Saudi Aramco Intelligent Field Data Exchange Experience Using PRODML: Case Study

An approach to identify design and manufacturing features from a data exchanged part model

Flexible exchange of farming device data

With recent advancements in field instrumentation technologies and the expansion of Saudi Aramco's state-of-the-art intelligent field infrastructure, a wealth of valuable knowledge is being derived from integrating intelligent field capabilities with best-in-class reservoir management practices. Intelligent field applications have already leveraged real-time reservoir surveillance data to enable timely responses and fast interventions for events observed from real-time data. As richer and more granular datasets are collected from the field, deeper insights and understanding of the reservoirs are gained at fractions of the needed time where intelligent field capabilities are not present. The abundance and timely processing of intelligent field data enables more tactical adjustments in reservoir management strategy. This type of real-time reservoir management has already shown great value in reservoir performance optimization. This paper discusses several cases in which an intelligent field enabled early detection of anomalies and shortened the time-cycle for an analysis, decision and action. The first case discusses the early detection of localized pressure support anomalies due to poor rock quality. This timely detection, facilitated by intelligent field infrastructure, enabled quick decision making and actions to overcome the anomaly. The second case shows how real-time monitoring of reservoir pressure propagation has helped to detect over-injection in two injectors with malfunctioned meters. This has allowed timely action to ensure uniform pressure propagation and repair of faulty equipment. The third case shows that the utilization of real-time data has assisted in detecting a casing leak in a short time, which helped in making a corrective action to restore the well's capacity. In all three cases, had an intelligent field not been there, the time-cycle from detection to analysis, and to decision and action, would have been substantially longer, resulting in higher operational costs and an impact on reservoir performance.

This paper describes the continued & increasing impact of WITSML on the Drilling & Workover real time operations. The Wellsite Information Transfer Standard Markup Language (WITSML) is an Industry Standard that is used for transmitting GeoTechnical Data from the Wellsite to specialised databases. Saudi Aramco has been and remains to be the largest user of WITSML and is the Leader in the development of innovative techniques that improve Well Safety and Efficiency. Saudi Aramco Drilling & Exploration teams have commenced a project of Picking Shallow Casing Points Remotely from their corporate offices. From past experiences we found that sending wellsite geologists to rig sites to select these Casing Points has always required people which could be utilized for other frontline exploration jobs. Due to the remote locations Saudi Aramco are also eliminating the risks associated with these field trips. Saudi Aramco teams determined to utilize the real-­-time data transfer and pick those shallow Casing Points from the office. Across these shallow sections, loss circulation is more likely to happen which means rate of penetration (ROP) data is the only entity that we depend on in order to pick these shallow Casing Points. Based on this understanding, and based on the fact that we can easily get the ROP data of the pilot well and also having ROP data of the offset wells, we see the great benefit of starting such project. In this trial project, Drilling, GOC (Geosteering Operations Center) and exploration teams were able to pick the formation tops to set the shallow casing point remotely from a few fields. This critical requirement was achieved by using AramcoLink’s Real-­-Time software that collects and transmits surface parameters data. This Paper will address the important operational aspects that were implemented and demonstrate how this innovative technique has led to improved employee safety and operational cost savings.

BackgroundDisease surveillance is a critical function of public health, provides essential information about the disease burden and the clinical and epidemiologic parameters of disease, and is an important element of effective and timely case and contact tracing. The COVID-19 pandemic demonstrates the essential role of disease surveillance in preserving public health. In theory, the standard data formats and exchange methods provided by electronic health record (EHR) meaningful use should enable rapid health care data exchange in the setting of disruptive health care events, such as a pandemic. In reality, access to data remains challenging and, even if available, often lacks conformity to regulated standards.ObjectiveWe sought to use regulated interoperability standards already in production to generate awareness of regional bed capacity and enhance the capture of epidemiological risk factors and clinical variables among patients tested for SARS-CoV-2. We described the technical and operational components, governance model, and timelines required to implement the public health order that mandated electronic reporting of data from EHRs among hospitals in the Chicago jurisdiction. We also evaluated the data sources, infrastructure requirements, and the completeness of data supplied to the platform and the capacity to link these sources.MethodsFollowing a public health order mandating data submission by all acute care hospitals in Chicago, we developed the technical infrastructure to combine multiple data feeds from those EHR systems—a regional data hub to enhance public health surveillance. A cloud-based environment was created that received ELR, consolidated clinical data architecture, and bed capacity data feeds from sites. Data governance was planned from the project initiation to aid in consensus and principles for data use. We measured the completeness of each feed and the match rate between feeds.ResultsData from 88,906 persons from CCDA records among 14 facilities and 408,741 persons from ELR records among 88 facilities were submitted. Most (n=448,380, 90.1%) records could be matched between CCDA and ELR feeds. Data fields absent from ELR feeds included travel histories, clinical symptoms, and comorbidities. Less than 5% of CCDA data fields were empty. Merging CCDA with ELR data improved race, ethnicity, comorbidity, and hospitalization information data availability.ConclusionsWe described the development of a citywide public health data hub for the surveillance of SARS-CoV-2 infection. We were able to assess the completeness of existing ELR feeds, augment those feeds with CCDA documents, establish secure transfer methods for data exchange, develop a cloud-based architecture to enable secure data storage and analytics, and produce dashboards for monitoring of capacity and the disease burden. We consider this public health and clinical data registry as an informative example of the power of common standards across EHRs and a potential template for future use of standards to improve public health surveillance.

The primary focus of technical communication (TC) in the past decade has been the system-assisted generation and utilization of standardized, structured, and classified content for dynamic output solutions. Nowadays, machine learning (ML) approaches offer a new opportunity to integrate unstructured data into existing knowledge bases without the need to manually organize information into topic-based content enriched with semantic metadata. To make the field of artificial intelligence (AI) more accessible for technical writers and content managers, cloud-based machine learning as a service (MLaaS) solutions provide a starting point for domain-specific ML modelling while unloading the modelling process from extensive coding, data processing and storage demands. Therefore, information architects can focus on information extraction tasks and on prospects to include pre-existing knowledge from other systems into the ML modelling process. In this paper, the capability and performance of a cloud-based ML service, IBM Watson, are analysed to assess their value for semantic context analysis. The ML model is based on a supervised learning method and features deep learning (DL) and natural language processing (NLP) techniques. The subject of the analysis is a corpus of scientific publications on the 2019 Coronavirus disease. The analysis focuses on information extractions regarding preventive measures and effects of the pandemic on healthcare workers.

Teleophthalmology services are considered capable of supporting screening, early diagnosis, and monitoring of leading causes of blindness on a global scale. Therefore, standards and best practices are needed to seamlessly exchange medical ocular images and related data among relevant stakeholders with maximum interoperability. This paper provides an overview of current standards in the field of store-and-forward teleophthalmology data exchange and further developments in this area. A literature review was conducted for healthcare standards with a focus on data exchange in ophthalmology. IHE, HL7 FHIR, DICOM, and clinical terminologies are considered the most important standards, providing distinct concepts, solutions, and guidelines for ophthalmology. Available standards provide the necessary base for teleophthalmology on technical and semantic interoperability, but practical use is limited due to missing process interoperability resulting in proprietary interfaces of vendors and rejection by ophthalmologists. Further investigations should analyze processual needs on ophthalmology data exchange standards.

The idea of Building Information Modelling (BIM) was created already in mid 1970s, first under the name of Building Product Modelling (Eastman 1975) and started to become a wider industry issue since 1995 when the International Alliance for Interoperability (IAI) started to develop a standard called Industry Foundation Classes (IFC); a schema and format for the data exchange between different software applications used in Architectural, Engineering, Construction, Owning and Operations (AECOO) activities (IAI 1995). In the first 10 years the main focus in the development of BIM was strongly in the technology. This was necessary since a standardised data exchange is a mandatory precondition for an efficient BIM-based workflow between different participants – Integrated BIM (iBIM) – and the development and implementation of such a standard proved to be significantly more complex than believed in the beginning of IAI (Kiviniemi 2006; Kiviniemi et al 2008). However, despite the still existing technical problems and limitations in the data exchange (Kiviniemi 2008), the main obstacle for the deployment of iBIM in the last few years has not been the technology, but the old work processes, old business models and conservative attitudes in the industry. The traditional paper-based processes do not utilise the full potential of iBIM and AECOO industry’s business models do not support necessary development of the processes. Finally in the last few years the development focus has started to shift towards necessary changes in processes and business models. This Chapter is based on several research and pilot projects and the development and use of iBIM in Finland since 2001 and highlights some of the current problems.

To develop a health/medical data interchange model for efficient electronic exchange of data among health-checkup facilities. A Health-checkup Data Markup Language (HDML) was developed on the basis of the Standard Generalized Markup Language (SGML), and a feasibility study carried out, involving data exchange between two health checkup facilities. The structure of HDML is described. The transfer of numerical lab data, summary findings and health status assessment was successful. HDML is an improvement to laboratory data exchange. Further work has to address the exchange of qualitative and textual data.

Chapter 7 - Deck of Cards: Designing Small Viewpoint Content

This article describes the development and implementation of a methodology for evaluating the effectiveness of model data exchanges between Systems Engineering tools. The context of the work is the SEDRES (System Engineering Data Representation and Exchange Standardization) project. This is a European initiative toward the development of a standardized data exchange capability. The paper addresses the issues of the use of this capability, and its potential influence on the System Engineering process. It describes the development of the evaluation framework and methods that were tested and refined for the SEDRES model data exchange context. Results that demonstrate the potential benefits of adopting the standards-based approach are described and a number of issues relevant to its applicability in the support of integrated product teams are discussed. © 2001 John Wiley & Sons, Inc. Syst Eng 4: 13–23, 2001

Biodiversity data exchange depends on established standards, such as Darwin Core, Audiovisual Core, Taxon Concept Schema, etc. Standards provide terms with defined semantic meaning and structures for data exchange. Standards simplify interchange of biodiversity data among government agencies, researchers, and engineers. A notable challenge during such data transfers is the complexity involved in processing data for various uses. Most data exchanges are performed via Comma Separated Value (CSV), Extensible Markup Language (XML), or JavaScript Object Notation (JSON), and need to be parsed and imported into a database before being queryable. For example, to explore the data in a Darwin Core Archive zipped file, it is necessary to decompress it, parse XML-based metadata about the content of the file, parse and read Ecological Metadata Language (EML) to extract provenance metadata, and correctly open the text-delimited files that use a wide variety of character encodings, delimiters, enclosures, and escape characters. All of it requires non-trivial data management and programming skills from users. We propose a paradigm shift toward using queryable SQLite-based files for more straightforward data interchange. This approach would reduce friction in data processing by directly using Structured Query Language (SQL). SQLite is an open source, high performance, lightweight database already installed on most computers (SQLite 2024). It integrates a database into a single file, facilitating straightforward data exchange and compression. Its robust engine is able to manage terabytes of data. The SQLite developers are committed to maintaining backward compatibility of both binary and SQL text file versions until 2050 (SQLite 2024). Connectivity to SQLite databases is supported by all popular programming languages. Furthermore, the United States Library of Congress endorses SQLite alongside XML and JSON for data archives, attesting to its long-term reliability (United States Library of Congress 2024). We at the Species File Group are experimenting with using SQLite to create a universal data converter, in which an SQLite database serves as an intermediate data storage format. This provides several useful advantages: 1. Universal data converter By learning a standard SQL schema for biodiversity data, users are able to efficiently write importers and exporters to/from the intermediate schema for a variety of other biodiversity data-exchange formats. Any programming language can be used to develop the conversion scripts. Import and export are completely decoupled from each other, allowing mixing and matching of converters in a flexible way. For example, we aim to use SQLite as an intermediary archive to convert data between Darwin Core Archives, Catalogue of Life Data Package (ColDP), Global Names Verifier, and TaxonWorks (Fig. 1). 2. Semantic versioning of the archive Like any other data format, the intermediate data exchange SQLite schema will evolve over time. Using Git tags and branches, it is straightforward to follow a semantic versioning paradigm (Semantic Versioning 2024). For example, in "v1.2.5" the numbers respectively indicate major, minor, and patch versions. Major versions include substantial changes that might not be backwards compatible, while minor versions are backwards compatible and patch versions indicate tiny bug fixes. 3. Data querying With SQLite-based archives being relational databases, they can immediately be queried without any additional parsing and importing. This allows the archives to be explored with ease by anyone familiar with SQL. The Carpentries also provide online learning resources and courses for researchers to learn SQLite (Martinez and Poisot 2017). 4. Biodiversity application development As SQLite-based archives are relational databases, and the intermediate data exchange schema is standardized and stable, a database file can be used directly by a variety of applications. For example, it would be possible to develop a web application frontend that utilizes the SQLite archive as a backend. It could be possible to develop an ecosystem of biodiversity informatics applications based on SQLite archives. We believe that adopting SQLite for biodiversity data exchange can reduce operational friction, enhance data accessibility, and promise a streamlined and universally compatible data management framework.

The last decade has been marked by the emergence of intelligent field technologies. Many E&P companies have moved from the piloting and trial-testing mode towards strategic implementation demonstrating that these technologies have shown their capabilities. While the current deployment of these technologies represents only a small fraction of the overall installation, the trend is indicative of the shifting of attitudes and preferences within companies. Among those companies, Saudi Aramco has deployed fit-for-purpose technologies such as intelligent wells equipped with multiple downhole valves, as part of its best-in-class practices (Figure 1). Continuous assessment of these technologies is important to provide reassurance on their values. The resulting performance of this technology is a function of multiple factors spanning from reservoir parameters, completion schemes to surface infrastructure. Understanding and assessing the impact of these factors is complex due to the wide variation of variables. This article summarizes the lessons learned from more than one hundred deployments in wells equipped with multiple downhole valves. These lessons illustrate the advantages of these applications and should provide insight for improved performance. As there is no "one size fits all," then proper design should be emphasized for maximum effectiveness.

The Northern Area Oil Operations of Saudi Aramco has embarked on the installation of Intelligent Field equipment and innovative technologies on a mass scale for the past decade. It was noticed that the initial performance and utilization of such technologies were lower than expected. Therefore; a plan comprised of the following was devised to tackle these deficiencies:Launching a major organizational restructure and assigning a dedicated team of specialists to look after the Intelligent-Field equipment and the real-time data transmission to the databases.Establishing a tailored maintenance service contract with the providers of such technologies.Developing an in-house training program for specialization in the operation, maintenance and utilization of Intelligent-Field equipment.Launching of an innovative technology deployment and evaluation program where each technology is assigned to a technical champion who assumes full responsibility of the technology deployment process.Initiating and maintaining state-of-the-art knowledge management system to track the progress, document the procedures and processes, and capture the lessons learned from the application of each technology. The implementation of these steps resulted in enhanced Intelligent-Field equipment performance efficiency. In addition, the utilization of real-time data — in advanced production and reservoir engineering analysis; such as automated well rate validation and allocation, production optimization and sweep monitoring — has improved due to the high availability and quality of the transmitted data. This paper will provide details of the holistic approach developed by Saudi Aramco for the installation and maintenance of Intelligent-Field equipment and all the implemented changes in the work processes to maximize their performance and tangible benefits. The success and value-added by implementing this generic approach will be illustrated through the high efficiency of Saudi Aramco's Intelligent-Field equipment, which has been maintained at 99%.

Abstract The increasing level of electronic data exchange in the health system and the correlated transmission of laboratory data between different institutions require a harmonization of the data languages. This aim is served by LOINC (Logical Observation Identifier Names and Codes), a system which has evolved for years. The defining axes and the code provide a valid method for the unambiguous identification of laboratory results and more. The existing and defined integration into data exchange standards such as HL7 and LDT highlights LOINC as a bridge between laboratories, medical practices, hospitals, or other facilities in the health system, especially regarding laboratory results. Pertaining to the recording of the performance of a laboratory, the methods are the most relevant data. In this regard, the existing options of the data transmission standards have to be used. In its present form, LOINC by itself is therefore not appropriate for economical analyses and controlling, i.e. cost and output accounting. In the light of the broad international implementation of LOINC and its established adoption in German facilities, LOINC turns out to be the most mature standard among the competing systems for clinical use.