Study
Commercial ProductionHigh ImpactStrong effect

Automated Field Monitoring Boosts Hydrocarbon Production Efficiency

Integrating subsurface and surface sensor data with expert systems and automation can significantly enhance operational efficiency and decision-making in hydrocarbon production.

SPE Annual Technical Conference and Exhibition · 2002

01

Key Findings

  • 01Integration of sensor data with expert systems and flow simulation enables seamless data-to-production modeling.
  • 02Automated processes and distributed decision-making lead to unprecedented operational efficiency.
  • 03Enterprise-level integration through a corporate portal realizes the intelligent field concept.
  • 04Empowers operating companies to improve real-time operational decision-making and production optimization.
02

Application

Design takeaway

Implement integrated sensor networks and automated decision-making systems to optimize complex industrial processes.

How to apply

In any industrial setting with complex, dynamic processes, consider developing a system that collects real-time data from sensors, processes it through an expert system or AI, and uses automated controls to adjust operations for optimal performance.

Project actions

  • 01When proposing a system, clearly define the data inputs, processing logic, and outputs.
  • 02Consider the potential for human oversight and intervention in automated systems.
03

Method & Evidence

AimTo investigate the effectiveness of an integrated production optimization model for achieving the 'intelligent field' concept in hydrocarbon reservoirs.
MethodConceptual model development and system integration proposal.
ProcedureThe proposed model integrates subsurface and surface sensor data, real-time data delivery, field-level data integration, automated task generation, feedback mechanisms via remotely operated tools, distributed decision-making through expert systems, and an interface with a corporate portal for management information systems (MIS).
ContextHydrocarbon exploration and production (E&P) industry, particularly in mature or geologically complex reservoirs.

Variables

IV["Integration of subsurface and surface sensor data","Real-time data delivery","Expert systems","Remotely operated tools","Business process automation"]
DV["Operational efficiency","Production output","Decision-making speed and quality","Operating costs"]
CV["Reservoir complexity","Fluid properties","Market volatility (as a driver for optimization)"]
04

Strengths & Limitations

Strengths

  • +Addresses a critical need for production optimization in challenging environments.
  • +Proposes a comprehensive, integrated system architecture.
  • +Highlights the potential for significant efficiency gains.

Limitations

The proposed system relies heavily on the accuracy and reliability of sensors and the intelligence of the expert systems. Real-world implementation may face challenges with data noise, system integration, and maintenance.

Reliability & validity

The reliability and validity of the proposed system would depend on the accuracy of the sensors, the robustness of the expert system's algorithms, and the effectiveness of the integration between different components. Empirical testing would be required to establish these.

Think critically

What are the ethical implications of relying heavily on automated decision-making in critical industrial operations, and what safeguards should be in place?

05

Design Principles

"Leverage real-time data and intelligent automation for dynamic process optimization."

In resource-intensive industries, optimizing production is critical for economic viability. This research highlights how advanced data integration and automated processes can lead to substantial improvements in output and cost reduction, directly impacting profitability and operational sustainability.

06

What This Means for Your Design

Imagine a smart factory where sensors constantly tell a computer what's happening, and the computer automatically makes adjustments to keep everything running perfectly and producing the most goods.

How to use in your project

  • 1.Reference this study when discussing the benefits of integrated data systems and automation in optimizing production processes for your design project.
07

Add to My Project

08

Quick Cite

(2002). Applied Production Optimization: i-Field. SPE Annual Technical Conference and Exhibition. https://doi.org/10.2118/77608-ms Retrieved from https://designdex.org/study/f7b1befe-d222-4a4a-b8e1-ee4d30bbae8a/automated-field-monitoring-boosts-hydrocarbon-production-efficiency

Paragraph starter

The 'Applied Production Optimization: i-Field' research by Sengul and Bekkousha (2002) demonstrates the significant potential of integrating subsurface and surface sensor data with expert systems and automation to create an 'intelligent field' concept. This approach enhances operational efficiency and decision-making in complex production environments, offering a valuable model for optimizing industrial processes through real-time data utilization and automated control.

09

Source

SPE Annual Technical Conference and Exhibition

Applied Production Optimization: i-Field

journal · 2002

View source

Questions about this research

What does the research say about automated field monitoring boosts hydrocarbon production efficiency?
Implement integrated sensor networks and automated decision-making systems to optimize complex industrial processes. Evidence: SPE Annual Technical Conference and Exhibition (2002).
Why does "Automated Field Monitoring Boosts Hydrocarbon Production Efficiency" matter for design?
In resource-intensive industries, optimizing production is critical for economic viability. This research highlights how advanced data integration and automated processes can lead to substantial improvements in output and cost reduction, directly impacting profitability and operational sustainability.
How can designers apply this research?
Implement integrated sensor networks and automated decision-making systems to optimize complex industrial processes.
What were the main findings?
Integration of sensor data with expert systems and flow simulation enables seamless data-to-production modeling.. Automated processes and distributed decision-making lead to unprecedented operational efficiency.. Enterprise-level integration through a corporate portal realizes the intelligent field concept.. Empowers operating companies to improve real-time operational decision-making and production optimization.
What research method was used?
Conceptual model development and system integration proposal..
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2002 journal from SPE Annual Technical Conference and Exhibition.
What should I do differently in my next project?
In any industrial setting with complex, dynamic processes, consider developing a system that collects real-time data from sensors, processes it through an expert system or AI, and uses automated controls to adjust operations for optimal performance.
What are the limitations?
The study is conceptual and relies on proposed system integration rather than empirical testing of a deployed system. The effectiveness of expert systems is dependent on the quality of their programming and the data they receive.
Is there evidence that decision-making systems affects design outcomes?
By combining real-time data from sensors with smart decision-making systems and automation, hydrocarbon fields can operate much more efficiently, leading to better production outcomes. In resource-intensive industries, optimizing production is critical for economic viability. This research highlights how advanced data Source: SPE Annual Technical Conference and Exhibition (2002).
Where does this production research apply?
Hydrocarbon exploration and production (E&P) industry, particularly in mature or geologically complex reservoirs. It sits within commercial production research on designdex.org.

Related research topics

decision-making systems design research · evidence on decision-making systems · does decision-making systems improve design outcomes · production studies for designers · decision-making systems and production findings · commercial production research evidence