Toward Seamless Interoperability Between Real-Time Drilling Management and Control Applications

Cayeux, Eric; Daireaux, Benoît; Saadallah, Nejm; Alyaev, Sergey

doi:10.2118/194110-ms

Cited by 7 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the semantic networks that capture the how and why of drilling data generation need to refer to drilling real-time signals. A method to represent the meaning of drilling real-time signal in a computer interpretable way can be found in Cayeux et al (2019). The method uses semantic networks.…”

Section: Semantic Networkmentioning

confidence: 99%

Connecting Multilayer Semantic Networks to Data Lakes: The Representation of Data Uncertainty and Quality

Cayeux

Damski²,

Macpherson

et al. 2022

SPE Drilling &Amp; Completion

View full text Add to dashboard Cite

Summary Drilling oil and gas wells is a complex process involving many disciplines and stakeholders. This process occurs in a context where some pieces of information are unknown, or are often incomplete, erroneous, or at least uncertain. Yet, during drilling engineering and construction of a well, drilling data quality and uncertainty are barely addressed in an auditable and scientific way. Currently, there are few or no placeholders in engineering and operational databases to document uncertainty and its propagation. The Society of Petroleum Engineers (SPE) has formed a cross-disciplinary technical subcommittee to investigate how to describe and propagate drilling data quality and uncertainty. The subcommittee is a cooperation between the drilling system automation, wellbore positioning, and drilling uncertainty prediction technical sections. As the topic is vast and complex, a systematic method was adopted, where multiple user stories or pain points were generated and ranked with the most compelling user story analyzed in detail. From this approach, a series of multidisciplinary workflows (drilling data generators) can now be captured and described in terms of data quality and propagation of uncertainty. The paper presents details of one user story focused on capturing the description of the quality and uncertainty of depth measurements. Multiple use cases have been extracted from this single user story exemplifying how multiple stakeholders and disciplines manage, communicate, and understand the notion of wellbore depth and its relative uncertainty. Current data stores have the main objective of recording the results of processes but have very limited capabilities to store how the interdisciplinary processes generated and cross-related these results. The study explores the use of semantic networks to capture those multidisciplinary data relationships. A minimum vocabulary has been created using just a few tens of concepts that has sufficient expressiveness to describe all the extracted use cases, showing that the semantic network method has the potential to describe a broad range of complex drilling-related processes. The study also demonstrates that use of a multilayered graph, employing other notions that do not expressly refer to the processes that generated the data, can capture the description of how uncertainty propagates between each of those concepts.

show abstract

Section: Semantic Networkmentioning

confidence: 99%

Connecting Multilayer Semantic Networks to Data Lakes: The Representation of Data Uncertainty and Quality

Cayeux

Damski²,

Macpherson

et al. 2022

SPE Drilling &Amp; Completion

View full text Add to dashboard Cite

show abstract

OpenLab: Design and Applications of a Modern Drilling Digitalization Infrastructure

Saadallah

Gravdal

Ewald

et al. 2019

Day 1 Tue, May 14, 2019

View full text Add to dashboard Cite

The transition towards drilling automation in the oil and gas industry has increased the need for digital infrastructures for development and testing of new technology. This includes infrastructures to facilitate changes in work processes and technical competences. This paper describes the design and use of OpenLab Drilling, a digital infrastructure with applications in education, technology development and testing. OpenLab Drilling offers access to a high fidelity drilling process simulator capable of simulating transient hydraulics, temperature, torque and drag, and cuttings transport. Since 2018, the infrastructure has been publicly available for students, researchers and engineers who need realistic drilling data for technology development, demonstration and education. The simulated drilling data can be accessed by several means. First, through a user-friendly web application used as a tool for teaching the physics involved in drilling operations. Secondly, drilling data can be accessed programmatically through a web API or via programming language APIs written in MATLAB, Python and .NET. Thirdly, OpenLab offers a fast communication interface that can be used for applications that are closer to hardware, and which require a realistic Hardware in The Loop (HIL) infrastructure. This paper describes the objectives of OpenLab as a project, its system architecture, its simulation capabilities, the design of its web application, and its various communication interfaces. The paper also presents projects that uses OpenLab in education, research on machine learning, semantical representation of drilling data, and other industrial relevant activities. The paper is naturally divided in two parts: The design of the infrastructure, and its applications.

show abstract

Verification and Validation of Sensors and Systems Using Systems Engineering Methods

Wardt¹,

Wood

2020

Day 1 Tue, March 03, 2020

View full text Add to dashboard Cite

The demand for ensuring the reliability and accuracy of drilling sensor data is rapidly increasing with the rise of data analytics and automation. Proven systems engineering methodologies used in the aerospace industry offer the drilling industry a solution for assuring the reliability of sensor data and software processing of this data. This paper describes an approach for verification and validation (V&V) of sensors and systems leveraging experiences from the aerospace industry. Both oil and gas (O&G) and aerospace have high-value assets where failures can lead to significant loss of life and huge financial impacts. Commercial aviation, in particular, has achieved a remarkable improvement in safety over many decades through design standards requiring functions be precisely defined and their implementation be verified against those requirements. Avaition industry standards, such as DO-178C for Software Considerations in Airborne Systems and Equipment Certification, acknowledge that not all aircraft systems require the same attention. The standards define a range of classification from the most critical, such as autopilots, to the least critical, such as entertainment systems. This paper describes how mapping levels and requirements from DO-178C to oil and gas can assist in the improvement of data quality to the higher standards now required in drilling operations. A range of criticality provides the ability to seek a balance of costs vs. benefits for application to drilling systems to achieve the needed data quality.

show abstract

Toward Seamless Interoperability Between Real-Time Drilling Management and Control Applications

Cited by 7 publications

References 11 publications

Connecting Multilayer Semantic Networks to Data Lakes: The Representation of Data Uncertainty and Quality

Connecting Multilayer Semantic Networks to Data Lakes: The Representation of Data Uncertainty and Quality

OpenLab: Design and Applications of a Modern Drilling Digitalization Infrastructure

Verification and Validation of Sensors and Systems Using Systems Engineering Methods

Contact Info

Product

Resources

About