling system for drilling oilfield boreholes or wellbores utilizing a drill string having a drilling assembly conveyed downhole by a tubing (usually a drill pipe or coiled tubing). The drilling assembly includes a bottom hole assembly (BHA) and a drill bit. The bottom hole assembly preferably contains commonly used measurement-while-drilling sensors. The drill string also contains a variety of sensors for determining downhole various properties of the drilling fluid. Sensors are provided to determine density, viscosity, flow rate, clarity, compressibility, pressure and temperature of the drilling fluid at one or more downhole locations. Chemical detection sensors for detecting the presence of gas (methane) and H,S are disposed in the drilling assembly. Sensors for determining fluid density, viscosity, pH, solid content, fluid clarity, fluid compressibility, and a spectroscopy sensor are also disposed in the BHA. Data from such sensors may is processed downhole and/or at the surface. Corrective actions are taken based upon the downhole measurements at the surface which may require altering the drilling fluid composition, altering the drilling fluid pump rate or shutting down the operation to clean wellbore. The drilling system contains one or more models, which may be stored in memory downhole or at the surface. These models are utilized by the downhole processor and the surface computer to determine desired fluid parameters for continued drilling. The drilling system is dynamic, in that the downhole fluid sensor data is utilized to update models and algorithms during drilling of the wellbore and the updated models are then utilized for continued drilling operations.

Drilling systems with sensors for determining properties of drilling fluid downhole

A performance measure of an ATR is made from ancillary target data and the ATR output. A parameter of the ATR is varied to determine the change an ATR performance due to the parameter variation. Separate performance in the form of a quadratic equation models provide performance as a function of parameter and metrics. The performance model is partially differentiated with respect to the parameter. The partial differentiation allows solution for the estimated metric.

Signal metric estimator

A knowledge and model based adaptive signal processor for ensuring optimum performance in automated object recognition (AOR) systems. The invention does not require ground-truth information but uses predictive performance models for parameter adaption and updates for the AOR. The performance models are representations of continuous surfaces that interpolate and extrapolate for data and scenarios not previously tested. The present invention increases probability of detection and decreases false detection alarm rates of AOR systems.

Knowledge and model based adaptive signal processor

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Houston, TX, United States of America

Hatem N Nasr