Real-Time Perforation Cluster Analysis Using Surface Pressure Data

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Solution Overview

Problem

Current hydraulic fracturing methods face challenges in determining effective perforation clusters in real-time, leading to uneven stimulation and reduced well production, due to the invasive and expensive nature of existing downhole diagnostic tools, which often require extensive processing and expert interpretation, and neglect the potential of surface data for improving downhole conditions.

Innovation Solution

A computer-implemented method using an autoencoder and convolutional neural network to analyze surface data from wellsite equipment during hydraulic fracturing operations, enabling real-time determination of effective perforation clusters and adjustment of operational parameters to optimize cluster effectiveness.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If downhole diagnostic measurements using fiber optic technology are used to determine effective perforation clusters, then measurement precision is improved, but device complexity and cost increase

Engineering Contradiction:

Improvedetermination accuracy of effective perforation clustersVSAvoidcomplexity of downhole diagnostic tools

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent uses surface-measured pressures as a proxy or copy of downhole conditions. Instead of directly measuring downhole parameters with complex fiber optic tools, the system infers downhole fracture behavior from surface pressure data collected during hydraulic fracturing operations, thereby achieving similar diagnostic capabilities with simpler equipment

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent introduces an intermediate computational model that translates surface pressure measurements into information about downhole perforation cluster effectiveness. This intermediary processing layer allows surface data to convey downhole conditions without requiring direct downhole sensors

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of information

If downhole diagnostic tools are deployed to assess perforation cluster effectiveness, then information accuracy is improved, but ease of operation deteriorates due to requiring expert interpretation

Engineering Contradiction:

Improveinformation quality about downhole conditionsVSAvoidease of data interpretation

Core Design Contradiction:

Loss of informationVSEase of operation

Solution Approach 1:

The patent implements a feedback mechanism where surface pressure data is continuously monitored and fed into a computational model that provides real-time or near-real-time assessment of perforation cluster effectiveness. This closed-loop system automatically translates raw data into actionable insights without requiring expert interpretation

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs self-interpretation of downhole conditions through automated computational models that process surface data and directly output assessments of perforation cluster effectiveness, eliminating the need for external expert analysis

Inventive Principle:

Principle #25Self-service

3Measurement precision

If expensive downhole diagnostic tools are used to determine effective perforation clusters, then measurement precision is improved, but productivity decreases due to waiting for production data

Engineering Contradiction:

Improveaccuracy of perforation cluster assessmentVSAvoidspeed of assessing treatment effectiveness

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent performs preliminary assessment of perforation cluster effectiveness during or immediately after the hydraulic fracturing treatment by analyzing surface pressure data. This allows operators to evaluate treatment success before waiting for production data to materialize, enabling faster decision-making and operational productivity

Inventive Principle:

Principle #10Preliminary action

4Device complexity

If surface data is used to determine effective perforation clusters, then device complexity is reduced, but measurement precision deteriorates due to insufficient data quality

Engineering Contradiction:

Improvesimplicity of surface-based systemVSAvoidaccuracy of downhole condition determination

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent replaces direct mechanical/downhole measurement systems with a computational approach that substitutes physical sensors with mathematical models. Surface pressure measurements are processed through computational algorithms that infer downhole conditions, replacing the need for complex downhole diagnostic hardware

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20230313653A1Effective perforation cluster determination from hydraulic fracturing data

Publication Date: 2023.10.05 SCHLUMBERGER TECH CORP

AI summary

Systems and methods presented herein relate to systems and methods for determining a number of effective perforation clusters created during hydraulic fracturing operations performed using wellsite equipment of a wellsite system based on surface data collected in substantially real-time during the hydraulic fracturing operations using an autoencoder/convolutional neural network architecture. In certain embodiments, the wellsite equipment of the wellsite system may be controlled in substantially real-time based on the determined number of effective perforation clusters insofar as the autoencoder/convolutional neural network architecture facilitates such real-time responsiveness.