Schlumberger Technology Corporation

Cameron International Corporation

University of Houston System

Halliburton

Frontender Corporation

Onesubsea IP UK Limited

Onesubsea LLC

Supriya Gupta

Aria Abubakar

David J Rossi

Luigi Saputelli

Michael Nikolaou

Cesar Bravo

Michael Van Spankeren

Andrew Acock

Werner Menz

Timothy J Roberts

Satya Deepthi Gopisetti

Miguel Angel Hernandez De La Bastida

David Rossi

A method includes receiving historical well-production data, and determining curve parameters based on the historical well production data. Determining the curve parameters includes accounting for uncertainty. The method also includes determining curve fits for the historical data based on the curve parameters using a plurality of models, calculating accuracy for each of the models based on a comparison of the curve fits to the well-production data, comparing the accuracy for each of the models using an information criteria that accounts for uncertainty, selecting one of the models based on the comparison, and determining an estimated ultimate recovery (EUR) for the well using at least the selected one of the models.

Probabilistic oil production forecasting

A chemical injection system for a resource extraction system includes a controller having a memory and a processor. The memory stores instructions that cause the processor to receive a first pressure from a first pressure sensor of the resource extraction system, receive a second pressure from a second pressure sensor of the resource extraction system, determine a flowrate of a produced fluid of the resource extraction system based on the first pressure and the second pressure, determine an ion concentration of the produced fluid, and adjust an injection rate of a chemical into the resource extraction system based on the flowrate of the produced fluid, the ion concentration of the produced fluid, or both.

Chemical injection system for a resource extraction system

A method for optimizing oil production includes receiving historical production data for one or more wells. A first model is generated based at least partially upon the historical production data. The production in the one or more wells is predicted based at least partially upon the first model. A second model is generated based at least partially on the predicted production. A production allocation is determined in the one or more wells to maximize production based at least partially upon the second model. The production allocation is then implemented.

Systems and methods for optimizing oil production

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

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Houston, TX, United States of America

Supriya Gupta