Landmark Graphics Corporation

University of Houston System

Halliburton

Frontender Corporation

Halliburton Energy Services, Inc.

Luigi Saputelli

Gustavo Carvajal

Hasnain A Khan

Amit Kumar

Alvin Stanley Cullick

Priyesh Ranjan

Sheldon Burt Gorell

Hatem N Nasr

Karelis Alejandra Urrutia

Michael Nikolaou

Jose Antonio Rodriguez

Gerardo Mijares

Alejandro Garcia

Sathish Sankaran

Ankur Awasthi

Marko Maucec

William Douglas Johnson

Ajay Pratap Singh

Supriya Gupta

Seyed M Mirzadeh

Cesar Bravo

Sergio Henrique Guerra De Sousa

Jordani Rebeschini

Gustavo A Carvajal

Ranjan Priyesh

Burt Gorell Sheldon

The electrical submersible pump (ESP) is currently the fastest growing artificial-lift pumping technology. Deployed across 15 to 20 percent of oil-wells worldwide, ESPs are an efficient and reliable option at high production volumes and greater depths. However, ESP performance is often observed to decline gradually and reach the point of service interruption due to factors like high gas volumes, high temperature, and corrosion. The financial impact of ESP failure is substantial, from both lost production and replacement costs. Therefore, ESP performance in extensively monitored, and numerous workflows exist to suggest actions in case of break-downs. However, such workflows are reactive in nature, i.e., action is taken after tripping or failure. Therefore, a data-driven analytical framework is proposed to advance towards a proactive approach to ESP health monitoring based on predictive analytics to detect impending problems, diagnose their cause, and prescribe preventive action.

System and method for detecting, diagnosing, and correcting trips or failures of electrical submersible pumps

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

Systems and methods for reducing run time for a reservoir simulator model using a proxy model based on a neural network.

Systems and methods for reducing reservoir simulator model run time

Systems and methods for optimization of real time production operations. In one embodiment, a moving time horizon based parametric model provides fast predictions for production optimization in a short-term framework. In another embodiment, multiple technologies are selected in connection with asset performance workflows that are uniquely implemented in a multi-phase approach.

Systems and methods for optimization of real time production operations

The present disclosure describes systems and methods for performing multi-level reservoir history matching. At least some illustrative embodiments include a method that includes generating a first history-matched model using at least one updated model parameter derived from one or more existing model parameters, generating a set of second history-matched models by applying a probabilistic inversion to the first history-matched model, and deriving a set of third history-matched models from the set of second history-matched models. The method further includes generating dynamic simulation realization sets using each of the set of third history-matched models, ranking each of the set of third history-matched models based at least in part on the dynamic simulation realization sets, and presenting a production forecast to a user based on the highest ranked third history-matched models.

Multi-level reservoir history matching

Growing community of inventors

Houston, TX, United States of America

Luigi Saputelli