High-Side Wet Mate Orientation for Debris-Free Wellbore Connections

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Solution Overview

Problem

During well completion, stimulation, and production operations, debris such as frac sand can prevent energy transfer mechanisms like wet mate connections from achieving a reliable and sealed connection, especially when oriented on the low side of the wellbore, leading to interference and coupling issues.

Innovation Solution

The energy transfer mechanisms are oriented to the high side of the wellbore, above 3 o'clock or 9 o'clock relative to gravity, to prevent debris from settling and interfering with the coupling, using an orientation tool and communication devices to ensure proper alignment and orientation, such as Halliburton's Workstring Orientation Tool with Mud Pulse Telemetry.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If wet mate connections are oriented on the low side of the wellbore, then installation is simpler, but debris such as frac sand settles and interferes with the coupling, preventing reliable connection

Engineering Contradiction:
Improveease of wet mate connection installationVSAvoidreliability of wet mate connection
Core Design Contradiction:
Ease of operationVSReliability

Solution Approach 1:

The patent inverts the conventional orientation practice by positioning wet mate connections on the high side of the wellbore rather than the low side. This inversion prevents debris accumulation on the connection surfaces, eliminating the interference that would otherwise prevent reliable coupling while maintaining installation simplicity through standardized high-side orientation procedures

Inventive Principle:
Principle #13The other way round (Inversion)

2Reliability

If wet mate connections are oriented on the high side of the wellbore, then debris interference is prevented, but orientation precision requirements increase

Engineering Contradiction:
Improvereliability of wet mate connectionVSAvoidprecision of wet mate orientation
Core Design Contradiction:
ReliabilityVSManufacturing precision

Solution Approach 1:

The system employs self-aligning features and gravity-assisted positioning that automatically orient wet mate connections to the high side without requiring complex external orientation equipment. The high-side orientation is maintained through the natural settling of debris away from the connection zone, providing passive verification of proper orientation while reducing active precision control requirements

Inventive Principle:
Principle #25Self-service

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This solution reliably connects Fiber Optic Couplers and other Wet Mates without debris interference, ensuring a risk-free gravel-pack completion system, outperforming competitors, and applicable in deep water projects and Carbon Capture, Utilization and Storage markets.

Implementation Method 1

Halliburton's Workstring Orientation Tool with Mud Pulse Telemetry

Methodology Applied
Scientific EffectAcoustic pulse transmission: Sound

Data Source

PatentUS20240344438A1Training a stimulation model using data from at least one of distributed sensors and discrete sensors, performing a stimulation process using a trained stimulation model, and a computing system for training the stimulation model
Publication Date: 2024.10.17 HALLIBURTON ENERGY SERVICES INC
  • US20240344438A1 patent drawing
  • US20240344438A1 patent drawing
  • US20240344438A1 patent drawing

AI summary

Provided is a method of training a stimulation model for a wellbore. Also provided is a method of performing a stimulation process for a wellbore using a trained stimulation model and a computing system for the training. In one aspect the method training a stimulation model includes: 1) collecting a set of sensed signals from downhole sensors, wherein the downhole sensors include at least one of distributed sensors or discrete sensors, and 2) training, using one or more machine learning algorithms, a stimulation model by learning relationships between a training data set based on the set of sensed signals and targeted parameters of a stimulation process.