Halliburton Energy Services, Inc.

Schlumberger Technology Corporation

Vestas Wind Systems A/s

Daniel Viassolo

Burkay Donderici

Luis Emilio San Martin

Yinghui Lu

Paris Smaragdis

Avinash Vinayak Taware

Alberto Quintero

Muralidhar Seshadri

Joni Lie

Jason Hoffman

Abdul Qadir Shabbir

Jianhui Zhang

Randy Coles

Clifford Lloyd Macklin

Jason Daniel Dykstra

Ahmed Elsayed Fouda

Wesley Neil Ludwig

Nam Nguyen

Mehdi Sebdani Mazrooee

Sridhar Sahukari

Rajesh Kumar Bade

David Alan Welsh

Marco Alioto

Daniel Rex Barker

Bo Hesselbæk

Brett Rollow

Juan Santos

Abhishek Dubey

Nicholas Williard

Daniel M Saban

Sherin Thomas

Ezequiel Saavedra

Magson Zanette

Yuchong Zhang

Bo Hesselbaek

Yonatan Abebe

Aditya Upadhye

Gerald Murphy

Se Un Park

Lucile Baur

Laurent Cotelle

Ahmed Fikri Ali Mosallam

Mihitha Nutakki

A scalable digital fleet management system may be leveraged by organizations with high-volume high-value assets. In such scenarios, predictive analytics for tool health enables decision-making in terms of planning, maintenance, end-of-life replacement, tool selection, etc. An end-to-end solution spans all the way from gathering live tool data to visual representations of tool health. Long-term fleet management can be accomplished through a consistent evaluation of the fleet performance profile. Predictive analysis can anticipate maintenance needs and resultant downtimes and help improve scheduling of procurement and distribution of the fleet. Framework deployment focuses on topics related to developing an end-to-end solution architecture, scaling, and deploying it on demand, and maintaining it going forward. Sustenance and algorithm development focuses on defining a risk index, developing machine learning algorithms for different tools, defining a single comparison metric, and deciding when to trigger automated re-training for each tool.

Systems and methods for predicting and managing tool assets

Methods for evaluating sensor data to predict when the sensor should be recalibrated are described. The methods include a model that utilizes current wellbore data as input for the recalibration prediction.

In-situ evaluation of gauges

Disclosed are tools, systems, and methods for detecting one or more underground acoustic sources and localizing them in depth and radial distance from a wellbore, for example, for the purpose of finding underground fluid flows, such as may result from leaks in the well barriers. In various embodiments, acoustic-source detection and localization are accomplished with an array of at least three acoustic sensors disposed in the wellbore, in conjunction with array signal processing.

Acoustic array signal processing for flow detection

A blowout preventer (BOP) health monitoring system includes a controller having a tangible, machine readable-medium storing one or more instructions executable by a processor. The one or more instructions are configured to receive feedback from a sensor, where the feedback is indicative of a pressure within a BOP stack over time, determine a position of a plurality of components of the BOP stack, determine a start point and an end point of a pressure test based on the feedback, determine a decay and a hold duration of the pressure test based on the feedback, the start point, and the end point, determine a health index of the BOP stack based on the decay and the hold duration, and provide an output indicative of a condition of the BOP stack based on the health index.

BOP health monitoring system and method

Described herein are tools, systems, and methods for detecting, classifying, and/or quantifying underground fluid flows based on acoustic signals emanating therefrom, using a plurality of acoustic sensors disposed in the wellbore in conjunction with array signal processing and systematic feature-based classification and estimation methods.

Acoustic downhole leak classification and quantification

A method includes deploying an electromagnetic (EM) defect detection tool in a borehole having one or more tubular strings. The method also includes collecting measurements by the EM defect detection tool as a function of measured depth or position. The method also includes using the measurements and a first inversion process to determine a defect in the one or more tubular strings. The first inversion process involves a cost function having a misfit term and having a stabilizing term with nominal model parameters. The method also includes performing an operation in response to the determined defect.

Electromagnetic (EM) defect detection methods and systems with enhanced inversion options

An automated system for assessing pumpoff risk can be used to warn operators and/or control machinery in order to avoid pumpoff. Pumpoff risk is assessed through the use and/or comparison of one or more models of pumpoff risk. These models can include a sand build up model (e.g., to determine the threshold buildup size where pumpoff risk is too great), a line speed increase model (e.g., to determine the maximum flow rate allowable given a maximum support line feed rate), a residual error comparison model (e.g., to compare the deviation of predicted tension from actual tension), and a statistical analysis model (e.g., to determine likelihood of pumpoff given statistical probability of each of a multitude of possible scenarios).

Assessment of pumpoff risk

The acoustic energy induced by a transmitter module of an acoustic logging tool is dependent on several factors. In some implementations, the induced acoustic energy is dependent on the electromagnetic energy input into the transmitter module, the response behavior of the transmitter module, and the operating conditions of the transmitter module. Variation in one or more of these factors can result in a corresponding variation in the induced acoustic. Thus, a desired acoustic signal can be produced by applying an appropriately selected input signal to the transmitter module, while accounting for other factors that influence the output of the transmitter module.

Control of acoustic transmitters for downhole measurement or logging

A pump-down method includes deploying a tool in a well via a wireline and measuring a tension of the wireline. The method also includes determining a difference between the measured tension and a reference tension. The method also includes updating at least one of a pump rate and a wireline speed used for pump-down of the tool based on the difference and at least one control parameter obtained at least in part from prediction model results.

Model-based pump-down of wireline tools

A resistivity logging tool includes a plurality of excitation electrodes, at least one return electrode, and a plurality of monitor electrodes. The resistivity logging tool also includes a controller that determines a set of independent currents to be emitted by at least some of the plurality of excitation electrodes during an excitation cycle based on at least one measured downhole parameter and at least one predetermined constraint.

Resistivity logging tool with excitation current control

Controlling cable () tension and tool () position in a well () may include controlling either or both of tool position and cable tension independently by regulating reel () angle and flow rate of a fluid pumped over the tool. Thus, despite physical interdependency of tool position and cable tension, the tool may be controlled such that its position is changed while cable tension remains constant, or its position is held constant while cable tension is changed. In addition, actual downhole tool position, cable tension, and other actual values may be estimated using an observer.

Cable system control using fluid flow for applying locomotive force

A resistivity logging system includes a plurality of excitation electrodes, at least one return electrode, and a plurality of monitor electrodes. The resistivity logging system also includes a controller that determines a level of excitation current to be emitted by at least one of the plurality of excitation electrodes during a subsequent excitation cycle based on a comparison of measured downhole parameter values corresponding to excitation current emitted in two previous excitation cycles.

Resistivity logging tool with excitation current control based on multi-cycle comparison

Embodiments for determining an optimal configuration for a renewable energy power plant are provided. The power plant, which may be a virtual power plant, includes one or more generator devices and one or more energy storage devices that may be operated to undertake one or more operating applications. Data indicative of the power output of the power plant and the voltage level at the energy storage system connection point is received as an input. Using this data, combinations of energy storage devices and operating applications stored on a database are processed and, for each energy storage and operating application pair, a value indicative of the profitability of undertaking the operating application is determined. These values are used to rank the combinations of energy storage devices and operating applications according to their profitability, which in turn is used to select the most efficient or profitable application/energy storage pair.

Renewable energy configurator

An example downhole tool includes a tool body and a multi-layer sensor coupled to the tool body. The multi-layer sensor may include a first transmitter coupled to a first conformable element and a first receiver coupled to a second conformable element. One of the first conformable element and the second conformable element may overlay the other of the first conformable element and the second conformable element.

Multi-layer sensors for downhole inspection

Method, system, and computer program product for estimating a consumed battery life of a battery used in a wind energy application. An energy storage system for use with a wind farm includes a rechargeable battery configured to be selectively charged by wind turbines or the power grid and configured to be selectively discharged to a power grid. One or more remaining battery life algorithms are used to compute a consumed battery life for the battery. A battery life curve characteristic of the battery and a state of charge profile are used as inputs to each remaining battery life algorithm.

Estimation of remaining battery life in a wind energy application

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Katy, TX, United States of America

Daniel Viassolo